04_Energy_Meters_Recognition_CNN.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KexTT6csMJHG"
      },
      "source": [
        "Augmentor is being used to supplement the training data with variations of the images."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![vanti.jpg](data:image/jpeg;base64,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UUUAFFFFABRRRQAUUUUAFFeefFqS4vr7wZoC3d9Y6frWrNb3s2n3T2sxjS2mmEazRssibnjXlGBwCM4JrnfDmueKdY/Z91+TTLm6vvElkdUsLC6Uq9xN5FxLFEwL/ACtJtReW6sMnqaqwHstFfMFjfeI/EGl+L/C3gRPFV1aiHSJ4P+Eo1K+0++hMs7rdj7Rc/wClRIY4c5AJBLGMEECtXSLqXxNfaH4GnbxB4citbnU11VYPE17dSvcQRwvGseoO63DxFbgSAZX7u1lwCKOUD6KrA/5n3/uGf+1ax/g34kvvE3wl8M6zqkrXd/cWCSTzqgzMwGC4UDGWxuwPXirP9sR/8J0G+z3eP7Nxj7M+f9b6YqQOuorP/tqP/n2vP/AZ/wDCj+2o/wDn2vP/AAGf/CgDm9Y/5Kz4b/7Bt7/6HDXaV59q2qo3xU8OyeRdALp14Npt33ctD0GK7H+2o/8An2vP/AZ/8KANCs++/wCQppv+9J/6BR/bUf8Az7Xn/gM/+FZ99rMf9qab/o1596T/AJdn/ufSgDoKKz/7aj/59rz/AMBn/wAKP7aj/wCfa8/8Bn/woATVP+PrS/8Ar6/9pSVo1z+qazH9p0z/AEa8/wCPr/n2f/nnJ7Vof21H/wA+15/4DP8A4UAaFZ+tf6q1/wCvqL/0IUf21H/z7Xn/AIDP/hWfrGsxmO1/0a8/4+Yv+XZ/7w9qAOgorP8A7aj/AOfa8/8AAZ/8KP7aj/59rz/wGf8AwoANe/5Bx/66xf8Aoxa0K5/XNZjOnn/Rrz/Wxf8ALs//AD0X2rQ/tqP/AJ9rz/wGf/CgDQrP8Qf8gS9/65N/Kj+2o/8An2vP/AZ/8Kz9f1mM6Lej7Nef6pv+XZ/T6UAdBRWf/bUf/Ptef+Az/wCFH9tR/wDPtef+Az/4UAS6v/yCb3/rg/8A6Camtf8Aj1h/3F/lWTq2sx/2Ve/6Nef6l/8Al2f+6faprXWo/ssP+jXn3B/y7P6fSgDVpkv+rf6GqX9tR/8APtef+Az/AOFMl1qPy3/0a86H/l2f/CgCXQv+QHp//XvH/wCgir1YOh6zGNF08fZrz/j3j/5dn/uj2q9/bUf/AD7Xn/gM/wDhQBoVn6B/yB7b/dP8zR/bUf8Az7Xn/gM/+FZ+g6zGNIth9mvPun/l2f1PtQB0FFZ/9tR/8+15/wCAz/4Uf21H/wA+15/4DP8A4UAGh/8AHi//AF83H/o560K5/Q9ZjFi/+jXn/Hzcf8uz/wDPZ/atD+2o/wDn2vP/AAGf/CgDQrP0f/l9/wCvp/6Uf21H/wA+15/4DP8A4VHoEwuIbuQK6BrmT5ZFKt26g0AalFFFABRRRQAUUV5rr7a5a/G7wlv12T+xLu1vkTR4IvLj3JHGfMlbJMrZJ2/dVR/CT81MD0qivMPjlZ2Umjxyy6r4gi1qSOS30XS9B1SezkubwjKHbCymTbgFvNLRIoZmAAJrgPE2r+JbK41vW9Q1q+h13wxeaJYRW1pdyR2Nx5wt/tJa3DBJfNM0ihpFLLtGzaRTUbgfR1YHhH/mNf8AYTm/9lrYurpbOMO0csgJxiKMufyFcx4T1iNf7Z/0e7OdSmPFu5/u+1SB11FZ/wDbUf8Az7Xn/gM/+FH9tR/8+15/4DP/AIUAc18Kf+QPrP8A2G7/AP8AR7V2tee/C/VUh0nWAYLps6zfN8tu56zt6DrXZf21H/z7Xn/gM/8AhQBoVn6D/wAg4f8AXWX/ANGNR/bUf/Ptef8AgM/+FZ+h6zGNPH+jXn+tl/5dn/56N7UAdBRWf/bUf/Ptef8AgM/+FH9tR/8APtef+Az/AOFABoH/ACB7b/dP8zWhXP6DrMY0i2H2a8+6f+XZ/U+1aH9tR/8APtef+Az/AOFAGhWd4d50DTf+vaP/ANBFRXniCO3tZZPs94Cq8Ztn69u1ZvhDXo38M6aDb3ZZIEQ4tnPRQPSp5teUvlfLzHUVl61+8aCL/ec/8BU1J/bUf/Ptef8AgM/+FZt1rEct9Ofs93iO3I/49n6n8PSlPaxVPdvsTWv+jR6RcdFeFIn/ABUYqt5zf8JV5v8Ayz3+R+O3/GiPUY7jw7boLa83CBCv+jP1Cj2rOXVgNOF21vd7/tgmJ+zv0zjHSiOkmu51UWnG79PvN3Uv341J/wCGG2ZB9SpNT2H7m6C9pYVcfUDFZT6sn9h38jW13uljkc/6M/ofamyeKLOP7EypctJGvzIsDk4K89q6ftOJ5UYtx5l6/wBfI6msPxpdC28N6gC20vA4/Dacms6TxlNd3Hk21pdWyt8vmTWz5B+mOvSuL+IXxO8M+CbS+h1TUm/tKW3ZYraeNmmmcjACxgZxz9Kin7z06Hf9Vq1GqUE3KWyWr8/wPULLUdOjjVIbmJR/tNtJ/OuT1T42+DdOv59Pm1VJJ0PlkRozRs3dd4BXjvzXlvjn4oeJPiJpdvD4Q8HaveabcLia4jhWJ5f7yiVsrGO2eT6jtUOi+DfiJqunrpEdpp/w30TaBN/Z1s9/fzZ65dhtBOPvDn8sV6Lo0qC/2htyfRPVeuj18vvsRh8LUxv7ylOFKlG/vTd+a2nuxi+dr+9az6X3O+8TfEfwhpXh+W71nxHYLAqqIY1uFlBJOcBVyWI+lYEn7RXgXUGJ8PaX4m8QSdFbQtKlJb6bttSR/BHwj4X8NSLHZ6zq18pDPe6lJO8rnIz0wq/gBW03w/gj/wCPC58T6SOwtbybA/BgaOTC1NVNr1S/R/oFPGrB3pzoOtbZxqOCa/wOm3/5OYC+OPiH4qYR6J8OdSig7XHim+S0RP8AeiXLt+Bp2tfDH4h+LNEltvFXjKw0vSrgxx3Gj+GbAIkis6gjz5MyDr261tfY/GHhxhJp/iC8vo/+eGtWLyoT7yKAw/CrN58S9UtdNK6/4Wv7VBJGft1gpngbDqcngMucYAIPOKiWFdr0pKXzs/udvwubLif2bShhlRa68vP87tzS9bRPRNF0Wx8N6RaaZpttHZafaRiKGCMYVFHb/wCv3pzapCm4sGx/DgZ3fSuDl+N3hi6Jha/exB4P2qB0b8iP61lat8YvDtqnl6V5+rTNzvt497PjsD0A9T+lc6wuIk1FQevdWPMeNw8uerOqnbfW71/G7++52HiDUrq8EEMMbJi5h+UdR+8XkntTNeu7bTvDt/qms38kdpZKZJVC5K44HGeSTwPrXiPiD4r+JtXkjtNMVNMUSrJshUEAhgQZJ2BU4ODhAfesLxzrHi3xNZLY32stqcMyD7StjEkUIwQRvJVC/IHQY4rGpCMoSp0KkXUW29r+crcr87M68LOpCrTq16coUpPV+7fl7qDfOtNrxV/uPXfhT8eNB8ZaomgRW91ZXr72gM6KFlABJHyk4bAJ/DrXp2sf8uX/AF9J/Wvlf4K+FE0XxlolzOTc3cM8jh7aM5KmNht25OcAn8q+nNQ1NLiawjEFyhNynzSQMq9+5FcOFp4qlDlxck5eX9d/wPTzSrgK2Ic8uTUPPr5q+tmraPVO/SxuUUUV1nkBRRRQAUUUUAFFFFABRRRQAUUUUAY3irwhpfjTTFsNWhlkhSRZopLa5ltp4ZF6PHNEyyRtyRuRgcEjoTVaL4f6BDocWjJYY0iOzksPsHnyeQ0T43703Ydjj77Aty3PzHPRUUAcXa/B3wrZ+H7vR0tL2SC6mW4lup9Uu5b4yoAEcXbymdWUABSJAVHAwKbc/BnwldeHbTRWsLpLW1me5juIdSuo7zzXz5jtdLIJmZwxDFnO4HDZFdtRTuwK2m6ba6Pp9tYWNvHaWVrGsMFvCoVI0UYVVA6AAAVk/wDM+/8AcM/9q1v1gf8AM+/9wz/2rSA36KKKAOL1j/krPhv/ALBt7/6HDXaVxesf8lZ8N/8AYNvf/Q4a7SgArPvv+Qppv+9J/wCgVoVn33/IU03/AHpP/QKANCiiigDO1T/j60v/AK+v/aUlaNZ2qf8AH1pf/X1/7SkrRoAKz9a/1Vr/ANfUX/oQrQrM12VIobUu6oPtUXLHH8QoA06Ko3Or28Cja6ysegUjH50yK/n2mRkjmi7+Q24p9fWq5WQ5JC69/wAg4/8AXWL/ANGLWhXOeJ/EFpb6aAC0rtJE22NSSAJFyT6V0EMyXESSRsHRhkMO9RfW3U0s7KXRj6z/ABB/yBL3/rk38q0KxfGGoR6f4dvZJMktGVRF6sT2FDdldgk5OyNqioLO9h1C3WaB96N+Y9j71K7rGpZmCqOpJwKL6XCzTsytq/8AyCb3/rg//oJqa1/49Yf9xf5Vk6xqL3Wm3qWce8eS4MzcKPlPT1qex1aJNOVrhhFJEoDqeucdvWpU03ZFyg4q7NSmS/6t/oayxq1wtxFLLD5NlIdgLfeyehPoKt6hqUFjE3mv85HEa8sfwrQwU1uN0L/kB6f/ANe8f/oIq9WPoOpW6+F9OuZJBHF9mjyW7HaOPrVW58QXK7bhfstpanOwXsojab1Iz0/+vWcpKO5vCnKprE6Ks/QP+QPbf7p/maxB8TNCt22X+oWti/q9wjJ/30DV/wAH6zYaxodvJYXtveoFOWt5VcDk9cGlGpCTsmVOjVpq8otI3KKZ50e8JvXef4cjNRXV4lqoz8zt91B1Nar3tEYS91XZX0P/AI8X/wCvm4/9HPWhWF4Zv/MtXjl2hmuLgqynIb98+R9RW7Q047ijJSV0FZ+j/wDL7/19P/StCs/R/wDl9/6+n/pSKNCiiigAooooAKz7zQbG+1fT9Ung332nrKttLvYeWJAA/AODkKOoOMcVoUUAcP4g+DPhrxNf6ffXra4l/YW72kF3ZeItRtJhE7B2VnhnVnBZVPzE/dHoKs3Xwn8MX2uafrFzYzz39ksSo8l9cMkpi5ieeMybJ3QnKvKHZTyCDzXX0U7sArA8I/8AMa/7Cc3/ALLW/WB4R/5jX/YTm/8AZaQG/RRRQBxXwp/5A+s/9hu//wDR7V2tcV8Kf+QPrP8A2G7/AP8AR7V2tABWfoP/ACDh/wBdZf8A0Y1aFZ+g/wDIOH/XWX/0Y1AGhRRRQBn6B/yB7b/dP8zWhWfoH/IHtv8AdP8AM1oUAZ+qfvpLa1H/AC0fc3+6OaoeE/8AR7K3h7SW0Uy/ioB/WrayBr+7uTytumxfr1NZ2hTg6LoVz3WJIX/FR/n8ay5Xb2nmdHMrqj5f8E6WuI8WeLIvDmja9fu21IUZpJeygDaoH+0zEAD1NdHqd9KZTaWp2y7N8kvaNf8AGvJvEVuPH/i7TvCMPzaRpKjVdaftJJgmGFvqOSPQ+q1zYus6MFyazekV5v8ARbvyR6mW4WFSblW+BK8v8K1+96JebQ6x+NyWOkW5udM8R2sSwLh7vSPkI2jkMrHIPrVzw38YdA15f7Kh1COOZ8OsN1G8Tt0OF3AAnI6Zrf8AG3xI8O+BfBkDavqsFtNJbxrHbBt80mQPuxjLHv2xXlniibxV8e7QWukeErfQNEjTEeq+IFP2tlH8cUK8oemMnnNONHGQtPnUrd42/FP9GevRpYTGUeadL2UJO3Nzqy80nFOVr7J3PSviVr1t4U8Az6hd3jSS3EXk20LOEjDMvUjjhRkkn0rzix/ac0z/AIRqaG10JdT1KGQW+/TbmNbZ2H8Rkc/KOnrXF+Hfhzqp0+81XU4L34k+H7F5LM/arhlfCKPniiJO6NT/AAg8/hx6pY+EfEfxEtrXS7+2h8EeEFVZF0fT0VbmVR0MhAwmePlA+o6GvUrY2hhOXDSpOdTd2urX2974bLrfrdbo8GnlyxDqYqGIXsoytG9lCUY73gv3l5P4bclo2k21Kxxd54x1rxXJc2nibx3pvhCzUG4bTPDStc3kuRwrXOCqt7IfTOKi8K/DHRdc0+XU7eCG1sgS4iM3n3t1gFt9xKSSBx9wYHt6+p6d8GfC2m6k7x2L3XzFI7W5k82Adi4Vs5YgAc5HFU/Gfwf8IiKeePRI9OvI4HlR7GV4sYHHCnH6U51amDj/ALGlKT6u8bLol8WveX3W3Ijjf7UfJjW6NKKXuU7OMpdXN+42u0Zc3m3summ8e+HPh7pk1lqOo2li1q5jitd437eowg5x9BWd4N+MGg+JfFC2EF1OJ7mPMT3UJhSfk/6rdyce4FLoHwr8N+Hr2JLXRLeG5lBcTXA81zjr8zZYGmfFGz8Natof2TU7i2t7u3/eWklmd1xFIOhXHI/Hg14044x/vJNXX2Vd/wDk2n5Hb7TLub2cYytL7Tsreair6X/vHb+IrrdpdzHEPMbAyey8ir32xo/9dCyf7S/MK+f9N+Luq2+kw6Fqd9Yidoy39oTIQXQHjIHyh+OQTWrefE+N2ZH8bTXDYz5Wnafz+BA/rXs0MNXxEVKnBv5bep8ticVhsJUdOrUV+mu/oe2te2+3mRSD26/pXG+LNY03Trdma7iggiljcxySD7wcdF69K8k1XxFZ/YZdQ1C18W3dvkbn1CX7Nb89BksOtZZ03VfG2nQt4a8LRaNZSBWOoXcu+RwWAUxlxwCSOQD7Grlh5wl7NuN7Xs5RT+5tCWJpSiqsVK17cyhJpP1in/wTqfGHxU/twDSbSGRo7lv3UMUHm3d2T/cj/hU/3mx7dMVSh8B23h3Rz4i8f6jD4Z0W2AZdLtpA0z8cLJIPmZj/AHIx+VcrcaxqnwsuI9G0630vUPGF62wafp0ct1qEpz96Z2IVF9+B3x1rvLf4B6t4xmsda8b62sWqxDdHZQqssVt3GM4Td7heo6nrWSwLq2eLxCUf5Y3/ABaWv4LyZ7scVHCwhWp4OfJL7cuTmf8AgjKS5V05uWTXTsc7Hot98YJoLwWB8DeA47iFbeGJBHqmpqXA3M4+aJCD0zk+/Ueh+MPFHhz4Y+CrzTtIs9PgdozHFp7yKZJmPGWXJZuuSxyfenar8LtGht421DxJrGphp4leO51D5CC4BwqgdjXD/GSHwz8Mfg74t8QaZpmmQamLf7Np8d0oldC7BNwLkkuAxbGf4a6assHhoOpJyklq7Lot+v3aHDPFZlm9SOFoU4UYN2iubq9E5ae876uTforaFf4D+OP+K0tdNm0uzDXiuq3MQfzI2CluNzHg7cV9Eax/y5f9fSf1r85v2M/H/iWb4/6BZXkkuo2d/HcxSeeu7ygIXfepx8pyuPoxFfozrH/Ll/19J/WuSeYYfMn7bDUZUktGpKz739GmiHk2MyP/AGbG4iNeT1UoO6ttbZapp6GhRRRWYgooooAKKKKACiiigAooooAKKKKACiiigAooooAbIXEbGNVaTB2qxwCewJwcflXJ+dqf/CdA/Y7Tf/ZvT7U2Meb6+XXXVgf8z7/3DP8A2rQBf87Vf+fKz/8AAt//AI1R52q/8+Vn/wCBb/8AxqtCigDz7VptR/4Wp4dJtbUS/wBnXm1RctgjdDnJ8vjt2rsfO1X/AJ8rP/wLf/41XN6x/wAlZ8N/9g29/wDQ4a7SgDP87Vf+fKz/APAt/wD41WHrGp6wmu6XbwWFk8pEjkG7fGNuP+ef1rrK5Jrp5vEkE8SCV3eSOPJwAqpyfoaqKuZVJctkXftniX/oF6f/AOBr/wDxumSXniPHzwaVaj/au3J/9F1pyW8zRtJdXPlxqMssXAA+tYBe1nt3v7uePTtLjBJLSAMwHUsx+6KHyx8/QFGUlduy8zJ1jVNeGoaaE+zy7bnPmeeVT/Vvxjy8mtO31DxDqAYvNp9mAeUExz+BMdeReMf2loJNStLPwdpkd/bW8xY31y7RQyNtZdseFLP1PzAY/OrWm/Ej4geIFVF8DRo7crOJ5VUjucuqjFdiwNWUOeUWl3bSX42PHjnGGVV0qMuaXlFy/wAz1L+1NdsJmVBa3UZH/LW4YkH1B8sVz/iDxEYY459UutOtESaMlri9K55ydo8v+VeD6z+0hdaxey6QNSjtZYHaOa18N2Emo3TkcZDLmMjPpItc7qXiyXUHhuYfhzrGsXLMCb7xZqCQN8vJBtYyZMEdixrhdXA0XZ1eeS6RTn+K91fez3qeHzXGR544f2cH9qo1TT+UmpP0SVz1fX/2mtM0m6ks9O/s29dTxcXT7Yh74Kq7D8q47Vvj9dRxSzv49gtIpj80en6VIsQx/CrPFz+daPg/wH4w8T6OdUurjw/4L06T7lv4e0yKCZznjDDMo+rSD/dNbS/s96Cs0V7Bdyx65CxmgvNUgjvUcnqHUrx7MvIyTg8VyyzSpJXw2E/8Ctd/ff8A9tudMOH1CpbG5kl5U1JpfNOO3XSdjze4+NXjTUrFX8NW2ueILYMP38diEhbBBGGWNieccECtXSf2mvEfh9o7fWbe405e6X1nJF83opT+q11vjD9oJfDGdH8YaMG1eBY2hj0N0kW5AYfM2TuiGBwCPwrzHxN+0Z478cZ0LRrRNDtZiQLLToPtd/KuejHGBx7A89a9ihjJYiyVPnl/LyJW+6KaXne3mcv+rWMw0XXq4qNCk/8Al5Kbal2tHnbk/SLfex38f7XMDkh4ZFI9L1ufw8sGsLxZ+1BqGrWDxaTb2MNwykLcanfOwQHr8iqD/wCPVyfhf9k/xp4j8i61eWDw9Zzvl57om5uiCeW2KQnTsTmvUP8AhQnwg+HGnzzazqaeINWjQkNq16kcKv8A7MQKrj2O6vR/2Tl/2iCi+0W2/vu4/c/keTX9th5Xw+MdVd5U1Tj913J+jgvU5PR/2ttd0ss1/ZaFOW5d7HU5IiT67TE+fzq3q37aFzNCqWWjwzXBHyefcNIpPfaG8oE/8C/Cu81jxN8AjYm2fT/CcAAwGs7aBiPxRcmvNryH9ny7Zo7fTX1G5bqln9ub6EDIAP0Pp078FSnl9aPs1GcU+qlG6817v6kwzbFYWoq050pNdGpWfl8f+Rz2rftKalrEMq+IbTxLeIykJZ293b6fb9OAyIWLj8RWVZftMa9ot4kujNdWsCDAt9WvkvVT0CgoCAPQNVq48L3tncPJ4I0DxNqemoMyWmsacUSJf70c4OfoGXA967TS/hp8XvESRTfufDcUu0Qi/wBRkuJz/u+X8vHuBipo8NYKFqn1lvzbs/w95/kdb40xeKqOnPCL2f8ALDb1vJSgvXkv5pnDt+1H4+uNWN0fEUd+GX5tMWx/0fbnOMBd3Xvuz71vWH7XnjN7eY2+kaVJN91nS3l3DPGNzSfLXpsP7J9/eLs8RfEbVb+Q4aQ2tssaox7fMzZ+uB9Kfqf7HPhXTdP+1S+KvE93gjKfbYkDD0AEfWtvqeXRi41MTJ+kbf5aeupdTHV4VYVsuwCi+qqVHVT9Vyqz/wAEkfPfiL4/eO9UtxaPe3OlW8Ix5NhJHHjAxkSbS+eOzV57/aun6zqjXGraZNrF64/eXV3NLeTHnqd7YNfZfh/9m34bvpNsz6RM11Lbq7XepXkzPK5UcDDBBz7UWf7OetaJdCXwtd+H9V0iZcrD4jtpPNtWz0Uw4WTvywH071yPLsgrNuqpzfm/85Ox6dbi3iXDQVOhhqME/wCSMk//AErX8T5YhsfCvlb00SQn+4dBjY/+jDT9Pj8CWsiz3+i63Z3C9Gs9PjhA/U19VXHwQ8Y6hPLZjxB4cshIp8+Gw8OQyRgeh8zvWX4X/Y18Pahaw6h4h1C4dcZMVukNsh577V4/OueeVcP/APQM3/2+/wDKw8NxJxPVV5Yxw/7h03/7fzfh9x4rY/FDQ7GMQ6NrHibTbhf9XNc6ikMa/UKoP61cf9o7xLpbMZPFc+qDGFheOGRR9ZPLLH8Sa+ipvgT8KtNtWs7Dwrb3L4w087PMT+LsevquMdq4i4+A58BvJqvgS00jX7MtvuPDHiG3jkmUjtb3DLu/4Cxx65NduHw2V0o3wUeSf96Taf8A7b/4FFru0YVcZj6rks3csRT/ALvJTa9bQlK3+GUX6nknhn9pjxjZX3k25a8SSQv5Uab1yWzn5lBByexFdJrnxU8T+K/EC3V7qGoWMqbVW1hdoo4CFzkBWwpPXd1OfTGO9s/GvwouNsHjbwSvhe5Ekiut1oe0bjI3y+bGuSB07dK+dPjxrHgnw38RJv8AhB9Skk0iaKN2+zoVjgYqAyIxOW9TnGCxHavawtStSnz4mkuVdWor/wAm0ifE5spSioZOmpS05Y1JTf8A4DJc1/NP5dT7x+D/AIg1zXvhzo95crBfTMJYzcT3LLI4SV0Bb92cnCjnJJ6nk112gNK0N2ZkSOX7TJlY3LKOnQkDP5Vy3wHj0yP4P+FBo962o6e1krpcsu0uxJL5XJwQxYYycY6mut0f/l9/6+n/AKV87WadWTStq9O3kfVYXn9hT9q7ysr+ttTQooorE6QooooAKKKKACiiigCC6e4SMG2iimkzysshjGPXIVvbtXMeE5tT/wCJzts7Q/8AEymzm6Yc/Ln/AJZ111YHhH/mNf8AYTm/9loAv+dqv/PlZ/8AgW//AMao87Vf+fKz/wDAt/8A41WhRQB578L5tRXSdY8u1tXH9s32d1yy8+e2R/qzx712Xnar/wA+Vn/4Fv8A/Gq5r4U/8gfWf+w3f/8Ao9q7WgDP87Vf+fKz/wDAt/8A41Wfoc2qf2eMWdn/AK2X/l7f/no3/TKugrP0H/kHD/rrL/6MagA87Vf+fKz/APAt/wD41R52q/8APlZ/+Bb/APxqtCigDn9Bm1T+yLbFnZkbT/y9v6n/AKZVauNQ1G2Ul7SyB2lgv2x8nHX/AJZVNoH/ACB7b/dP8zWLqEbaxeM6tgCTyIT245ZqTdkb0aaqS97YZcXd/p+h+ZcwWcUchMssjXTDav3iT+76ACuY8IeOLTX/AA+iabcabcj7MjNEt4wkjdVGQVMeQc+2Pes7x1eXfxA8T6d4BhP2dVHn6zNGcbLcEHYp9X4/PPTNXdT+Gfh7xRpem7dPSzubOxiY31kfJmTCDaNy9cDPXNcftK0pOlRScVo76Xfk/LTpu7aWPT+q4enFVsTJqbd1azSiurWl+bW1nsk9bo1PEnii88H+B9T128tbR3kXOFumLOzHaiAeXzyfX1ryDQda8Qy2OpeDvCawT+KdQdrnxD4iMjNFp7ODmD7uDKq/LtBO07u+cY39neJfjJ4lTwhoviq51LwZpV3um8QNCqssmOY4nH+tcAth+gznOCufe9D0fw38K/CUltp8UOmaTaRMke48scfNI7dWZj36np6UYWnUr1/b1YWcVZR3d3u9L+i8r9z3sdLDZJg/ZTkp1aj5rbKMV8PPez035e9nLRJS5bwb8G9N8HLpN6mmQaprly0ckuq6lfPPO3ygnBMWEH+6PzrpviJrmsLb2vhvToLW31bWy0EbxXDMYYgP3kpGxcALnnOfTOKwbfxN4l8cLp8fh20/sm1gh2/2nexZZgQFLRR9+nBPH0rzDS/FHiXwp8SvFHiLR45vE+i6Js03VftGJLqduXnkhPUGMqB5ecbccZ+YfSQprDy9pXl7y2jvr0v0S8t+lkfE/wC18SXhh3dW1nJ2Vm7csPN3tHaK3voe2aba3vhPQ73S7WxtVsLaNoQv2piVypwxPl8k9/WtzSm1e6hMn2S1USBct9qYfKBwo/d1yF78UvDureGpdWstVtpoNQtPNi8tjuc42hdnUMD1BHHNY8nxaXVrQRNrtt4a0+IeW7Y8y7bGPuxrkrn1NefDDVa0uaKbfzevyJxdengn7KsuS2jvZbdNbHba14ui8K6gxuY7NrjYAlpBdO8jE8cARe3fFcv4l8VeILrSdV1G4srTQrSGHYft0uZX3A4VRjg4z1FR6DqnzbPBXhy81G+cfNrGsxmKFc5+bcfmb6DFU2+G8utTalrHibUJNdurfzGiT7ltHJg4KIOuB3PtXXUo+xX76Vn23l93T529DzcPiFiOZ0I8yXXaN/Xr8rrzRz91eax8Rtr6RJd3UEEm2TVNQuWW3AOAdkQRSfryK7Ox+F+jaPALfVrODWr0cu817KBn2RIwoH1yfeu51TT7M6VuZBDcRfuU8rgnHRcemP5VR0nXJljkghSISsS7XMrHGPpjk/SuaWM9mkqa5F3W79Xv91kdKwP1hfvHzvrf4Uuyjt33uzi9Z8KaFb+Hr+2fwjpojblZllZJFXIxh/LDD8DWPqfwusoVEeg3WqaaDy/2HWpgB6DDRHNdD8SvGmh+D9Me71rVIkmkXCteY+bkfcjHJA+led+J/wBrDwj4XsJE0WO78UXbAPssoCkG7sJJn2qq/TOK8bFVqLSni9X9m+rfp1+4+kyuji4zlTy//t6ySir9+ifm7WNS58H2vhIp4l8W382pWlnhoJdfvGa3Vuc5UR4Zj2yCePYYwfEvxv1/xxod3caHb23hLw/cTxRnxJqshXeNw2C2jKhjyM7iMYBPGCa+edV+M3jHxz4oOv6npdnq8nmBLK3u4nuLS0b+FIYw6oT2yQ2cZ5611Xib4e/G/wAeTaZrWuaXJ5SbYrOOR7W3iiLEAAQHc2c45IzwPQV0UsPOmnFU7X3blBP5qU1Lys1dbWPTxOKwdHlnTrRq1Y2teNR01f8AlUYOMn1u2oy6r7R9M/DLw3p/w/szc6Allqd3qUQa71u5v2nlnfJJdpPL5GcnA4+pqj4n/aC8FeD2uY9RvtN1K/jb5/JvZJWzgcELGcfQ4ryDw3+yT4gTSZ01DxstkJ8vcadp/m+SWA4DglQfThah0v8AZV8SaTqULDTPD9zC254p729l8p8c5wqb147AjmuuVRxXLSXO/JqK/wDAnd/dFrzPmKtVVajr5hUactfhlN/JKyXzmvQ0de/a5GtbY/D3gqa7CyLIk88whiO1gePlOQcf3gea8r+Ilj4w+KMUuv8AiW4ja2gAaCxht51tLLcQMZI2g843EuTkc9Me/wBv4F8e2dmlvYyeEfDsXmxq1xpFhLLKPnAB3zZZsHnDHFd1H+zn4b1nTxL4q1jWfGN7LGyxSXl20aQEgjMUSHamMng7hmudyqVNHyK3lKbv6ylZ/KKMlilS97C0qrT0vKcKaa7JQjKST85O/c+TP2X9B1DTPj54bubcRyQxtMPMWUhJN0Eg25AJ6n07V+gmoS37TWAntraOL7SmWjuGdu/Yxj+decfC39m3w/8ADrxBHrcWoX2o3luGW3iuHj2QblKk4RRlsEjn1PFep6x/y5f9fSf1rtrYzFYySni2nJKysui7+b1b2XZHJh8NhcNDlwsJQT1ak09Xva20Vsk23pdu700KKKK5zpCiiigAooooAKKKKACiiigAooooA57xp40g8F2NpK1he6te3twtpZ6bp4j8+5lKs21TI6RrhUdiXdRhTznANez+JWh3fgCfxi00lvo1vbzT3PnRnzYPKLCVGQZ+dGRlIGeRxms34oaPqk1z4W13SdNm1q40HUjdyabbTRxzXETwSwsIzK6Rlh5obDuowp5zgHnNF8F+J9L+D+q+HoLOGHxBq0epX2+48ma0tprid5Vt5AxbecS7c7HT5GzkYDVpYDVT45aevh/WL+68P61Y6jpj2ySaHOLY3kv2llW3KFZ2hIkLYGZBjB3bauap8VpdH0nTZrvwb4hj1nUJJUg8PqbKS8KRrueQstyYAoXB/wBbk5AAJOK800j4b3l5oPiOLVvhpe33hmd9PePwvrmqW1xqN1JCzNI5m8+SOSMfuxHBNOEwjLiNSFMvh3wD4j8DtpfiLS/CNybWC61Fbbwda3dqkum2lykWxEzKsAAkg3siyEKJmCbsYNWQHuPhvxDYeLNA07WtLm+0adfwJc28u0ruRgCCQeQeeh5FUv8Amff+4Z/7Vqh8LfCV54K+GegaBeTRnULOzWOeS3+ZBKRltuRyAxOMjoKPsN3/AMJ0B/aU27+zc7vLj/569Pu1AHXUVn/2fef9BSb/AL9R/wDxNH9n3n/QUm/79R//ABNIDm9Y/wCSs+G/+wbe/wDocNdpXn2rWdyPip4dU6hKXOnXhD+WmR80PH3cV2P9n3n/AEFJv+/Uf/xNAEuqXX2PT5pQcMFwv1PArD0u18m805z6yIPwQk/rTNftbySa2tV1KZizbyPLj+g/h9a4H4m+PIvBENrNLrMghgEgRFjj3TNt5x8vC89T6100qcqnux3ZxVasKcnOb0idb8TPHmn+GtJkNxMdg+8seC0jdQi+pP5AcnjNeKt4D8TfFmOPU/EV9HpOh7g0WjRscOoPykp/E2P4n47hRWx4E8F614+vYfEviOR0eTmys3jH7lTyGYEY3Hg4I9CewHba34gsPA+l3mq67q66fZ24DLJKkR59B8uWY9gASa9BVKeEtCjac++//gK6vz+7u/FnQq5o+eveNHotr+cn0XZX9eyyF0/wR8KdJt9XvkS1gjm2yXEwLuF8twFz1ZicYVRycADNea6rd+Mfj/cXOn6ZDeeHfA+7bLEzlLq7jznbLID+7jP/ADzU7mHUjoPLvHXxkf4seJv7WuNcbw14fsWaOzH2NLm8mYA5fyxhFJ4G5mGBgDPzZzbG58TePJbHSdI1rVbuC5LCOOXVl2qo+87QRcRAd8nntmuPF0HWvUx9W0eqs39/6L7+x7eBxKwlqGV0eer0cbLltva+1usnt02u/e7zTfDvwljsdEhtV1bVHTEWm+cIbaLPPmSYICICc8nJ/HIsaPrngTwKk+sa/wCI9J1HV5JE3KJ4vKiTcCyRRAnA7cAk856187eJPCfgPwbZ3TR+OrTVtRh3eZZnTPPEko4IVt2Bz3J/GuAudRtvFElgljp9jqV6W2RaXo9k0UpcnC7yqZbPornmolh8Dhn7XFVGl9nRRXyV25PpdbbaHfGWdYlPD0cI76c0vibvtzSslBddbX3fNofRfiD44aM+pS3ngiG5jkLb/OuZEstNYDqf3xyxGOgGfSvL/En7W/jLxBNJp1nq2l2YyVjurC0dd/qyvLz+JA9sVV0X4B317rdlp2ozwR68/wC8m0+NwtppMR6PdTZ+eQjOIwfTLHO2vo7S/wDhW3w58NHw8Nf0S8t8k3MM1vDIJXxyWLLg+gBPA4FQ8RN/7rSUY/8ATxc0pedk0o+W78lsa4fCKpUca0pTqL/n27Rg+iXuv2kv5r2gtk5PVeD+Ffg3458ZeHzdRXENvaXRzPcS3S5uGLD/AFhTc78kEB/XtWtJ8JPih8KbWW5dbvT9KwGa60qKK4De7KJFb8xUPixPht/ahufBeo3PhjUpWXfeWGoLaQKQQQ2wHB+gAqDXh8UvGlnFNH4ofxt4ehZP3sb+RM4H3o1dVZEPbcfm4zx0q/7XxuHVsXTXJ2goyv58rs1f0ZOM4QWPk69HEz9tdXlW5o/JStKMrdk0/Qh17XtbuLNYbzxHaXgkACJfWuxiTjsZTzz6VJN4B8VRaY13qGlaZp+n26CS4vp9ORIwD90FpBjmu9j8Ta7428Ox+DfDPwte3iUCLy/EywzWNq5z8yfKTK2fm35Bz6k5riPFn7PWh+BtLtIL74p6Zc6zZu07aXexG4sFkIOY1hDMwGT94qc8DArkxGeSqu2GwilHrf3fl8Mb/L0LwHA31h8uMxjjU6KEVPTveLly/Nve6G2raJfW8aad4z1HW9VVAW0rwf4bZ5QSeAWDRr+IJqeHwB8U77beW2heJ5NOY/u4rjxPBaXTr/e8sh9o7c56GtLQb/w3ppu9vxE/ta8eMr5OheGprOGPcMFAFVSy/gK5yH4b2euM0OmfDDUtUv5JC51GQ3UaHPcyu429Ohrmr08fiYRqUaiinvG0Y2/CpzL5p+R9Hg+GsHgasqOKwcnbacnNp6a35pUOR/Jq32mTtH4v0+dob7w141hfBwjeMbViePQwjd2+tUNe8dazBIVPiH4h6DdxjaI7i5lnhUennRSfyjrtfC/7M3iqOGa4ji0vwrbAM7SzSyXsyEDJCZJXP0aupn/Zw8TRRr5XjKxmd/mE8+jxYbjpyWx+ld2Hp4nDJXxV59uWMl99oP8AA8jGZTk2Im1CjTpx/mVSqn90Pbw/8m+R4ZL8Ynt7JXTxn41upmO0rb+KZAzt/wBcjDvH4/nUVjrPxH8TFZ7LxL4g0a0zlbjXPEknyD1KKA36V6+Pgj4sbUY7bUdSa4QqT/xLdItIZMZx/r41bb9DzWhcfA+w0eMXV34euLwRjP2zxDrMLov4GSNfwIp1MdXaviazsukYqP335/wt6nVhMjyfDv8Ad1PaSffmml5LTD/jzenfzLTPDXjDULSOO6+M4t0CgbI9QvpVAx0ACY49M1ck+G2pLGzQ/He/M/8AdSK8Cfnu/pWzrvw28K6naw3MWt6JoFxsB3aeWEy8d/LLLn6CoLKFrHThDD8S/Fslsp2r9l1O4gi+gHl4rzf7Ry2TtVnO/f21l+G33Htf6v4ipHmwdSME+jwaf43dzh9Q8H/EPT2b7B8TbjVV6gQ3N2hP13BazZ/B3iRbYT+JPFepR2zcB5L9oIx/wJtw/UV31z4BHiJyR8RfEW/rtnvzcn9J0OPfFS+Gfgf/AGTOupRX2gancyHPmeINLkuwSP8AfZ1/Sun20a0LUk5LzxFSS+5LX0vY43kUqFS9XM6UH2WEpwl8pOOnra5wOj+HfB159otYrS38R3aj5pYlvr6Rc9w0EgU/lViT9njWNSiNxpeg6xBCeh+wXsI/AuDX0jp3jL4o6Fax2mn678O4bZOFhEUluF+iqABVib4qfFi1Uefq3gF1bj5LmT/A1pTWHgv9pw9Jx/wv83J/kc2IyXHVLfUs0qRl39urf+AqEbfJny/Y/BLxvaqyWdxrtlGSQfJmnC5zyCFX19a5XxV8GfEljHdBmmvbzKhopnPmMSR13AH35r640/4s/ELT4ykUvg6YmWRvluiDkuSeqdOePbFeHfF668c+NPGV3q+pNbwELHGn9n3SiHAUAFcEFj7kZ7dBRKnk1SjUXI4RaabjK1vla23dHZlORcRzxtKVXM6bcGpL2lp3aast+a7fZryd7H2P+yj4fuvC/wCz94R029x9phinZ9pyBvuJXAz9GFelaP8A8vv/AF9P/SvP/gDcaprnwd8L3d3qEkVx9mMLqkMYA8t2j/u9cLz713mgRvFDdrJK0zi5ky7AAnp6cVMY04pRpfCtuunTXrofNYyNaniakMS06ik+ZrZyvrbyvsalFFFUcgUUUUAcN42+Lml+BdWNnd2Go3kdvbreajeWccZh023ZyizTb3VipZX4jV2ARiQAM1qeNvHVt4LtNPY2F5rN/qVyLSx03TfK865k2M5CtK6RqAiOxLuowvXJAPnPxa8C+JNW1fxXb6NpD6la+L9Eg0Z71bmJI9NdHmDSzK7qxQpPkeUHYsmCADmtf4raDrGuaXo8FroOparpulagpurPT7uC1v7uMQFUltJzNGYSsjjJ8yFyFcA4OHqyAt33x20aHQ9I1Gx0rWNabULee7NnYwxefbQQMFuJJRJIi/u2IUqrMzH7ivU198cNBtNXtLaK21C+0yX7IJtcto4zZ2jXRAtlkLOJCZNy8ojBd6lyoNcBp/g3xv4Y0nQtSj8MDUr06VqWktpVncW0L2KzzrLbNKS6RMEVdsrRlmLcqJOTVOH4N+JvDujt4Ks9ObUdN1M6M82vLcxCG0+xpAk6ujOJMstuDH5aMCX+YriqsgPo6sDwj/zGv+wnN/7LWxdQyzxhYrhrZs53KqsSPTkGuY8J2N239s41KZf+JlMP9XHz93n7tZgddRWf/Z95/wBBSb/v1H/8TR/Z95/0FJv+/Uf/AMTQBzXwp/5A+s/9hu//APR7V2tee/C+zuZNJ1grqEsYGs3wIEaHJ89ueV712X9n3n/QUm/79R//ABNAGhWfoP8AyDh/11l/9GNR/Z95/wBBSb/v1H/8TWfoen3f9nj/AImcw/ey/wDLKP8A56N/s0AdBVXULs2sHyDdM52xr6moDY3agk6pMB/1yj/+JrPs9PvNQumu21Kby0ysWYo/xP3aiTey3ZrBL4nsina2z2/h21P2md7iX92iB+NxJqv401zT/hl4Vl1aVTPdRr5VtBkkzzNwqge55+gNeYeK/jhpng23kaLULjWLnTYWJjt0j8qJiTzJJtwOwwMnJArktD+Hfin9oSePxR8Qrme30i3GNP0KEmHajn777cHceD64AycYFcTqQqqVKg/e2vuk/wBWt7fefX4XK506UcZmT9nQWr/ml/ditHqurslfc73whq3hfwH4fub/AMV+KtNsvFOpT/a7qSS6UzCQ8hAgJYhckYA9cdq4201q8+NixeH31UeE/AlqFXU7iaZYL3V3AGYkUnckXbJHPv0G1q3wn8K6PqOm+GvD+kWMOpXq+Zc30ttHJLbW6kbnDsCwJPAwecY71vSaX4P0jQ7FbhbMym3jyv2KKSZztHRQuSfc179HCUcPRipX129O79X/AJnztXPvZ4ic8BHmmt5TsuWT19yKulyrq20tLK6ubja94Z+Huhw2+mNB9lt1+z2Vhp5Du+ScKqg5LEk5J7nJOTWRdWK3E0OseOJF88Ddp/hq3+fyyR8rSD+J/rwP0rkLy+tfCt+t7axWvhqZlxBHNDHJfzA4+5Eo/dkg8EkexrS0fwl4h1q3nvNQlGk2EwJdIrhHv5xj+NzygPdR+I716EYwpRcqUuVPeUt/SK/Nr70fE1albE1LYlOpJbQjdr1nJ/k/WzZY1r47DwuLhzb29n9ntY4g08wdlwByVH3ecfeIrnPAXw18V2fgsfY520S3uLh9QeS4fdcTO/LM+3ByRjgkdBxWP4o8O2fiDxN4Q8AaaUhtJXGuauBGm7yYhiJXbGWDMSDuOOVNepeJvixoPhmN7S78VRkwgHybONJmzyNuVU814WJr4ai+VL5yfX02/Fn36wWMjhqd6n8VKThBO3LG6V3q22+Z7RsrHnHxQ+GeoeGRp+saUX8WWNqnm6lpjAwvJGecxFD1Uc7TnPv0rvfh343+HuqXMVx4Tis7e/jTbdafcQrFdKR1JDfMSMdRke/NeJ+JPjxrvjK1vrXwRL/Z+mxgpd+ItYeG2giBGCqbuC2D2JI9O9cfrGm6Hr7Qaj4r+NWj3F/EAVksrZbqceg81FBOPxrrrY1UkoYiuoK21ml87Ky/M5sJgMNiqNlhJTmrpVItOXkmpP3rd1Jdten29q3jNbG3aWe4t7WF1ADu4XbnvkkDPNed+MvjT4Y8LeD7kQarb6nP5LiK1sSJHkdhjBfO0depPAHc8V8Za14hjs9ehTwt4rl8d3t2fLS3udGlZ++SA4O7GO35Vx15da5qmsXMOpyW0V9cHypvtVuEW3CkMFjiRf3fIA+UAk5zwTn5qssXV9/BONSH80by5fVWi79vs+Z9Rg8LlOHtDOKjoSWvLNKHMlvZ3kn96l5dT6M8TftYeKvEkkv2GPQfDNrn/XX1x9pnAx1C5jTPsc1wWm/tIeLdNur2e08VXWsXJiPmCaG3igXbkhokdQqnrwpbOeQcDD/Dvwp0SFH8T/FXxN9itsb7bSI1RLqfrtb7OgPlqfp+I610vhPR/wDhKLq6sfh18M7fcwLNq3igxSNt9Y4Zsrkf7Jb6VeF4dx+Jft8RVlGK6tpf8Bei5mb1OLuF8PB4fDYdzv2jdvzV/et2cku9tjkbT4ieBbzT317WZdY8W+LZ08x4LjEMVu5/gklZjJJ/2zCgV698Jf2UdR+IVkde8fSyaXYXQMtpotm2xtpHyNIedqgYIXlj/Ea4Z/2Q/G99C4uhodjepL5zXrXUzysCPu7VXytnPTb7Vur40+NH7LOgf2fqthDrvhS1k8tL+1czR2uSPlLFN6LzxuXGfumvRisNlUkoxU6j3q6Oz+bbj/i27cuxwVpviDAyoYaXsoJ6UNVzx6O6ST/wRSv9pyaR7n4N+BeleHPE1lM95qOrTafD9nsotUlWVbEZJYxAKByOhIyOKt/GD4iQaTqlpothEt9qStHst/OESRAMCZJHPCDgLk968f0P9rrxDrUfm2nh8Xl9eQkWs1jfw3OWHQNCsW8H/ZbacCvPIdFvm1K81X4k+HPF2oSzzCX7JZWyfZ3kLDmaTeN3HGBwvbPNcmIWLxVf6vg0pS0balFxS6a3au+2vdo8PL8HgMswksRj26cLtKLjJSlLr7rtKyWl3ZdE0e7a5+1NodjJbw61plrZ6iW2u1nq9rcnA68Kw/Wlt/jyfFVrHH4U8P6j4gCbm6KsUee7OhYAfUr9a8wsbfWb64lufDXgfQfCOn2/+surqzivriJQM72YKFHAyQzcVT0bxNqHiC6huPHnibxNpnhvUiVtNYLPBp1wAdvzR24QICQcA8Y5zjmu+lkuOlrPELzUUpS+T0i3+JhUznK5L93hG7bOUnGPzV3K3XdLzPY9at/Hn2GG41/xXovg23kkheKzhaMEsHG0uzN0HU4YqcciuR8SfGvxV4K02803UbnTNQjuE8tNWsSykZYZOCq54OPlUDp81d/4P+BfhK00a21Gwliu5JLmOM3duqMsoLqMhxktkHruNdt488IeHdL8A6yusXMdhoggLXMiW8UZAXkNkLksCAR3yK4a2XRp3WGnPnWzb3fZp8yt6I66Ocuo08bSp+ye6UbWXVqS5W36s8E+BfxMmufibpNtBM09heM0DPtCsxKHH3SQV3Y+n4GvrDWP+XL/AK+k/rXy3+zrN8LtY8dRjw/4j1abxJEkjWkGoQoildhDsmIwC20twx6EnHGR9MahZ3MU1g0l/JOguUyjRoAevoM0sHhswwsHHMVabd0uyf8AwbhmuKyrFVoyyjWmopN95K+v3WublFFFd54oUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFYH/M+/9wz/ANq1v1gf8z7/ANwz/wBq0Ab9FFFAHF6x/wAlZ8N/9g29/wDQ4a7KR/Ljdz0UE1xusf8AJWfDf/YNvf8A0OGun1iYQ6XctnBKFR9Tx/WmtwONur03pnvb248i3hjMk8oGFjjAyf61896PrGnfEz4xPqetBLXwvpKiTbccRxjB8oNnjPG8k9yvpXc/FTxLDc2UmhrL5GjWULalrdwr7SIl5WLPq3AA9xXh3w/0XSk0G4+I3xECW/hlp5ZdP0uXgXMrDhyufn4CqqnjCknAya9dcsaTlN8q/Jemmr/Ba9T52rGdbERpQXMo6vza2V1fRPtu9Oh6d8SP2stJ8HrPB4Nt5dfkJaCK4mUrZxvjna5wXIznA496+e/Cfh7Xfjx4oupvFHiC4Gl6WM32qajN/o9mCc+Wu4hVOTyOAOSRng0PFvxOj8Zalca7BoskmQ1no0ESeXZWAAJBzgB2XO8gDBPXAwBW0C2vZrSLw3dl9Ye1H2pbMTbNOtyw3NPc8Zkl3Z5cnoQARjHy2LzpUGqOWR02ck7tvtF/ny/8FffZdw68evaZzN0+sYNWaWvvzS1itLR53Ft9b+6/UH+KXhnwHqFlpXwt8MWOuSRM0Ta9rwPk3DkMhVRwzjnttXjoeteT+LNAnt76V/Eusafp15JKbl49D03fO+89SuUEannap2D1A6V6B4Z1zQvBeL6C3PirWwNrSFWS2Qbem4fdUcfImWfBBI4ziXyTeOL6bXvFWow2unW5JS0gjWFSSf8AVwwJjGeBj7xxlivJq8uoYzGVr1a/LLspNJLa2jV39/zPtMZhMsyXAqU8OoYdW1cVKpUa192LTUV5tJa3dracToVppzakI73w/qHiJj/x7Le3ZiSNf4flVdoyOxOOa9X0rxd4rsbL7B4b0eHRYQy5trQyzEHsQsQ659q6DSf+El06zs9M0fwj4TvL68TfDp90s97exxn7vmqh8qM4IJHBGeavab+zL8Rjb3MC63pHhgzXKSNo9rLcvArscbgpLKuScnbnrXrexwVGrKlh+So1u3K1n2bSk396fkfNPOKtSEa9XDypQlqk7zk1f4rSlCEflTa/vHIw6D4iupDNreieKta3MXa3h025t7Zyf7yqgJ+u4GrF1pLahbmeP4eWukWNkMyPrMYsreLH8Ts7NNMP9lTnOPpXcXX7P/xL8J2Zln0fwR4m2/8ALSe8vkkP4eZEua51fhP8SfFHiixu08I+HtEtLVdo00Xkpt7p+fnkKSPKRz90MvT65cp5oouGGp0kn/08vbz1V18tex0QznJedVa8q75dUnHli3/hpOEW+19O+hV+GvwzHxgV9e8XT6T4Z8JwSJFZWGn20Gny3vzqC5UAOIxyQ0mWbrxXq99+y58G4132HiT+z7gdHXWo/wCfasy6/Zb8ZX1uLnUtc8NaGS6AxaXpRuQuWAxmVh61uW/7E9lcf8hPxrqU/r9hsre1/LCtisKWEqwgliK8HLy5n/7Yl+JGL4yxdetz4T2tOHSK5Iq3pzu77to4rUvgz8NtHkY6z4/1K8tV+7brq0cwx3wsaEmqVx8JfBN1p0z+E/DfiK5mVdw1C6vhZW4HqGf5mGO3Fdhrv7PvwX+GzF9f8c6paTr1judZQTn/AIBGgY/gK5G98O/CjXrO4/4RP4f+MfHRVSRqDzXMNmPcyu64/wC+a7I5TPELm9tJR7qC/wDSpyt+BwVOPMfR9xay7Oo1/wCS043/ABKGrJH4RHlPqmtaW6HpD48t05HorLn+dZnhv4ueLbbxIkfhy+8QeMx5ZE2lX0x1Lfn7pEiKpXoeRnNej6Podv4TuVms/wBnS6Tb/DOLe+I91dyWB/Gusk+I3i7WP+Jb4U+EOo6ZfOBltWlisra2wR8wx9/twMGuyGVxo/DUlJecqSXzstPk0/M8avxfjMYnCvTglLR+5Uk/lzPftdHA+Gdc+JHx20u6eDxXpfha1txJFcaTpduzXsG3qJfMwY8+qnnnitDxV8Of+FcWaza7+0HqGluyh/s12vnynIzxGJC5H0WnX/7OPjfxodT8V6h4wsfDfiP7NLC0Hh+2kiD8Esk024OwIwDkH8cVN4d/ZFvPDMA1O58XQ293IAT9h0pJJy57rcTM7g/7oFaylhqenPp2gvzlLX5+96mUMGsVVcqU/Zr/AKett+kY04tNf+C15Lr5jJ4q8amG4m0vxZ4yvdATj+128MKI8nuCJN4XH8XX2q34D1K61C88+G88D+Prw8CPWby7t71j3HlynYf++TX0NZ/su6FcwiXW/EHinWr2T5pZbvWJOvp8oHFUdZ/ZD+G6xm7OiXN7JH80izahcEuO/IcHNZyrZfipL61QUrbNqLf5L80XWw7wFJrLsfO/VJOMX6Pndl/269DzzSf2kLWxvrTw/efDNmv4YctbeGpre/kmKqMbhGAUB4JByRmqd1+1BrevXU633iTTfhhDECkOj3eiXF5PIP8Apo7RhBj2x9K77Rf2TPhtqul2bxeF5FSSJHe4udQuQWJUElUEg/Wun0z9k/4Z6RcRzW/h2FpVH/LyzTqffbIWH51t/wAJEVZxkvRJfnKRxQzDM6Mk6EYS03qfvPVpckVr5p+TPlDxd8Wo9Qukhi1vQPE13MdqmHw3HaoD/tSyNHj6jIqz8O/C9t4u8RRaXr3itfBepXBzb262yG1u/wDrlciQox5xj14GTX2Td/A/wdfxCO50HTJ41GAsmnwED6fJxXmuq/sieBvEWjpPFpK207KSXspHhfqeAoJQ/itc0sPkeIXJOk4t9bJu/wCC/C/md8eI+JsNLnpVIadFCKVv/AW/TWy7GjY/sp6fHAsV54i1a4H8RWSMD8AYz/Oma5+y34G07TZJ7i71dyowpa8VBn1JCD61xn/Ctfi94PubWz8CfEK6fw/INqx65GtzLbKP4ULRuGGB2CY6YA5pdZ/Zf8Z/FWUyeKPijfahHEwzC9iFgVx12xLJs/HA7VywyHLqc1UqVIcv+Ft/dbT7xS4vzySapKal5OMV963+4ZpHw0+C+nQCSfxlpFzdRyyJJb3niRF6SMBjEgxwBXiPxa8SeCdD8eXtt4alNzpsaRlWt3E8asUG5Ul3HeM859SR2r3PTP2Ude0mx3aVrfhTVUSWVBb6z4Ps2J2uy580KWOcdO1fK3x08J6z4P8AiTqVnrem6fpl0wjdY9Itvs9m6+WoDRJ0AOOcfxbq9WlwzkucTdOcr6XvH3Zf+krT7zy6nG/EmUJV1J9rT96P/pTs/uP0T+AR09/g74Wk0yaSezmtfODyrtbe7szqRk4w5Yde1dho/wDy+/8AX0/9K4X9m7TbvSfgb4Ptb22a0uVs9zQupVgGdmUkdiQQfxrutH/5ff8Ar6f+leDVo08NUlQpfDFtL0WiOuOIq4yKxNf45+8/V6v8TQooorIoKKKKACiiigAooooAKwPCP/Ma/wCwnN/7LW/WB4R/5jX/AGE5v/ZaAN+iiigD59+Cnxkt4/iR4t8CawYbW5fWbybSp42yk48wmSEntIv3sdw3bAz9BV+a/wAQvhLr918WvEWr6e91Gk2r3FzFeafcIhikE7YbBOcgjgjkV2Gh+I/jr4cmiNv4yuL+FT01IRz8e+9iT/Oscbivq9SMZUXqlrD3o/8AyUX3TW97aWO/C5esRQlVoYiLs37s2oTXpf3ZLs1La11e598VnaGwXTck4AlmJJ/66NXzrpv7RXjqz00R6npOi3V4Bjz4ZTCv4qXbn6ED2rndQ+OnjS4s3tBc6PpsLMzeYkiu4yxY9WKnr3Fcjxq+zTm/+3WvzsdlLK3L+JWpx/7fi/8A0ls+mtU1wXSSxQSxQWqDM13O21FX6noPevAfiN8e7DWNQbwxpnidbTRx+7vNR0+FpZJR3itwgJJPTdwP6+I+IruLXL5brxR4nj1SCM58r7U8rfRIlBUfgKq2XxC8JaHeRQXOja8thId0Uf2ZY0l6DOGKg/8AfJNZr2uNkqSg4p76q78tOZ+ui+R9DTllGVU3Xc3VlHb3eWKf83NNWflp8me2+AdH0bxEtlf6rbwaN4X0uQy6Zot2Qkt5OM4u7pTyx/uqc4Hbru9O1b4saPpOnM1qJLoqd8jqCkW49eSOfTvmvm3UvGeratpivpfwr8QQW8MZd76S0uHGzBO7KlVVcA9QR1rh/Ddr43+KH7zwd4Vub+OVzH9ucBbfg85Y7U/8e/OvqYZdOnbSCS6c1n8la33tI+Cxuef2m2/fstvdvFXf2pXvd7uyk5M9y/4WDomlXV/qF7rz3F7qBDNZ6VGWlx2jMn3VA9Ac+uTXFaj8fLHSY1SQSaLAqAFrGNWu5PcuxG3PXIqHWv2UfiJJoNxc+I/Fug6NbiISPD57QQRYP8YjjVM/n9a4v4ceEfE8M8dn4R8OeE/Hk2N1xfXel3NzbQtxw7zssQYeqL26150sRXw8nKq+d/3I7eXNJuKS6csX6o9nB5NRzKlJYWHJFatznZerSind9eaa16Prp2Hx1vpriVvBulaXo7N97UNWmS4vJSe/mSFUH0p91J4u8Zqf7e+JlhBCwy8D62sCH28u3VlP0yK9F1r9nP41+IrdZxqHw90RWTP2PTdPEKp35cQE5/4Ea5Wz/Zi+JGnqZtQ03Q/E5y2RHrFxCpA64G1eK4KuZTqO8lOPpFX/APAvel/5MehhMlp0af8AvGHdujdR/er06b/8B+Z49o2g3+uXa22m6veXWu3k/wBmsdKt7V5vPRcEFneTgAZOArYAycDkdto37OviLWNWmtfEusxaTYrLm4uFhFysEgHC5X5R1PAbFe7fBv4Pf8IbYxNcajEPFt3bKNT1bcBBpVswyLOEk9f7xzknJJ7j11vE3hbT9Hbw7bapos+6PyxHHdxuXyRu3LnkmtsPjIxcamIhFS+y5Wc12956p/keTj8Lia1SrDC15Spv4o004U35KN3p0u3d9raHyn8Sf2Pbbwn4bHi3TtfsNTtbeIyXMOtRyJGyBd3ySRucZ5AG3kkc1xHhDwDrGvXBvNJ0W9+x7SwXSEewsQD0/wBLumJbA6jYK+g/GGl2fxA1DXPC+mJdXHhrQTDeXtutyPs7XA5W3BIO/wCXDMoPUEcEAVY1K51/xpqkHhrTQz3UahrhbdSYdPT+9IR96X0XtjHrjOvh8PgZqrODc5O9r2vdXW1m21rul1ZWGxFfNaH1VtOEE/ekuZpbPWV0knorJvoux882vw0gtdcnh1LXLHT7idxHL5EUlz8vXDybo9xHft6V0XgX4H2HxAm1u4vvFEOnjT5mgsTDYx273SRAsJR82VycY6sccnivX/HHizw74F8F6n4Y0PRZbq6hje2+33CI6S3ki7WZ3P8AGqhmA55XHGKr6l8N/DF1oujxeHL/AO0avDaia61GxuiflRfmZ1z8rM3bA6169Orm2BpqtKlGK30VrX87O+j1vbucqw3DlWlOGHqNzqe5eT5m1FptpXTim0krX6xTtzGj4H+C/hm11Yx+HbFpbS3mxdeKNQHnTSyf9Mt3Cn/aUD/H2xvBWgR6C9m0IiNqxkS6ibE4Y9HD+pwPbj2ryHVPgydDt1Ft45mtoFHzyX1ujJnvyWFceNJ8OQ6hJbat42N1b7djwaRBIHnznlyNy4HHA9e1cNTFY3HO8aM5S2TumvvTdvu+RlDB5flytUxMIQWr0kpfJNK7+fqzsde+Kl94bknsrMf8JdLCMPPZk7YVJAAmYAqCDxx3681X1DxJ4t8dSXKX9y2m6Zep5c9lpNgZPMXGMPJL04J6cVj6h4+tvBOjNZ6ZPNb6F0/0rT1t1kwc8PkFjnv1rA1r48P4qjNpY6fqHlMNoGkoI1c9991IcIP91SP51vQ4dx1b3q8rLstF/wDJP52XkhVeLMvw75MHT1/maTdr+fuq3ldro2EfhvWPh74iuJvBN7o1lbrCC9j4huLeKUkfwo/GCTzg4HAznFbvhv4o+HtdtLibxLBfweIVlR2a/wD39thWUsImjBUjAPUeuK5Gz8Qanb2paz8N+BbJnPNvqjXOpXJPcmSNWQnPoRWP4k1XxVrEFzpPl+GrHeoZ1sdMKPGmQA6+aMjJwASB9RX02HyOjQhyy0XyS/JnzuP4sq45qjVpxlUWikk3P0aTSfzV/M6r4nfFa4+Jti/hTwdYyS6DbSmfVrnY8cVyQciH5BuEfA3D5SenHWrPhPxD8QNcuH02NtO17TzEobSbzT4oreOHG0LH+8GUGAOcj2qTwX8H9Sn8M22nLfX8thu/fGOTyrWFifvE4+ds9SAD7mun8ffAHwx8Pvh/eaidcltb/hjNdXREczY+6MncGI6EHnjiqliMDzKhFX3s+l+7tZu/W23S6PO+s5xh6c6kYxS3ldRcmv7vMpKK7bN63PLvEU2vfC/X5BoFhqvgeKaVWlsVWS9015g3BjAQqrbsEYbHQdKwPit438UeLvD8+m6h4p8ReILmQKHto7O3tLRTkEq8cZLNgeoHOK19D+HuufEy3jfwlfa5pukpJFb3dzq+qbkMjkAKkSgMVyfvHt713En7PXjDwv4J1Ww0/UtNuPs/7yaxt9OUzTYIO5Xb5icD1ycY71X15Ka9rFKatqkr/fK9/v8AnY4qdROMa9DDynDW8ZSfJLySgqbXm7vyVzyL9mHwTrOl/HvwjcyWTpCskzM25SFH2eXOcE449e/FfoTrH/Ll/wBfSf1r5O/Z90HXZPiLpLtYXwjs2ke6vJrOSCONfLYbfnH3iSFwCTyT2r6x1j/ly/6+k/rXn51WjWrwcZ83urtpvo7aHuZJWVfDyn9XVH3npHms+t1zNuyXu7va+9zQooor58+gCiiigAooooAKKKKACiiigAooooAKKKKACiiigBskazRvG4yjAqR7GuT/ALDsf+E6CeR8v9m5xvbr5v1rrqwP+Z9/7hn/ALVoAv8A9gWH/PD/AMfb/Gj+wLD/AJ4f+Pt/jWhRQB59q2jWa/FTw7GIcI2nXhI3N1DQ+/vWB8TvE1jpc8Wk6XZ/2hrE/wDqbRXbA9ZJDn5V/nirnxOl1r/hP/DsGgQo+o3FjdQrLKfkiUtFuc/7oH6jr0rxP47eILL4R+F7nT7a5a+8Q6v8lzfSHMjoeGI9AcYA9Nx6iu2ChRpqrP3pPSMV1d7K/ZN6f8DU40q2MxH1al7sVrKbWytd27tK77er0POPGWvWOpa5ZeG2naXQ0uDd6/qcRYjUHX5jFEO6Lwq4+8xz0FcT4g8VN8TvGkV/rGkXUHhfTyU0/Q4kwqgYwDuwoBwCSeuAMYAA4bV/FF5eXip9sETiPDCJfLCqO5I6sc/Wuiv/AA3Na6TYyahf3kU16iyJbGTP7rJG5xknJI6Y/UYHJi4VsfNU6leC5XbSM5a7XfupO3TWyaS2jY+ry+hRyOl9ZlQquVSyUW6cZa7JR53NOW212m2tZXWv4u1hfGGqW9wwh0jSrCMW1rY2koVIsnJLzNgF2PJCgcLxwK7f4c/8K70/Q5ZvE/iKFrdHZ00fSxJvkx3Z2T52bt0OMZbqBX+Hug+BfD+sRXXiS2bU7lUWSLRbJRctGABtErN+7VycMwJwvTAOQPSruGe+8UjVdc8M3DLDHs8M6ItqDbpIw4mlGAZXAOQNuBxxXbgsgwctffqvq21Fa72Wqt+SNc6z/E5dKVKsoYXRSVlKpJ22Sdo6t6cz0fvcsnbXwtdesJZrqewlMzXU4Sy0lT501vG7HC8ACSTGB04JGe+Pp74Rfs02UdrF4h8a6dBd6zdxBbHRgSYbCLHBcj/WSkclj0PT2m0e80DwrYadbw6DfSausm+RpNMK3E0pVtxUn6469AK7W4tfG3iyzmurmdfCGlsu1Ybc+ZeSL0C7uiDp05r1cRh6GGh7DD0lTjqrtK8r72sr2fXy0vY/N6eZ4rMsQ8XiKspy0tGLfLC2y1drrpd3bvLV6m/4T8J+HfD880NjpkNrG7bWaPIJPY5z0NaPinwfZ3X2GaAG2uFuY18wFjlS3TGa43UfhAvhtLe50bW9U02cqPOcTeasjdWLI3BJPP4Vbm1Tx7pkdvby2+l+I41liMVxHKbaRvmG0sD8uOnSvJWEo1YWozWnT4f+B+J6ksdiKNTmxEJO/Ve/8n9r8Dr38F6Np8L3VzC95IgzvnkZj7ADOOtR6Doumru822C3MxLbtzAEeg57VwnirxF4jh0+e98TeJ9F8HaNb/NLNbxGZhnouX+8x5wFH0BxXIeGfDPjL4srdSW2qa3ofg6aIpHda1KftV6f78UKbTDGRjq2T174rtpZdCNNyqVFFdXq/wBNX5K5wVc0rVKiVOjKXlovwvovN2N/4j/FOwbVJfCngXRv+Eu8TRvGbgxTMLOxHmLgzy5wDnjaDn6HioIPgb478bMJPG3jltKs3wW0fwohhTHUqZ3y59Oh+tWdI8N+I/hB4dTQ9L0jQbjS43jYG1DwzytvGGcnIY57nJrpf+Eo8f3kgSO08O6cG73U0rlf++cV0cnsdMK4JfzNpt/ft8lfzZzfWFV/3qM7/wAqTSXzW/zdvJE3hb9nT4d+EAr2Xhm1muhybu9LXEzNnJO5ycH6Yrb8WaLZHSprSKDaWiLNhm+VB+Nc43ibxZ4LuBfa6LPX9JmGJn0gHzLXH8W0/eXB7V0Nrr2neIvDF/q1new3MdxGwVlcfKB/CR2Psa8nGU68rTqT50+t7/LyPawFfD6xhHkkvstJPyfmvS/ZmnYaTZbJIZ4f30PDHe3K9j1qGHwzaXVu1wsO2dmLod7dOw61Q8f+MNK8LWsdxPc77yTMcdrb/PNOD2VR79+nNYlrd+OtWh/tBNQ0jw/bOB9n0y6TzmVMcF3B+8fQdPaihh5+z55SUV0b6+m7fn0JxWLp+15IRcn1UenrqkvLW+uhq6rpdjaR3NyLfZFPE8U6b2+STacHrV7TdBsdVuY5/I/0a3ULH87fM+OW69q4bXtQ8b/Y7tJv+Ecv45IWR/sxnRiMdfmG3IrU0f4pXGh6dBFrnhXUtOgiQKbqzAuoQMfeYryuevetXgaktaTUvRq/3bjjm1CMf3qlGW13F2t6q68tzv8A+wLD/nh/4+3+NMk0Gw8t/wBx2P8AG3+Nc/p/xk8GaljyvENmgxn9/J5R+mGwc/hTdS+L3hG1tpSuu2csm0hVjYtk/gK5/quIvb2b+5mssXh4WcqkVfzRoadpunWuhacWgLyNbx7UV2yx2j3pJPDccafbHizMpz5Ku2AncdeteeWnx08N6PY2aLJcahfmJIgIoDnOANqBsA8+9QjXfit4m1RLqy0CDT9LXOLbUvlL+7fMrA8dAO/Oazng66/iRs+10mvN3a/ra5pTx2GavSldd7SafkuVP7/vaPUmttIVAViLkjO1WYn+dZeiWto2hW862kaQBT++lnIXqfevHNW8X+M476a08RnUPDeiM5ElzodgtzGcdvNDsyj/ADitj4baF8OvFXyTa1/wkNwpDwpfX7Zwewjyv8u9FOrgebknWXMuiTv+PL+p0VsDmnsfbU6D5d7trlt6x5tfJpHR6l4w8JaDeNZtrFgHuCSPInMiox4IyD8uOvNdT4ZuPCmpWcUOn3tpcuowVjugWJ7nAarH/CN6PpkiWem6Bp5CpvkjWBF46AZxyTz1rH1TwD4M1jKXugW+nTn+JIfJyfqmM13OWEmuV8y89H+Gn5nhL69Td7wfl7y/HX8joND0GxNi/wC4/wCXm4/jb/ns/vXMeMPGngTwvqyafq/z3seHKLE8nk5GQSexxg8c9KxtO+E/hprNma6a3CzSoPKvpc4WRlHG70Arx74peGdB8O+LltYfFOmwR3Kqyx6ldMksfGMtkH5SRwf8K7MHhcBUq2q1na3a343Zy47EZvCjzYfDJu/dy09LRPqnT7DSNWsYLy0C3FrOgkjkR2wynoetSaBAlrDdxRrtRbmTAzn0qn8P9FTw/wCC9HsI7qO+SK3Ui5hOUk3fNuU9154PpitDR/8Al9/6+n/pXh1FGM5KDur6eh79GU5U4yqK0mldefU0KKKKzNgooooAKKKKACiiigCC7s4b6MRzpvQHcBkjn8PrXMeE9Dsn/tnMGcalMB87dBt9666sDwj/AMxr/sJzf+y0AX/7AsP+eH/j7f40f2BYf88P/H2/xrQooA/ND4qXTWXxE8U2sbPcGPU7rbBMDBOVMjHMTjhx1GDg9Mbs8c94a1j9/u0ieN7iM7jY3gIkjP8AeTHI+o4r7S1T4K6J8W/ButJfW6rqEOsagILtVy6fv249x7V8ifEX4Cat4S1KWB4Pt/lguIGJ8x4/70TD749V4YenXHfhMHXw96+UV2m/ipz1jfq15P00Fjs8o4qEMFn+G54x0hUh7s0ui16rzdn6myvxL8SybotQ0201FV4P2ny2f/vqTLfrWhoPx2vfCMDrbfD3SZVYktdX0chxz/eUjiuJ0rw/Lpunw6vay3mlJMvli7lVb3T3IzhHyCYj7NkjORgGul8N/DnxR42kdtP1HSIb6P5nsVV1d1/56JltrKf9g8Z5A6V58s8rUKjWKwMJT2TXu/q3fy0PYwuQ5Vjlz4XMKkaUUnKLd2v/ACWyXZ6r7za1D9oLxD4oVLO3Gi6MJWCgaPZXE10SeAsasNpY54z37iuo8F/DvxTb3Eur2mgWWjXx/eT+J/Glz591H0+dYgdkJB7OSRyMmvLtR+FPiC21CS21GwtJZyOfIY2z89N3mMOD61Y1L4I6t/YcN5qVldy29p8yQHUlvIYvcAO6qP8AeAFdtTiHFVaSo4HBcre92v8AN39Hf0IjwnktPEOriszbS2vCVkv8Vkn6xUbd+hc1vWvF/ifSdV09dXh8ReA7C7WPUtXhkj01tS53PbROzbXUdyq88diM+mf8NJaVoegaZofgXwZNYM0CpG2oROY0bGAI44SxfpwcjPfrXgL64ujzWy3OmXDRW3yx/aP3aQqTkiJVUogzzlQOSTXpHhz4veGLxX0x/AVjqTXTLvuf7Yfz89wvmKGyfRa82hhcfK9athrSe8n7mnZJKVor0Te7Ptp/2JToxo4LExqpbRc4Nc38zUpwc5P/ABcq0XLY3b/wj468ZWo1zX9LaW2RsRz+K5TBCHOflisITncM4G9iDxnvXZeHdQ+JnwztbIT+DLXxVoojVUewElpKyY7j5kzjkYArzxvi5o2na4Np8R+HoLORWtLXzIdRjhkAwWMcjgdun611Gn/GbRPE9nDBrPivxXfxRoq/YrC1S1i4H3TsY5A6ZJGcZrvjmGKdqMMEn87r/wACvzfcvkfN43Iq2ITxOIx3ura1vd8lCK9mvv8AO56Gv7Qvhgxva6p4d13RLmJSzWt7AU3d9u8NjHvTdJ1LV/i1p73djqNj4c0BgQI9OnF1fTjH3eGxGOMc7T7GvOLP4reDvDWtSRaT4Qha2kUkyX1yJbzPclAJFGef4jnPbFcRr3iTSfiFrhTwp4c0vw/qi5Y3miNO96mPvMFhaNfrlT9a2pZO8ZP/AGufsk/sprXyd2pW8kvW589WzGpl1FVMFh5VbfbcG0vNcqlTv/eba7WsfVWi/B/wLZ6fazaj4fvbudo1Zri9mkcMxAPZgv6VxnxisfDOg6bBZaH4X0+XXdUk+x6TaGEf6wjmVyOioDuJ6cAHivBtRtfibYw2ovvGXiay0vhVvb6e5Ubcfwqe/tvrAvvD41DxNGNT8V6rq2kRpte5kdYZ3B5ZVLySYB9SO3SvSw/DuEoz5uWNv8O/roeJjOJ8Q0o18RJaXVpKTT9OZW9fw7+369pfgP4X+G4be98X3UWtwxbZYdMvA0l1MFwztAAdqk9NwXgda4sfGa/sfDv2DwL4aksY5F2yavfSNLcTO33mEcefm498e1VpfhrpvmWmraN4E1GXw/bxb1ZIJ7prg4/1kkwTBX/ZTaPeoo9TvtE8RaZY3s9x8MCwaYapeCZbe4ViSECIgiXAwdrkAdz0Fcn1XLcrm68oxUtldqyb/up2j6a9vQef5hnEFhqUalXVOSSesV15pJuWj0b5bb2b31fAPws8UeL79L/xFreteF9B0u3a8S9vtPS1Kz56IjMc/Lk7zz0AA4rI17Q9HtbPztN8d6xrfia5ZytlpcCsYox3kdBgkgdB+OK6TUvDOs+JvEtvZz65Y+PluJvNjuDcytGqDI+4rCNR3JyRXfab8LfHfg/QdWOlapoGk212rBmt9PMksy4PygsAAv0AFepHGV6f7+U172ysve7t6bfn958xjqn1+aw9DDOChp8UpOFuiu1FP00XbZHn+g/s7fFHXtJm1LVfECaNZlfkGoxiS6YZ44Xp+LA89K1dH/Yx8U6pcIl/4ktdLESs51C1gnkuJ2YcBg8m0Dp933rpvG3xs8bfC06VbeKG8O+I1llX7PZWDTQ31yc42+XtbOD6KRz1rH1j4jftC+JtcuJdGsh4dtpGX7Np39nwSokZAwWnlYEv1yAvHoOleRiM8xUp2SU3HRJcl4/e0l5fgexg8jy6FJSxClDmV25xqe90snCEr+dvmc6fgyvw1uppvFekXmpiA7Rr9jILlVGePNhcFl6j5lyK9n8A6V8K/EnlmLWLfxFOVAVbu8YSR+3lHaQP+A1xt94X+OtzbibXvF+kWcjKC8aWsTui5ABYLFt6n+8a5fx1+zP458VXhlv/ABho1/KxyLiOyigP4mOHNXPMsZVp6yfN6pP7/wDgnBDAUMPVfJDmhf7UX+HX74X8z074uaf4O+Feg3V/Fe3OJnb7NpNvdYR5cDCgDnGeSc8D8K88+HnwTstSsX17x/L5H2ueN10rLxvKWdQDM3VQATtjBBHGeeKseBP2VpNS1KRvHevSmys4VW1OmX7hmbcSSGZAyjjooB561veLvgD8DfDWlzXGoaZ9pnG0G71bWLgfKWG770o7Z6DPpXj1HjHJRw8FJvdttfkmfTYOlgsc3iMznKEY6RhGKenVyvKO/Rdt9zuNS/Z08CWNsv8AZD6xpTzD91HpeqzDd9Nxbiqdv8F/B/gKGfxH471q61W3sVCW0/iO982O34z8gwAzZ6cE5HArwnT/AAvqV9q0UfwH1PxkIYSVmvri92aQnJ4jWZS0nTGMZ9q6TX/hP8TLrXbTxP4g8U+EfGM1qny2viVZ4be1YdWEcYWMH3Ix7V0uUsJFyx6cWui96T9NrL/FZ+TNI5Tg8VVist5ZJ9ZJwiu190325brzRoak/ib4pavBrXw20P8AsbR42iil1fVWMceotvGwpEecA4+b09D1PGfjj4u+CPD9/qWq+DNFP2SMxHWLS6KpFnChpI2kOcEj0Gahtfj58VrjR2XSPAmjazbWt1EGvrC9ZLdijLhYxJtLZIHTt+dW9Y/a006TTtQ0vx34F1nw3cXVu0U9le22YbrjgAsBgN0zyO+ayp8S4KVaLnSThprJS2/xbO33HqVeD8yp0JqlU9+70jKHxf4N1ffv1Zwn7NPxy8X+Ivi9pPh3Xr1dZ03U/NV4/KRTEyxu4dTGB3TkcjBPFfamoaTaWs1hLFFtdblMHcT6+9fHv7K3xc+HF18WoNGsPAMfhzWtQSVLLUlvzdqMKzGPnhMqpGV69O9fZ2sf8uX/AF9J/WurFZpgM3qe3y5WgtHpbX09Gjz6WS5nkUfqua/xHqtb6Pz9UzQooorjNgooooAKKKKACiiigAooooAKKKKAOE+KGsapDc+FtC0nUptFuNe1I2kmpW0Mck1vEkEszGMSo8YY+UFy6MMMeM4Ih+G+t6r8QPhtKLzU5tP1iK5vNMk1WwiiEm+C4khE6pIjxhmCBiChXJPGK6Dxp4Lg8aWNpE1/e6Re2Vwt3Z6lp5j8+2lCsu5RIjxtlXdSHRhhjxnBDfDHge18I6TZ6Zpt5eR2NvFKjROY2M8sj73nkYpuMhYueCFy7fL0xWlgPH/7U8f2fgHxXqukeJPEvi/7TqsGm6JssNOa9jiS4WG5uE228UPJ83aZRsVUVicE4taX4y1rxQdC8J6b4r8R6Xq7y6gdVvtXstOOp2r26xEW7CKE2uD58bblRtydGycj06H4ewaf4E07wvpmr6ppMenwxRW2o2cyC6Qx4wx3IY3JxyroyHJytYc3wRsW0u1jg8Q65Z63DdTXb+IoZLc308kyhJS4aEw4ZAq4EQChF27SAad0Bs/C3xfceMvhnoHiDUligu7qzWW68oERiQDDlcnIXIJGe1H9vad/wnQf7ZDt/s3G7d382tzw34esPCegadoulw/Z9OsIEtreLcW2ooAAJPJPHU8mqX/M+f8AcM/9q1IF/wD4SDTf+f2H/vqj/hINN/5/Yf8AvqtCikB4F8fri01DXtFuV8U32gWtpp95JPPpMoSaUbogIg3Ysa+Gtc1M6xq1zdySz3FnYqz+ZcymV3boCzH7xztGfqRivsf9r7ULCTVfDuh3c/lT6la3McMifegYFCHPP3SQB+Br5G8Z6J/wj+jWmgQbJr3Y2pam0R3iFA3lQRkgcHkk+8q1rGM6LVT7crKmvN6c336el32OmWMq4ylHLKcmqUbzqtaWjH3uW61u1dq/XlS6jPhV4Ot9avp9e1hlTT9PUS7WwBPMMHBz/CmQT6sUHrj0HwPpmheLNV1TxB4smaTTYmNvaWaOyve3G3Oxcc7EXbu6YDLkjms/wr4Vvr3wFPqd4ywWyKttptmzbRNNyTK4/uR5aQ8fM7DPDYr0T4V+B/7N1DTo/PsDJZRf8fGon9xCSS5BX+Ji3zbcnkKGOFwPdwOWKPM27U49e9tkvzb7tv4mXm2eQy+l7eTvmE3aMb39lzfFJ2+1a0Vr7sbxTtBs19P8CXV9ZWrrqGn/AA+8LwHzPtJjV7y4b+9HF3bGQGb7oIKg8sWeG9Uu/g94qXxdO+oeIfAqyzWsEupytPe6cHbJuSMDKuxcnAz8x/4F6/a/8IfY3g1PXPElvruqf8/F1Osm3/djUlUHsPzrotW+Jng6bwzdSHVBeW+PLaxSMySSk9ljxnB9enrW9fHVKdJr2bVJaN2srdbN63/vP/wFXPiMPg44jEKVSqpV5O6XNzNy6c2tv+3Y7fzOxqrr+i60mhalDrVvqME84kjmjkBjZTFIRj8cVw/xO/aBsLXVLPQfCkK+JNceTcEi5hRh03HI3Y6nBAA6sK8Uk8N69JZy2XhLUYfC/hjWL77R/Y97qAP2FSrB5Ng+ZFPzfu8jJKjpg19BfBjwH4S+G8N4dO1C2vdQn2xy6jPMhmnA5PQ/KmeijgY7nmvknF1W3Rk40+jfxv5a8vq9X0VtT7u0MLGP1iKnVe6XwR9Wrc3pHRdZX0OXmj+K2n6ctyvijQ9bmlXzJtNuUUCNyc7UYBcgcfxKPbvXCeIf2jfFPhuxOn6l4KQ6jFIGtp1vDHCTnlWDL93PPDn0yOtfXMcqTKGjdXX+8pyK4vxBZrq1zFdtbi4txeRRrHj7+GHP0JrGjTnhZc9KV12leS++6l/5NbyOiVejjoOOKpK66wtB+mzh/wCS303PCfh/N4G1DUoPFfxJ8YWvibxJnzLew+zyjT9NOchYo9uGYcfMw7DuNx930v4seHvFTSQ6Dqtvdyx/6x33IIx64YAn+VLqOhrHOyw20KyzAu9rDGMADv8AWuQ+JHh7wxpulweJ49WXwre2a+Wuqw/LIrdldB/rAf7vX0oxGJzCo/a1HGSW0UnGy8tWl+vVkU8JlFOPJRcqa6zclLX+9aKfzvp0T2O21K40iLTmaS9iuZ2li3SO3J/eL09BVjXvHnhPw/a+bq+s6bZwgcfaJVGfoDyfoK+d7r4teL/iFDpmma1fr8PdEmQSHWTC8MupbXABhZwBEMfN1z6dQKmjk+D3hK6kuIFk8W6ovzPqWpB9QkbH8QBHl59+KJY3DU1+9qJPtdXT7ep5VLA5jjJcuAw7lF7TafK13Wl2vPS52OqftEeEryQxeGdG1jxC+7m406wKxYHX5nK4HvivL7jXr/xxqF5deHdBh8HWQjaO/vH1QKlyf9mNFPzDnJXPXqM80de/aK8I+LL7+zNR8WN4T8NKv+lrbq811dgHiFVhDBF9c8fyq9J+05pqaE2m/D3wP4g1yzijMcU8mmmOBB6jbvJ9cnack1z4XOqcqkoU5Ll83fm8uXt5tavbue7iOCsZ7OE8S5c6s/dShGPnzyvr5Rlot30NbTfhr4pg1ZtQf4haRp1ywAFysD3j7fTMqgn9K2bnwP4gv7kR3fxhke3wDusNMS3c+oGxuPzrirj4yftDa9JLHpHw6utHgZspK2lEyHPcmdwv6Vn/APCK/tI+L2I1Wy1yKN+rx+ILTToz7GOFScVviMyp1ptvmfpGX4WVkvuOnBcGYbD008TiacetnVu9ddtE33u7s9A1n4R+CotPlutf8T+INbZY2O7V9VWKFmA9xn/x6vNdV8WL8PbiK2+HXjy98SHgPoAga9hiX0EuNmP93061pQfsp+PZI576/wBJ8GRyiNmebVL+/v7jgE9AVTNelaF+zj44j0+KB/iVbaFYsoJsfDvh6CBV47SOzN+lYrF1F/BpS+dl+EmdtTJ+HKKfPVUpd1eP4wU5fc4+p53N8cPEs0sUWs/CbS9SuVQMzWtuRM3+0HAcD6c1Rt/j9FqmsNZ64E+HFpj/AFkOkG4kI/3sZB9wteq3H7KtzJeHHxI8ctcY3LcnV9qD22BMYz2qvrPwJ+Kdjp3k2PxTbWbGLkWOt6RBdHp/fOG/L8qVTFYqtHkrU5cv9yVvv95fgzXCvJqdX3YU/JyU7rzUmqkde84N92VvAvxg+B3gK3iu015r7XHjUz31xZXM027aMgN5eFHbj9an8W/toeE5IPsnh6C81O6mby1aYCziJP8AelkICD3I/Cs3w3+zn8QNf0+0uNR+INroNo8Sstro3h+2hcAqOj9RXRXH7G+katZyQaz8QPHeqeaMOJNXVY8egj8sjHsc1NOpKlFRw+HaXm0vyu/vNcRPKnP2tWq51POUpryWlOlp5J+hDb+Adb+KVjHL458ZWllojruh8N+G5dtuwOcedP8AelzxkDjPIIrz7xN8NNK+HKW/iLStLg13w2wP9peH5piJY15zLayg7gRyShOPr26Jf2S9X8HJNZeBfil4o8PXJJkigu7rzLWf2IQLtPvhvpWR4R/ZL1nxtNb3HxN8Zap4k2q0i6Wl7IbeMAkKCeMZPZQPXNOpKvW92VBNPu1y/k3+BOG4gWGqK+I9x6OEINLXfrFX/vc3P1uzkdT+OPhfw35T+D/F3iC3+0JvaxNwohi9FYy/dI6cbunWqU37QXiO+s5JD44stMj7fbdbhuSffZChb8MV7xJ+x/8ADzXYrK4062vPDPlx/Z7yDSZggulUjh94Y5yAcjkgnPYje8Nfs0fDDT554IPBml3VpCPLaS+hFy8kh6ndJkjHtiuKOX1Y6rlXledvzt+CNq2KyyScnXqvvHkpXWuzk4ttvvd3302PmPwr8RviL4gY23hPVrnW5AxJnSzhW2JLEk+ZPjqeeQOteX/GaPW9G8aXX/CZXUDa9dRxzu8bxsGUqFX/AFfyrgLjAA6fjX2hcfse/DfXImu7fQ0027S4mEctlNJblMSuBjy2GMAdwa+U/jl+yF8RLL4iXR0HR9S8T6RciP7NfPdC4kGEAKSMzAjaQQCeNuOetZ4vGY/A0ualQT16Ny/C1/xPoeHK+R/2gq0JcrSveo2ne668/J52UF+B9qfs6Tf8I98E/Cdjqt7F9tS1MhBlDbUeRnjXPsjKPbGK9D0C4juobuWJ1kja5kwynIPSuX+BPg3Uvh98IfCvh7WHV9TsLNY59j7gjEltgPfaCFyOPl4rrNH/AOX3/r6f+le/SlKpTjOas2lddj8szCcamMrTi005Sem2728uxoUUUVqcAUUUUAeKfFrx14k0nV/Fdxo2rvptr4Q0SDWXsltonj1J3eYtFMzozBAkGB5RRgz5JIGK9C8aeLp9D8NQz6bbrdazqG2DTrRzw8zKTlv9hFDOxH8KHHOKz/G3wj0vx1qxvbu/1GzjuLdbPUbOzkjEOpW6uXWGbejMFDM/MbIxDsCSDitLxJ8N/Dvja2e18U6TYeKbMTedbW2s2FvcR2h2BcRBo+OhOTlvmPOMAVoB4/oHjrxn408G+BoYvE0mlatc+FptfvtSt7GAm6njMSpGVeNkWJi7FggD4C7WXvBD8ZPE3iLR28a2eotp2m6YdGSbQVtojDd/bEgedndkMmVW4Aj8t1AKfMGzXcWv7OPhvR/Ceg6Bod7qXhyDSbWaxW50o28ctzbzFTPHKDCUPmFVYuqq4IyrLk50774H6Dd6vaXMVzqFjpkX2QzaHbSRizu2tSDbNIGQyAx7V4R1DbFDhgKq6A7y6vYLGMSXEqwoTtDMcDPp+lcx4T13T0/tndeQjOpTEfN2O2uurA8I/wDMa/7Cc3/stZgX/wDhINN/5/Yf++qP+Eg03/n9h/76rQooA89+F+tWMGk6wJLqJC2s3zDLdjO2DUPxA0PQPE9kY7iaKRM7kaNgHib+8p7GtP4U/wDIH1n/ALDd/wD+j2rs3RZFKsNykYINa0qkqUlKLsYVqMK8HCaumfCXizw7r3ww8Qzat4fmt76GXi6tXQPbX8fUiSPpu/n1HOc408/hrULC21bRfDnjDw1qqv5vmaPEZ7QPk8q27Ix04x06V9s674dhulktZ4Y54JQQqyjKuO6H/HscHivFv+FMeIdLkn1zwJqM+lu7OJrW4I2T4YjDKQVfGPvEAj1Fe9OtQxkU6sVzeeif3fk1ZdLbHyccLisBN+wblDto5R9L209H6njEnx01OQpYeJ4rTVbENgT6/pEsDJ+MHKngcgZ96dt1nWJYbrwhdW0idWtrfWorsBf7vlylZ1H1bNezw+ONGurdtI+J/hmbTL4Db/aVvE7qwPXgZbH03rXHa5+zX4U8ZeZqHgfWbHVGU7/Js51juIiO5TI5Hp8pril7GnfmpyV/NO/p8K/8mPawVbERahQxSj/dkuZJ+ampv7os49teg0vShJ4k8J30Mp4N3Zoohb65Hy/99mqljY6Zawz+I7LQbn7LGjA3q3NrCIwRz+9y2D+Ga0rXwD4y8PwySalqHjFdHCECWx1iS0ATpzvRlI9hx71e8L/A74ba0jy6h4q8Rab5x3mCcwyK8gOVZikZVsEn7wGc1vh8dQgrOtJLs0/wvdfjY2xWS1MQnUqWUm178OTl17pRU1bfZ+SOK8N/C3UvHGl3ev8Ah/wnpP2QHdDHqUkYubjvuXdtB/3iFB7ZrS0ux1yLR41v/h5qMezEsN/pNwYHXup2ESIy+o2jPqK+gLfS/iD4fkjn8LfEfw54uSFAEsdYgSCUxj+AtCeTjpuArHj/AGkr/QbG103xf4ZudOWKBF36PcR3ayjaMF8HdGMdjz7VX16clKNNWi3fTR/O1r/cRW4RU7Tji/ayStfmX4RqJNW7L5HH6R+1B4t8E7LfWPDdxq1nEn/Hw1p/Z8yj0YorIcDuFFch45+J3hf44awNngvT7S/Yqf7Xup5mnibs5a2j34HHUkV7ZbfEzRPGkJuG8ZW+g2EmGGnWpPm9P4i2GB69BXNeIdB+Dt5bzC+jS/uDlvt0cc63AbHB8xAP1rlWDpYyLhUotvppf8H/AJnkxxOLy6p7uO5UtHqk7dubcpeA/hT8NND/AOJt4r1l9du/LUzvcLOYo/8AZG4bn545JB7LUNzq+heLPFKTzeG4NK8CaY2+HQdNhhgNw4+5LchcbhwTs6DgZ+9u4bS/Deha/fzJ/wAJ9e+GtNhI+zw3wN4+7GN3IC469W4zgZ5NegJ4TvdetV0aH4jeCL6zmAX7UdJi+0gD1jdtg+vJrSFPL8O3T9rByjpZ3Sj32W/lfTrcqph80x1NVVRmqU/evu6j6czk78u1rrVdlY7SX4taX4it7i1m1M6Rp5jbydORWBkIHG9wMBf9kH/Gql7JpXxCtJLHR7DS9C0tY9lzqesBX8w45EcchOQSOp4+lJpf7G/ga30y5vZ9WuvEN2IXZz5yRwZwT8qRYwPqTWR47/ZV+HWj2H2yHxVJ4Z1EIGWK4uI5oTx0McnzH/vquiVXKoRbjKzel5ax/R/en6BChntRxhyJ21tF2k/Lr+FvVE0nw9+GFvDbyWPxK1DwlfQwCFv7K1WKJCwPJZCpUg4HFcL8QPi3rHgGzlTSvjPb+M52Rre20640aGeQIw2lmnjZVU/73PsawZvBeg6pdR6dqfijQRpsZzJNoOhut7OAOhcoyR/VST6jpXrWh6t8JvDfhufTtJ8B/aZBC6i7k0ozzMxX7zSyAt1564HYCvmcRUy2MnUrYjnqP+WTWnna33Jbdj7nAYPP1T9nQwqjTWtpQT1eu8k7+vfucz8C/iJ8NvD+oHxFrWv3F/481BWS91fxLblGjJPSFhujjj56K3T2wB79rvxe+GngvSWvtQ8S6ZPuTIW3mE8spwT8iJk8+o46ZNeBeLPDPhfxdcl9Y0vw38O9NL7sTSQwXko9NmQqfQjPPeucl0j9nrw/cBZtTkuJI+C8TXUwb/gUSEflW2Dq5ZOm5KM4Rj3t733+982mGLyviWpO7pxrTe3JzPl+6PKkuii7L8DoNe/aW1bxTJcW/h20Xw5pkhw9/qURnnkTOQI4cbR2++1Sw+KvHHiLiHxr4ku8/wANp4fsrMfgwc/nWSPFPwYjsZP7E8A6vrsuPlmg0++myc/9NCBVi48Jt4ucPoH7O+sIrdDql7/ZAA9SHkJ/CvOq5tCUv9nqKK7Rp8z+9834WXkbUskzClH/AGrCxb/6eV3D8ow/G782T3HgHxFqSmbVrzxNd2/8R1HxNBZwD6ooz+tYFx4Z8JaDcb2i8DQSLIpZrzUZr+Zm3DkgsVx68etL4n+CniLw3ajU9f03wT4N07GA2pale388X+6obYx9sEnsKreBfgP44+IAa8W/h0XwtvVYtSbQ7exnuFLhcxxoPMZec7ncAj8qqOKxlfR1qrT/AMMF9ys/wPZwuTP2MsTKjhaMV9pupVfybTTfknfrY9d03xZJNp6tD8RPAljbxrgRg7AgHYAsKyrHxd4H8S60o8WfETTdetbaQD+zIUktLZmHIdncASfQHH61zGo/sm+JvhxqMt9BaeHfiNp8h5XWDc2VwnspSVkz7tkfTpUlp4RsdMZn1P8AZ01tpV5dbbVkuLf/AIDJvww+gFcFsIm44j2no+aSf3XTPbjVqU4R+ryhWvvKDp0XH0jNSm/W8T3fxP8AFT4faRo9vLYa3oj2sBjb7LZyRsNodTgIn8gK+ffj18TL/wAWfCvXJrW1tdG8Ko0eDqjhbrVJRKhCQx54UYDE+i/UVm+JfCdtrisfDPwK8QaPqUjpFBO+obYEkZgBuIcgDn/64rqIv2I9c1vwTJP4i157/wAWrCzWkMlwXtbd858sFgzEHoWyOvTjJ75V6eKpOlhlq01dx0Xydr+n3nk0shwlLGQxGOxzhS5k+VJczd9byjUlZL+b3XrdHz/+yaiyftHeDr66tNtvvuGxsEcceLaUhuPQ4PNfpbqGsWV1NYRQ3UckjXKYVWyT1r5M/Zv/AGY/HXg34sab4g8Q2MWkWGmCVs/ao5WnZo2jCqI2bA+fJLY4GK+v9Y/5cv8Ar6T+tLB4GOAg6cZc13e772X+R1cZVMHLH044GopwjBLRppay0002t+upoUUUV3nwQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFYH/M+f9wz/wBq1uyMY42ZUaQqCQi4y3sMkD8zXJ/2lcf8J0G/sq7z/ZuNu6HP+t6/6ygDrqKz/wC07n/oE3n/AH3D/wDHKP7Tuf8AoE3n/fcP/wAcoA+bv2zLwWOoeFZPssd/N5Vz9ntNuZJJcx7cHooBILE8AA+1eK211qGraXCdVNtZa/dO0iyRqCZYEPIyc5O8k98bfXNem/tzeIm0630COKxktNW1KGaxt5ZWQMoLxFmyrHou7r0L5FeBeF7OXxNrUEkM8kFlo1sk0bhgrJEhVEH+87NvYDrvc+teplM6tfFOFOT/AHa8lFN/i2/VWutOpeLy/DYfLpZhiYK05duaU+W2mvuxirN/C78srtXSPS/hL4di+JOsapqFxdzWPh/SLfz7hkXa8uchAc/dLkNtX0BJ7Z9c0PR/Cnh+w0+bUrS31K7nLO1rChmkUN91SM44GOvrXBaT4s8O6J4M0K+1O2Zb/VpPt72GmxhpZoyx2BYlbJZlx8z4yc49KS3uPiH8btQmHhfSbLwho0IeCNQFMkh2kkPKMckddnTODmubF53iMTNSjOyfwpO1/N2383+SPRwHB1KjB+3hGMFZSnUS3W6jpfe9ktFom7noOqatc+Ita/4R/wAI+HdOt70DMv2WJP8ARl/vTzAfKf8AZXnqOtejaL8F9H0rRWOtzSarf7d8tzvaJVPcIqkcfXJ968m8G+JLn4P6adO8WfDG+t7VGw+p6BJ9sVmA5aRQwYfUk1o33xK1/wCKUUmmfCqxvYNHdd13rWoRKEg6ZWBHYeY47gnAPWvKpLFc/tsRK78m7L/P1fySPeqZLTcFSwsIxobuo+Vp+sk2o+UFr0d2UfG2m+EfhzfW/n+JpdE1G+O9beVReIkZDYd4yCQpbAy3HNXPD6397p5/s2Twh4qtFUyebFiGfHcfuztBz2PNbPhvwDoPhVY9RbT7q8lkug+papq00ElxcgROzNKzScAkdOBivFfi38RvgzdaldSaL4evLjXIm2wSeF3W3RZDn5nmRjGM4PQMa9OtmlOlCKq2be10m393vfczxKGS4DFVKipTqwit5q3KuzcW0opbXbem66Honi3xZq3gHS4tRTRrrwnc3LeXHAmoxzLdN6LFyWP04rB8e6h4t1TRdEv/ABjqeoeFPDajI0+KZNOkkf8AvvICxBAJ+UgcntXnfhn4WfG3xlqUOs28kmnTsm20vdVuo5LyCMjAVWkGUOP4lRWOTzzXew/sf391c2t/4vvrrWLySWOKaTEc8rgtziWWQlT7hRXBLEOs06NFJd22vwTb++xvOWHyZKnhsTOrU1blGEWl2UXNRjb++ud9kuuHdaf8MtS0R2sNS1HXdccfLFe+Iv3BPqWRixA9Mc+org7r4bw6RpvnW+gavr2qysJFm03WobO3s2z/AAh1kd8cc9eO1fcfg3wPpXgXR49N0bwzLp1qoxtt/IDMf7zMZMs3ua0X0eO48wT6HLKrccx23P8AvfPzVun7SDTk4vy/+2UiIZ9mVOrGftJTivsylLl9WouF/mfD9vL8WtSsWtLnWLaXTGZG8m/07+05UYMNp3PBgn6nnp3rstE+EvgzWLiJ/iT418Q6yEcv9hltXsbND6LDGrBP+Ala+gfEnhGGwhFzp+l6hpF0XRS1nPGiOCwHK+YQCOvFXbXwFHPGouoNevYzyxuL2Mlz9PMFZxweXud6tKUvNy/Tl5fwNa/EGdThzUK1Om3/AC0kvx5uZevNqYPg7Tfgv4Fjifw3pOj20qYKTral5hjv5jgv+tb3iP4seHp9JuYf7SDmRCojjhf9SwArVs/A+g2T708Iyu3/AE2eKX9GlNWtXt7e10S8EHhtoB5TY8tLdccez12pYeOkItL5f5HzlWtjcQ+bEVOeXd3f5t/mUbf4yeF/s4ebUGX3W2lb+SmmL8dPBDOVOt7G/wBu1mH/ALJW9fWdvqUJjuPDckq443LbnH/j9c1c+DbKIM0Ok6ppkn/PWxnjAP8AvKZCv6VpCWHXxxb9Gl/7azlqRxTt7OcV6xb/APbkXLz4s+ELzTbpItftCzQuFVmKkkqeOQK0bH4ieFpLeIDxHpYYIBta8jB6e5ri77w/q8dpPjSrHWbURtua8t4IplGOoKOcn6iqK+AH1lTJf6JZ6dZovzLp9tbs5/7aO2c/QVq3g7bSXzX+SMEswva8H8pL9Wehf8J1ou8T/wBt6d5Gduz7XHnHr1ovPiT4Ut43D+ItMJweEukY/oa4z/hV+lXNkUg8N3iRfwvJcR+Yff8A1m0flWfF4ZuPCvB8NaZfWv8Adv7W2jlUe0ik5+prGM8JvLmt8jpnTx2ihyX+Z0dn8afCOmaTZRSasjSRwIrLGrNghRkcA0wfHXRZvmtra9uY/wC9HayEH81FZVl4qvNP020Gm6PoenqsSL5011CWPA+bAZcE9cGn2+u3d5MRN4ujjnk6w2b2Y/UzZNW8TgI6KLfrJf5ERweaT1c0l5Qf5uX6F2++MWh6hb7WsdXilU7kdbFztNVfCvxi8L6XpijUru4tbpyTI0tnLjrwMhTxj+daqeFb28USSaj4gmVv4oZ7ZQfxD1jf2prXg3S4LlpY7jS2BxBrMsCEcnIWTfz+PHtVOtg0ryptL/F/nExWFx8prkqpv/A/0ki7/wALl8J2k1+LbW4WWcCSLcrrhzweoH1/CtOx+LPgrTbOOH+37dioyxVXOWPU8LXFL8VPCuqQNdz6aDMjciFIZYY8di24Zz9K6DT/ABN/wlmnifwr4Vtprc8G5leFAD6YB/rWNPEYCo/dUvL3lt/4CejiKGZ04JScVbSXuy36fa7Gponxa8Irasj65bxsZ5mHmBl4aViDyPQiud8WftIaToOrfY9Nsf7bhUAyXUdwETJGcL8p3Y/DnipbXTPFN5Zkf8I7o0yebKD5qLkHzG3AHfyM5xntXm/i/QnsPEVyb7SrbT7lwrGKBB5eNoGVxxg47d813UKmAg+arTlLyv8A5JM8bE0szqRUaNWMfPld/ubaPpDw3r1v4o0Gy1W1DLBdRiRVf7y+oPuDkfhT9H/5ff8Ar6f+lc98PmuNK8GaVajR7pQkWRsMQGCSQcM4PfuK3dAkaaG7d4ngY3MmY5CpYdOuCR+teZU5ed8m3T0PYpc6hFVPisr279TUoooqDQKKKKACiiigAooooAKwPCP/ADGv+wnN/wCy1sXVw9vGGjtpbo5xsiKAj3+ZgP171zHhPUbhf7Zxpd22dSmPDQ8fd4/1lAHXUVn/ANp3P/QJvP8AvuH/AOOUf2nc/wDQJvP++4f/AI5QBzXwp/5A+s/9hu//APR7V2tee/C+/nj0nWAum3UoOs3xyrRcfv245cciuy/tO5/6BN5/33D/APHKALV3areQNG/fofQ+tYWgzalDpqr9nWUCWUbtwyf3je9af9p3P/QJvP8AvuH/AOOVn6HqVx/Z4/4lV4f3sv8AHD/z0b/ppVKVtCJQu73sVNc0ODXIWjv9JE0TDDRsNyn8K8j8Vfs8+HNclNxpkNzpd2h3ArukQH2O7cv4N+Fe7/2nc/8AQJvP++4f/jlV7iR7htx0e9STtIjwhh/5ErenWdPb/NfczkxGFWIjaev4P5NWZ806d8DPF9xp8A/4Sm++yNwkcup3PT2QEcUtj+yDaXUBu11OzUMSZZE81mz33bnHNfQ3hmaW30uB/wCyrySRgcyM8Oep/wCmlSXNjHdXXnPo18C3LoskIVz2LDzK1ljJxVqUYr/t1fqmcNPJcJNuWIvL5v8AGzR4Npf7K+i3AbFxNdKpwzxxYX8CxOawdZ/Zd1k6fFqPh/UbCBJIlcW97p5PUA8yI5/9Ar6ojv5okCJo92iKMBVaAAf+RKo+H9SuBoOnf8Sm8P8Ao0f8cP8AdH/TSsamKxVSPK6rX+G0fySO/DZdluEqc9PDQf8AiXN/6Vc+No/hn8QNHmT+1/Blz4j0lT+9n8LapD5h9gjoJB+VM1GT4eaLeC6luviD8MdTUYz4k0g3dvn2BDEjPfIr7LurOO7l81tEvY5f78UkKn9JKqXlnerZzrDFqqqUYFZXgkXp6GSvJ5cZFt+3lK/8zcvza/M+6w2bYKgkqeEhTX/TuKgn6pxnF/8AgJ8q+Gf2ptVsbq00aPTLD4sWCosSSaPpFzb3AwMbSGhKEj/ZHPrVDxl+0JpkOtSWNz8HIbW+fqmrSQ6d5X+8GiLH8xX1foekyWmjWJt7HUIrhYVIlVoMH5Rxt8zpVu5utWvITb3fhr7cv96VoCh+ql6iphZYmFsRJX8kl/6VzHq087y2FTnhhLelRwfraKjH5JI+N7fTdQ8cQvNp0Pwx0FtpYRy+JLh7heP4UiYAn6jFZcOg+LLrWm0fwhceH/Fepx48w6LozXdvAx/56XFw2wf72TX1T4k+FGh65p93Lf8AgbS45BE7eZBp9oH6H+PcG/Wu38J6fb+FdAtNO0vw7NZWkSDEduIEXJHJwJOprkhkuChLns2/u/JI76nFlOlH90nLtGWsV6t3b9Fb1PDtB/Z6+KWr6PEuv/ExdFl6mz0fTY8IPTzE8vn8CPeptY/ZDt7jR7uTWviB4r1jyoXk8uS7AjJCk/dYPxX0F/adz/0Cbz/vuH/45Wf4g1K4Og6j/wASm8H+jSfxw/3T/wBNK9hU4RVuU+VlxHmPM3SlGH+GEF+Kjf73c868Nfsi/C/w3sYeHzqcy/8ALTULh5M/VAQh/wC+a7W4+DvgS6sfscng3Qfs/ZF06Fce4IXIPuK6D+07n/oE3n/fcP8A8co/tO5/6BN5/wB9w/8AxytDza2bZhiJc9WvNv8AxP8ADXQ8U8Ufsx+GNDjuL/w42seHg2C66LqUsTA5HIDFgR7YqxH+zvqlwwK/En4hWsf/AE28RF2/IRivU9e1K4OkXP8AxKrwfKP44fUf9NK0P7Tuf+gTef8AfcP/AMcquZnYs/zG1pVHJ93q/wAbnnPhr9nLw9pOtQ6vrWpaz421K3GLabxNefaxb85yikAZz65x2r0bXv8AkHH/AK6xf+jFo/tO5/6BN5/33D/8crP1zUrj+zz/AMSq8H72L+OH/nov/TSle55WKxmIxklLETcmtvL0Wy+RvOiyIyOoZWGCrDINZUPhizjkJbzJowcpDI5KJ9B/jU/9p3P/AECbz/vuH/45R/adz/0Cbz/vuH/45UOKlujmjOUdExusIsdnbqihVFzBhVGAP3q1pVz+talcfZoP+JVeD/SoP44f+ei/9NK0P7Tuf+gTef8AfcP/AMcqiDQrP1j/AJcv+vpP60f2nc/9Am8/77h/+OVT1C+mmmsEfT7mBTcp+8kaIqOv91yf0oA3KKKKACiiigAooooAKKKKACiiigAooooAKKxvFXi/S/BemLf6tNLHC8iwxR21tLczzSN0SOGJWkkbgnaik4BPQGn6f4q0jVPDaeILbUIW0ZoDcfbGbYixgEsW3Y27cHIbBBBBwRQBrUVw1r8afCV14e1LWlvL6Ky07y/tEdxpN3Dc4kIETJbvEJZFcnCsiMGOdpODUt58X/DWneH7TWLttWtbe7maCC2n0K+S9kZQSxFoYfP2gAsW2bQBnOOadmB2lYH/ADPv/cM/9q1rabqVrrGn21/Y3Ed3ZXUazQXELBkkRhlWUjqCCDWT/wAz7/3DP/atIDfooooA+Lf+CgGnq/iLwZdgeZP9nuI0XJO0AqScbu4PXHbqeMeJ6KT4c8K68Dua5mlS1fcMOsiphgP+BSv/AN+6+pv2sNPg1rxl4IsbmHdZLHc3d7K0fAt4tsjgN2JVGH4+9fJul6impatpcWryukc10bi5kiX590jM549VUuR78da9Z46jluVVaiSTe+3vPp91kd2Q4PEcQZ5ToVruhhle2rW6lJb/AGvhSVl5Xu39C/Bn4Tr4qnez05UsdEt40i1PV4juluWCjfFCxzhSepHAUD6N7pq3i3wl8GvDulXmqXVvoulLJMLeBQS8mFwEjQZZ2PHTJycmuf034reG/B/hewtLnR9U8G/ZrQJDb3lk+ydNufkcA7mPXLYJJOeaxvg34du/iR4vsPij4phWORhJa+HtHdg40y1VT+9cZIE8h+Y9wMDvhfhsPWUn+7kpVpbvpFemjSXRaNvX0+uzSNfE1HXxqlGhDZX1nJ7a6pt2eusYpWXRNdSm+InxouhLZwXPwv8ABd06xm9uow2r3a9VdY+luD0yx3DrSr+yD4Ht44IHOtSalM53z/2vPkqD8zthsEnr0716n40+J3hLQLOWDUdbsxKwx5McnmOvuVXJH5V5NJ8VL34q+IIfDHhDU10hLtMXmvXTBJkiBwY7ZCcl29e3t1rrUcHKoqdeanPza/BdPkvW54lbFZrhsLKeDpSp0u0E9e95bvpdt2V+iOA8efAvwRe+Lrbwh4OtNT8S62koa+GpavPJp1mArECbB5bIzsBz8uD6V7R8Pv2etA+G0EF7OkWseIiQkNw0CxwWrHqLeFRtjHXnGfcZrtPDngTRvh7puj6Xott5EIu90krHdLO5iky8jdWY+v5VjeMPiNLH4kOh+GrL+3fEMY2iFT+4tSeskz/wgenU10VI4XAxcqcVG+mi1fl3+R5OHp5hnFSP12o6nLqk37kPPXT1k/lbY7/+y7c2qwFPlXow4OfXPrWLrl9Jo8NqLxw9t9pj2zk4I+YcGuTh+D+ravGJfEXjnXri4kJeW3024+zW4JOdqqB90fhTLz4B+DLX7PLNp8+oztPGhlvbuWQkFhkY3Y/Ss/aYqetOko+sv0Sken7LA07qriHL/DC/3OUo/kdbc/EzwzZ5+0a1ZwY/56XCL/M1zms/HzwnZMsFnqS39y/Cx2aNOxPoAgIz9TW3a/CHwVZ48vwvpbY/562yyf8AoWa6DS9B0zQ1ZdO06009W6i1gWMH67QKOXHS0cox+Tf+QufKqeqhOfq4x/SR5VfeM/E2vWpk0zwPqDIzx/6Zrsy2oHzrgCLk4JwPxq9Hr/xK02TMfgW2nj7xw6zEF/DcOK9G17/kHH/rrF/6MWtCn9TqbutK/ly//IsTzKjssNC3m5v8pr8Ejy//AIWZ423GMfDK7Mw4I/tSLb+e2q+seMviJdaVdD/hXsNnE0Z3PNrMTkD1wAK9YrP8Qf8AIEvf+uTfyqvqtbrXl90P/kSfr2GXw4SH31H/AO3nA/8ACTfFJung3Sk/3tSB/lS/8JH8U4/mfwhpEy/3I9S2t+Z4r02ij6pP/n/P/wAl/wDkRf2hS/6Baf8A5P8A/JnkWreNfGyWNyLv4aSIPLYNPbatEwHB5xtotdK+JXjC3tzJPpvgzTgo2Rxr9suunU5+TP6ivUtX/wCQTe/9cH/9BNTWv/HrD/uL/Kk8HKelStJrtovxik/xKWZU6fvUcNCMu/vS/CcpL8DzeP4BaFfZl17UNY8R3jfenvr6QY9lVCMD25qRv2evAUEMhi0ICTafme6nb+b16TTJf9W/0NUsvwi19lFvzSb+96mcs4zCWnt5Jdk2l9ysjzrw/wDBLwUdHspJvD9rNJJEjtvLEZKg9zWpJ8GfA8sJiPhjTwp7rFtb8xzXTaF/yA9P/wCveP8A9BFXq0+p4b/n1H7kY/2ljt/bz/8AAn/meX3P7O3hJZDLpsN1pcp5zbXkq/8As3FL4L+Cfhi1t4dQ1Cx/tjUGH+u1CVp9oBOAAxx+len1n6B/yB7b/dP8zWccvwkZcypL7jeWcZhODhKvJp+bv9+5Etktjbmwhto/szjaiBAEAPUEdK4jXPg/pVpqCapo1xd+GrxvlkutKk8sZ7Fo/usueoxXplNkjWWNkcblYYIreth6dePLOO23l6djkw2MrYWblTk1ffz9Vs/meXado/xCtdPZofFmkSQrNMPMutNKsxEjAsdr45OTgetfPPxi0vxlceNp5dXWfUJJkRYbiygkWCRAo4Qduc5Hrk96+u/DukxLZs0rNNtuJwiv91QJn7Vu14+MylYymoe0lHW+7l+bPpMt4heW1nV9jCd1bSKj+KRy3wstdUsfh5oMGsiQalHaqJVmPzr12hvcLgHPpW1o/wDy+/8AX0/9K0Kz9H/5ff8Ar6f+le5Sp+ypxpp3skvuPla9V16s6rVuZt2W2ruaFFFFamAUUUUAFFFctN8TfDdv4wTwxJqDDV2ZY9otpjAsrJvWFpwnlLKU+YRFw5GCFwaAOpormvGfxG8P/D9bU63eSwNc72jjt7Sa6fYgBeVliRmWNMjdIwCLkZIyKg1D4qeF9M8Q2Wiz6p/pt4IjG0VvLJbr5pIhEk6qYojIRhA7KXP3QadmB1lYHhH/AJjX/YTm/wDZa36wPCP/ADGv+wnN/wCy0gN+iiigDivhT/yB9Z/7Dd//AOj2rta4r4U/8gfWf+w3f/8Ao9q7WgArP0H/AJBw/wCusv8A6MatCs/Qf+QcP+usv/oxqANCiiigDP0D/kD23+6f5mtCs/QP+QPbf7p/ma0KACs/w9/yAdN/69o//QRWhWf4e/5AOm/9e0f/AKCKANCorr/j1m/3G/lUtRXX/HrN/uN/KgCHSP8AkE2X/XBP/QRVuqmkf8gmy/64J/6CKt0AVNX/AOQTe/8AXB//AEE1Na/8esP+4v8AKodX/wCQTe/9cH/9BNTWv/HrD/uL/KgCWs/xD/yAdS/69pP/AEE1oVn+If8AkA6l/wBe0n/oJoA0KKKKAM/X/wDkD3P+6P5itCs/X/8AkD3P+6P5itCgArP17/kHH/rrF/6MWtCs/Xv+Qcf+usX/AKMWgDQooooAztc/49YP+vqD/wBGrWjWdrn/AB6wf9fUH/o1a0aACs/WP+XL/r6T+taFZ+sf8uX/AF9J/WgDQooooAKKKKACiiigAooooAKKKKACiiigDzz4tR3FjfeDNfW0vr7T9F1Zri9h0+1e6mEb200IkWGNWkfa8i8IpOCTjANcdpvhXWrr4A+IPDcdpeWOv63Fq2oWVu0BUwrNcySxRu7KY45CJU+RznJbghWx7pRVXA+bodFj8QaN4ogvbf4hSeABPptwiz/2gdVF3HIZLhoVm/0ryAVgyIR1D+UO9S6K2s+G7zQ/E99Z+KNV8LWlzqdrp8N1Y3V7qkNtMkPkNNEytcnLxTKDIN6rIm/HJr6Mop8wHEfBvw7qPhX4S+GdJv4VtNTtrBFkgYhhC5GdhwcHbnbwcccVZ2an/wAJ0P31pv8A7N6+U2Meb/vV11YH/M+/9wz/ANq1AF/Zqv8Az2s/+/L/APxVGzVf+e1n/wB+X/8Aiq0KKAPmf9rLxDqXhuCwdjE/2qxuLRjEhGUkeIMoBY5JwB06Mfw8X/Z58Mf8JB8WPD+nXdjDK8Fvca7JJcxlk8sOI1BPGR5iDjOMoexr1n9sa8W48WeC9LWKSSZ4riYEKdoAKd+5wDx649RXmf8Aa11oOu/Ea20Ng+uXkWleAtECDnzJI83L8DjaQzZ7Eivms1pTpyp1ql+Ry2f2uVc1ku10vNydtkfqGQ4rly+pgaNlUnHmbW8VKXs4uT7/ABO2yjG/W56zJdal+0x4+tWjnj/4Vv4fv9sREbLFrd5H95sZ+aGPkdcMSevbO1n4c2nij4233hjRETTtNsbZJNT/ALP3xxmeQFkjwWIA2jPAHcdq9o0ux0j4O+C4LVPk0jwto+CehcquSef4nOT7lq5/4B+H7qx8N2PiDVFxrfie7uNYuyR0EikxqPYJggdtxFepDAU6lPmxMVKb1bt17LyS0PzvEZxXhjI4fL5uFNX0Ta91bN2t70pO7+aWiRZ0f9nHwrpMa79G06/l/ia+E04P/AWkK/pW9rfwp0nXNHTTJtH0WK2iBEK29mYTDk5+QowK8+ld5RXV7Cl7P2PKuXtbQyVerGv9aUn7T+a+v37nh114D8V6Jbw2Fj41uobMz7DDdJ5s0HyOcwyE5wRlQD0zntXoPgnwS/gnRUs9OWzi3nzJpXidpZXPVnYtljWvrtjHc32kSMPmW6/MeW5wa2a56ODp0Z86W2i1bt6X2+R34rMa2Jp+zk9Hq7JK787JX+dzP2ar/wA9rP8A78v/APFVn6wmqeXa5ms/+PmL/lk/94f7VdBWfrX+qtf+vqL/ANCFd55IbNV/57Wf/fl//iqNmq/89rP/AL8v/wDFVoUUAc/riap/Z5zNZ/62L/lk/wDz0X/arQ2ar/z2s/8Avy//AMVRr3/IOP8A11i/9GLWhQBn7NV/57Wf/fl//iqz9fTVP7FvczWePKb/AJZP6f71dBWf4g/5Al7/ANcm/lQAbNV/57Wf/fl//iqNmq/89rP/AL8v/wDFVoUUAYmrJqv9lXuZrPHkv/yxf+6f9qprVNV+yw/vrP7g/wCWL+n+9VrV/wDkE3v/AFwf/wBBNTWv/HrD/uL/ACoAqbNV/wCe1n/35f8A+Kpkqar5b/vrPof+WL//ABVadMl/1b/Q0AYmhpqn9i6fiazx9nj/AOWT/wB0f7VXtmq/89rP/vy//wAVTtC/5Aen/wDXvH/6CKvUAZ+zVf8AntZ/9+X/APiqz9BTVP7ItsTWeNp/5ZP6n/aroKz9A/5A9t/un+ZoANmq/wDPaz/78v8A/FUbNV/57Wf/AH5f/wCKrQooA5/Q01T7C+JrP/j5uP8Ali//AD2f/arQ2ar/AM9rP/vy/wD8VRof/Hi//Xzcf+jnrQoAz9mq/wDPaz/78v8A/FVHoHmiG784o0v2mTJjBC9ugJNalZ+j/wDL7/19P/SgDQooooAKKKKACvn/AF7Q9WuPF+peHE0rUmu77xjY+IIdS+xyG0FlEkDOxuAvlqwMLxiMsHOQQpU5r6Aopp2A8c+JOoTWnifTvEkWjazq2mXegX+lRw2mlzyypcyPE0avEE8yNX2MC7qEXaNxAIz57b+BfEPh3w7J4HutL1K+1jVG8PyW9/DavLaRLapbLcF7hVKRGMwOwDsC24bNxNfUtFPmAguhcNGPszRJJnkyqWGPwI9q8/0mLxo15rX9l3Ogpa/2hLxeW87Pu+XP3ZAMV6PWB4R/5jX/AGE5v/ZakDJ8n4i/8/nhf/wEuf8A45R5PxF/5/PC/wD4CXP/AMcrtaKAPH/h1F44bTNU+wXPh9Y/7WvPM+0W07HzPObeRiQfLnOB1x1rq/J+Iv8Az+eF/wDwEuf/AI5R8Kf+QPrP/Ybv/wD0e1drQBxXk/EX/n88L/8AgJc//HKpaPD8QfsI8q78MhPMk+/a3BOd7Z/5aeua9CrP0H/kHD/rrL/6MagDmvJ+Iv8Az+eF/wDwEuf/AI5R5PxF/wCfzwv/AOAlz/8AHK7WigDz3RofiD/ZkHlXfhkR4ON9rcE9T/00q75PxF/5/PC//gJc/wDxyul0D/kD23+6f5mtCgDivJ+Iv/P54X/8BLn/AOOVS0SH4g/2NY+Vd+GRF5CbA9rcFsbRjOJOtehVn+Hv+QDpv/XtH/6CKAOa8n4i/wDP54X/APAS5/8AjlR3EPxE+zy7rzwxt2nOLW5z0/66V3NRXX/HrN/uN/KgDhtNh+If9m2vl3fhkR+Um3da3GcYGM/vKs+T8Rf+fzwv/wCAlz/8crqtI/5BNl/1wT/0EVboA4HUofiH/Zt15l34ZMflPu22txnGDnH7ypbeH4ifZ4tt54Y27RjNrc56f9dK67V/+QTe/wDXB/8A0E1Na/8AHrD/ALi/yoA4/wAn4i/8/nhf/wABLn/45VLW4fiD/Y195t34ZMXkPvCWtwGxtOcZk616FWf4h/5AOpf9e0n/AKCaAOa8n4i/8/nhf/wEuf8A45R5PxF/5/PC/wD4CXP/AMcrtaKAPPdZh+IP9mT+bd+GTHgZ2WtwD1H/AE0q75PxF/5/PC//AICXP/xyul1//kD3P+6P5itCgDivJ+Iv/P54X/8AAS5/+OVS1iH4g/YT5t34ZKeZH9y1uAc71x/y09cV6FWfr3/IOP8A11i/9GLQBzXk/EX/AJ/PC/8A4CXP/wAco8n4i/8AP54X/wDAS5/+OV2tFAHnmrQ/EH7PD5t34ZK/aIcbbW4B3eYuP+WnTOKveT8Rf+fzwv8A+Alz/wDHK6TXP+PWD/r6g/8ARq1o0AcV5PxF/wCfzwv/AOAlz/8AHKbHH4yXUdOOsXGhSWH2ld62NvMkucHGCzkdcdq7es/WP+XL/r6T+tAGhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFYH/M+/8AcM/9q1uyJ5kbJuZdwI3KcEe4rnJfDPm68Jvtd+n+jbPOWUj+PO3OPxoA6Wisb/hGv+orqf8A4Ef/AFqP+Ea/6iup/wDgR/8AWoA8p+NGh2Wr/FDwazRxLdRwTPJMUG7ykIkCk/3SVYfia8J/Yz0GX4mfFTX/ABpcqX0jSb68vbYSDhr67bBYZ/uQovbgsK+s9U8A2uqatbfap7+4Q2s0bStMSVBaP5c44B5477fameD/AIRaB8P9JbTPDgudGsGkaZoLWXapcgAseOTgAfgK5MdRljKuGb0hST07tu6fy/Q9XLMXHLcNjYJXqV3Gz6RilZrzvr95wHxhkfxpqmh+A7ZmDeI9R86+KEgrYW/zPn03EADnquO9ew3ESQahpUcaLHGm9VVRgABOABWJD8MtJt9aGrxzXi6mIPsouvOy6xbtxQHHAzzxVm68NP8A2hY7dQ1J1y+5/Ozs+X1xxmu1u6SPnaFCVOpOpN3crfcun3tv5nS0Vjf8I1/1FdT/APAj/wCtR/wjX/UV1P8A8CP/AK1SdpY1T/j60v8A6+v/AGlJWjXNX/hp/PsNuoalIPP+ZvOzsGx/m6cc4Gfern/CNf8AUV1P/wACP/rUAbNZ+tf6q1/6+ov/AEIVW/4Rr/qK6n/4Ef8A1qp6n4afy4NmoalKfPjyPOzgbhlunagDpaKxv+Ea/wCorqf/AIEf/Wo/4Rr/AKiup/8AgR/9agCzr3/IOP8A11i/9GLWhXNat4af7EdmoalM3mR/J52f41ycY7Dn8Kuf8I1/1FdT/wDAj/61AGzWf4g/5Al7/wBcm/lVX/hGv+orqf8A4Ef/AFqp6x4Zf+y7ry9Q1Kd/LOI/PLbvbAHNAHTUVi/8I1/1FdT/APAj/wCtR/wjX/UV1P8A8CP/AK1AF/V/+QTe/wDXB/8A0E1Na/8AHrD/ALi/yrC1Hwy39n3WzUtTlbymwnn53HB4xjmpbfw0fs8edT1NTtGV+0Yxx06UAbtMl/1b/Q1kf8I1/wBRXU//AAI/+tSSeGf3bf8AE01M8dPtH/1qALuhf8gPT/8Ar3j/APQRV6ub0nwy39l2e/UdShfyU3R+fjadoyMY4xVr/hGv+orqf/gR/wDWoA2qz9A/5A9t/un+Zqr/AMI1/wBRXU//AAI/+tVPSPDL/wBmweZqGpQvjmPzyuOT2xQB01FYv/CNf9RXU/8AwI/+tR/wjX/UV1P/AMCP/rUAWtD/AOPF/wDr5uP/AEc9aFczpXhl/sr79Q1KFvPm+XzyOPMbB6dxg++auf8ACNf9RXU//Aj/AOtQBtVn6P8A8vv/AF9P/Sqv/CNf9RXU/wDwI/8ArVN4fs3sbe5idpXxcOVeY5ZhxznvQBqUUUUAFFFFABRRRQAUUUUAFYHhH/mNf9hOb/2Wti6tftUYTzZYcHO6Jtp+lc/pnhfY16ftuo2++5d8LMVDZx83TvQB09FY3/CNf9RXU/8AwI/+tR/wjX/UV1P/AMCP/rUAYfwp/wCQPrP/AGG7/wD9HtXa1ymg+DY7G1uFju9QtA91NIVSbbuJkb5+nVuue+a0v+Ea/wCorqf/AIEf/WoA2az9B/5Bw/66y/8Aoxqrf8I1/wBRXU//AAI/+tVPSfDT/Yhv1DUoW8yT5POx/G2DjHcc/jQB0tFY3/CNf9RXU/8AwI/+tR/wjX/UV1P/AMCP/rUAWdA/5A9t/un+ZrQrmtH8NP8A2bB5moalC+OY/O245PYirn/CNf8AUV1P/wACP/rUAbNZ/h7/AJAOm/8AXtH/AOgiq3/CN/8AUV1P/wACP/rVT0fw0/8AZFl5moalBJ5CbovO27DtGRgjjFAHS1Fdf8es3+438qy/+Ea/6iup/wDgR/8AWqO48NH7PJjU9SY7ThftGc8fSgDS0j/kE2X/AFwT/wBBFW653TvDTf2fa79S1KJvKXKefjacDjGOKs/8I1/1FdT/APAj/wCtQBe1f/kE3v8A1wf/ANBNTWv/AB6w/wC4v8qwtR8Mt/Z91s1LU5W8psJ5+dxweMY5qW38NH7PHnU9TU7RlftGMcdOlAG7Wf4h/wCQDqX/AF7Sf+gmqv8AwjX/AFFdT/8AAj/61U9Y8Mv/AGRe+XqGpTyeQ+2Lzy287TgYA5zQB01FYv8AwjX/AFFdT/8AAj/61H/CNf8AUV1P/wACP/rUAWtf/wCQPc/7o/mK0K5nV/DL/wBmz+XqGpTPjiPzy2eR2xVz/hGv+orqf/gR/wDWoA2qz9e/5Bx/66xf+jFqr/wjX/UV1P8A8CP/AK1U9W8Mv9iOzUNSmbzI/k88t/GuTjHYc/hQB01FYv8AwjX/AFFdT/8AAj/61H/CNf8AUV1P/wACP/rUAWdc/wCPWD/r6g/9GrWjXM6p4ZfyItmoalMfPiyvnk4G9ct07DnPtVz/AIRr/qK6n/4Ef/WoA2qz9Y/5cv8Ar6T+tVf+Ea/6iup/+BH/ANaoLjQXtbiylS8v7rbcKSksu9QOeSMUAdDRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGH428VReCfCmpa3NbyXa2cW9beEgPM5IVEBPALMQOfWsvwB43vvE8+s6ZrWlQaL4g0eZIry1tLw3cBWRBJG8crRxswKnByi4ZWHIAJt/EfwnJ448E6po0E8dtdXCK1vNMhdI5kdXjZgOoDKufasPwL4X8SaVqmua/rVtpS6xr13B9ps7G9llt7S3ii8tdkrQq0rnliCiD58Z+XLVpYDH0v48eZeNcatoY0zw9d2t9eaVqEd550tzHaE+d5sWxREWUb0CvJuUHJQ8VFD8dr+zsdQj1nwuthrq29ldafp9vqHnJdR3cvkwK8piTy3EnEihXCAgqX6Vl2HwJ1nUFh0PW5tMj8M6TY6lYaZNZySSXFwt4CuZomRVi8qMlcK8m8ndlMYpJPg34s8QQ3mp61Notvr9ra6bbaZFaTSzWzmyuPtAlldokZDK2FKqr+WBkM/Sq90D0j4f8AjWbxfbarBf2Eela1pF61hqFnBcG4iSQIkitHKUQujJIhBKKeSCBiurri/hp4R1Lw6uv6lrf2VNZ17UTqFzb2EzTQW+Io4kjSRkRnwsSksUXJJ4FdpUMArhvAvxUtPH3i3xTpFjZyLaaI0KJqDOCl4X8wMUXHCq0bLuz8xBI4wT1mtaadZ0e+sBd3FgbqB4PtVmwWaHcpG9CQQGGcgkHkV5f8OfhBrvwz8VeItSg8RXHiOxu7Kxs7G01M2tvsWHcp3m3tEC7VbCbQc87uxArWA0dR+MU2n+MJrJtEQ+GrfVINDuNYa9xMl7MiMgWDy8NFmSNC5kDBm+4QM1mWf7QSxRzX+saCdP0O50281bSbq3u/Pmu4LYjzBJGUQROwZWRQ8gIJyVIxRq3wl1vUPFl3biTTR4RvtetvEc83myLeJNCsf+jrF5exlaSFHMhkBALLsP3qxIfgBrWraWnh/WbrTLbQ9J0a/wBG0mezZ5ZZ1udoWWaJkVYzGiBdiu4YknK4xVe6B6H4B+IF/wCJdU1TRtd0aHQtdsIbe7a2tb03cT284fy2EhjjO4GN1ZdvBXgsDmu3rz74d+D9fsfEms+JvE406DVr60tdPS10q4knhSGDzCHLvHGSztKxK7cKABluteg1LAK8v8JfGefxL4yi0qXQ47PS76e/tdOvFvTJcPJZybJ/Pg8tRCCc7SJHyMZCk4r1CvFfCPwn8V+HfHUeqSHRwlvLdy3GtQ3kp1HX1ff9ngvEMIVEi3/KfMl27F2KgJUNWA6Dx78YLjwbrd9BBoa6hpOjWsF9rd8955L20MruimGPy285lEbswZowFAwWJxUUnxmlh8XC2l0RE8LNqh0Nda+25mN6I/Mx9n8vAh4KeZ5m7eMbMfNWb4/+FfiPxVqWsrZnSYNN8Vada6frRluJPNsxEzl3gAixOWSRkG8xbdobnO0Mm+D+tXHiYWEj6YPBa663iISrLJ9tMzRkfZjF5ezZ5jNJ5vmZI+Xy/wCKnoBt/D/4vXHjDWrKzv8ARF0eHV9ObV9GmS8M7XNqrqpMy+WoikxJG2xTIMN9/IIr0mvJfhn8Lde8O65olxrkum/ZPDWjvoelnT5Xd7qJnjJnmVo1ETbYY18tTIMljv6CvWql26AFcZffEcWvxM0vwgmjX0gvLeeZ9UkQxW6NGqsI0LD96xDclflXGCc8V2dcvr3hi71Lxx4X1qF4RbaXHeLMkjEOxlRFXbgEdVOckfjQBj6L8QtdPjq08PeIvDdroq6nbXF3pstrqhu5WWFkDrcR+SixNiRD8jyrnI3dM1PGPxguPCviS8t4tCW80HSWtE1nU2vfLktjcttj8qHy2EoXKs+549qsCu88VS+Hvh7x/pniHVdX8UaP4dvNUu4pEGqWuvXEjKgYmG2jgayRYYufmIdmJ+Y7zjFbxp8K/EXiLXNWhtTpMOg+JjYS6xJJPILm1e3ILCFRFtmEiqiZZotmN2GztqtLga0Hxhnk8ZQ2EmhrH4cudVl0K21cXm6Zr2NGZg0Hl4WImN0D+YWLAAoAc16XXkNr8J9dj8YWkLyaanhKx1+fxJDNFLJ9skmlWT/R2i8vYqiSV38wSEnAXYOTXr1J26AFcb44+I48G614d0xdGvtRfVr6G0e6jQpbWiyMVDvKRtLZ4Ea5Y5yQF+auyrmPHfhi68URaEtrJDGbHV7XUJfOJGY4nywXAPzY6ZwPcUkBy2mfGpdZ+KUnhSztdHkt45ZYG3a7EmqExhg8y2JXLW4dTH5nmbsgkIVG6r3xI+KF/wCDdQNppWgJrslrp8usaj5l79m8mzjYK3l4jfzJj821DsU7Gy68Vz//AAqPXI/GVrHB/ZMHhiHxE/ic36yyf2i0zowNv5Xl7NpLnMpkJKfJs/iq18QPAXi3xBfSajo8ei217qujS6Dqcd3dS7baJ3LLcROISZmQM/7pljDbh8645rQCTVvjl9j1TzrLRlvvC9s9jFqGrG82Swvd7PJ8uDyyJFUSRs5MiFQ3AfGK9UrxTVPgjrCXU+i6VNpy+FNUl0ye/uJ5ZFvITZiJdscYjKyeasEa7mdCnJw/Ar2uk7dACiiipAKKKKACiiigAooooAKKKKACiiigAooooA89+JHxQv8AwbqBtNK0BNdktdPl1jUfMvfs3k2cbBW8vEb+ZMfm2odinY2XXitbxV4+i0LwZa67YWp1V79raGwtvM8oTyXDokQZ8HYuXBZgGIAJAY8Hm/ih8P8AxHrms3F/4ZbSg+qaPLoN+dSlkiMETtuW4j2Rv5jJuk/dNsDbh8645v8AijwDqOo+C7PRNMFlFLoc1hcaTLdTORO1syNtmwmYg2wpuXeQG3YONtVpoBjp8atWvo4dK0/wza3HjRb25s7vSpdVaO0gNuiSSOLkQFmUrLFs/cgkyAEJgmqt9+0QkmkW+taRoDX+jW+kQa5q8txd+RP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      ],
      "metadata": {
        "id": "yJYOnLg0faom"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Una CNN que reconoce lecturas de medidores en comparación con lecturas de otros tipos de medidores de gas y electricidad. Lógica para ejecutar 27 variaciones del modelo de manera consecutiva. Se utilizó Augmentor para complementar los datos de entrenamiento y Pickle para evitar la manipulación de datos."
      ],
      "metadata": {
        "id": "WyomU0vTfnYo"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "4lY8lsYOMJHI"
      },
      "outputs": [],
      "source": [
        "# import data set, standardise and create new samples\n",
        "# un-comment this block to create data set samples\n",
        "\n",
        "# import sys\n",
        "# import Augmentor\n",
        "# folder_path=\"./data/raw-data-energy-meters/no-meter-readings-raw/\"\n",
        "# p = Augmentor.Pipeline(source_directory=folder_path, save_format=\"JPEG\")\n",
        "# p.flip_left_right(0.5)\n",
        "# p.black_and_white(0.1)\n",
        "# p.rotate(0.3, 10, 10)\n",
        "# p.skew(0.4, 0.5)\n",
        "# p.skew_tilt(0.6, 0.8)\n",
        "# p.skew_left_right(0.5, magnitude=0.8)\n",
        "# p.sample(1000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "RbDqIXCNMJHJ"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import cv2\n",
        "import pickle\n",
        "import random\n",
        "import os\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Activation, Flatten, Conv2D, MaxPool2D, Input\n",
        "from tensorflow.keras.callbacks import TensorBoard\n",
        "from sklearn.model_selection import train_test_split\n",
        "import time"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nM8JGgV2MJHJ"
      },
      "source": [
        "Set all variations of the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "UtDxfamAMJHK"
      },
      "outputs": [],
      "source": [
        "dense_layers = [0, 1, 2]\n",
        "layer_sizes = [32, 64, 128]\n",
        "conv_layers = [1, 2, 3]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "S1B8d6QDMJHK"
      },
      "outputs": [],
      "source": [
        "DATADIR = './data/raw-data-energy-meters'\n",
        "CATEGORIES = ['no-meter-readings', 'meter-readings']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        },
        "id": "XuZPwlbqMJHK",
        "outputId": "86f9c260-0939-4404-dade-33b538939a12"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Cargar una imagen de ejemplo\n",
        "image_path = os.path.join(DATADIR, CATEGORIES[0], os.listdir(os.path.join(DATADIR, CATEGORIES[0]))[0])\n",
        "img = cv2.imread(image_path)\n",
        "\n",
        "# Verificar si la imagen se cargó correctamente\n",
        "if img is None:\n",
        "    print(\"No se pudo cargar la imagen\")\n",
        "else:\n",
        "    # Definir img_array\n",
        "    img_array = img\n",
        "\n",
        "    # Redimensionar la imagen\n",
        "    IMG_SIZE = 100\n",
        "    new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))\n",
        "\n",
        "    # Mostrar la imagen redimensionada\n",
        "    plt.imshow(cv2.cvtColor(new_array, cv2.COLOR_BGR2RGB), cmap='gray')\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lCa7U1psMJHK",
        "outputId": "4cf02740-58a8-4302-c150-1b4cdf5347c8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Creando datos de entrenamiento...\n",
            "Número total de imágenes procesadas: 20\n"
          ]
        }
      ],
      "source": [
        "# Función para crear datos de entrenamiento\n",
        "def create_training_data():\n",
        "    training_data = []\n",
        "    for category in CATEGORIES:\n",
        "        path = os.path.join(DATADIR, category)\n",
        "        class_num = CATEGORIES.index(category)\n",
        "        for img in os.listdir(path):\n",
        "            try:\n",
        "                img_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE)\n",
        "                new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))\n",
        "                training_data.append([new_array, class_num])\n",
        "            except Exception as e:\n",
        "                print(f\"Error procesando la imagen {img}: {str(e)}\")\n",
        "    return training_data\n",
        "\n",
        "# Crear y preparar los datos\n",
        "print(\"Creando datos de entrenamiento...\")\n",
        "training_data = create_training_data()\n",
        "print(f\"Número total de imágenes procesadas: {len(training_data)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "3jM6qsVvMJHL"
      },
      "outputs": [],
      "source": [
        "if len(training_data) == 0:\n",
        "    raise ValueError(\"No se pudo cargar ninguna imagen. Verifica la ruta del directorio y el contenido de las carpetas.\")\n",
        "\n",
        "random.shuffle(training_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IJhE13QYMJHL",
        "outputId": "08cdcd00-52d5-48a0-849a-ef171ffcd0dc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "1\n",
            "0\n",
            "1\n",
            "0\n",
            "1\n",
            "0\n",
            "1\n",
            "0\n",
            "1\n"
          ]
        }
      ],
      "source": [
        "for sample in training_data[:10]:\n",
        "    print(sample[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qZTZJIAzMJHL",
        "outputId": "2babfa08-4939-4853-c845-22f5ee40e55a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Forma de X: (20, 100, 100, 1)\n",
            "Forma de y: (20,)\n",
            "Valores únicos en y: [0 1]\n",
            "Preparación de datos completada.\n"
          ]
        }
      ],
      "source": [
        "X = []\n",
        "y = []\n",
        "\n",
        "for features, label in training_data:\n",
        "    X.append(features)\n",
        "    y.append(label)\n",
        "\n",
        "X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)\n",
        "y = np.array(y)\n",
        "\n",
        "print(\"Forma de X:\", X.shape)\n",
        "print(\"Forma de y:\", y.shape)\n",
        "print(\"Valores únicos en y:\", np.unique(y))\n",
        "\n",
        "# Normalizar X\n",
        "X = X / 255.0\n",
        "\n",
        "print(\"Preparación de datos completada.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "9OcY2WC_MJHL"
      },
      "outputs": [],
      "source": [
        "# save data manipulation\n",
        "\n",
        "pickle_out = open(\"04_energy_meters_recognition_cnn/X.pickle\", \"wb\")\n",
        "pickle.dump(X, pickle_out)\n",
        "pickle_out.close()\n",
        "\n",
        "pickle_out = open(\"04_energy_meters_recognition_cnn/y.pickle\", \"wb\")\n",
        "pickle.dump(y, pickle_out)\n",
        "pickle_out.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "lyClBfUDMJHM"
      },
      "outputs": [],
      "source": [
        "pickle_in = open(\"04_energy_meters_recognition_cnn/X.pickle\", \"rb\")\n",
        "X = pickle.load(pickle_in)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "tSfPHCmjMJHM"
      },
      "outputs": [],
      "source": [
        "pickle_in = open(\"04_energy_meters_recognition_cnn/y.pickle\", \"rb\")\n",
        "y = pickle.load(pickle_in)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "try:\n",
        "    from sklearn.metrics import roc_auc_score\n",
        "except ImportError:\n",
        "    print(\"Error: sklearn.metrics.roc_auc_score could not be imported. Make sure scikit-learn is installed.\")\n",
        "    roc_auc_score = None"
      ],
      "metadata": {
        "id": "VZ-wS8vc3pcx"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split data into train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "8IpKwg5T2jMc"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "dIs_BPF9MJHN",
        "outputId": "b5576e83-dea5-4ed8-c334-2beb89902878"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Model: 1-conv-32-nodes-0-dense-1724925840\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m320\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation (\u001b[38;5;33mActivation\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m76832\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │          \u001b[38;5;34m76,833\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_1 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">76832</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │          <span style=\"color: #00af00; text-decoration-color: #00af00\">76,833</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m77,153\u001b[0m (301.38 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">77,153</span> (301.38 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m77,153\u001b[0m (301.38 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">77,153</span> (301.38 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step - accuracy: 0.5000 - loss: 0.6999 - val_accuracy: 0.5000 - val_loss: 1.2914\n",
            "Epoch 2/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - accuracy: 0.5000 - loss: 1.0021 - val_accuracy: 0.5000 - val_loss: 0.6773\n",
            "Epoch 3/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - accuracy: 0.7857 - loss: 0.6018 - val_accuracy: 0.5000 - val_loss: 0.6614\n",
            "Epoch 4/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - accuracy: 0.5000 - loss: 0.7985 - val_accuracy: 0.5000 - val_loss: 0.6608\n",
            "Epoch 5/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - accuracy: 0.5714 - loss: 0.7105 - val_accuracy: 0.5000 - val_loss: 0.6852\n",
            "Epoch 6/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - accuracy: 0.7143 - loss: 0.5463 - val_accuracy: 0.0000e+00 - val_loss: 0.8348\n",
            "Epoch 7/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - accuracy: 0.7143 - loss: 0.5423 - val_accuracy: 0.5000 - val_loss: 0.9706\n",
            "Epoch 8/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - accuracy: 0.6429 - loss: 0.5945 - val_accuracy: 0.5000 - val_loss: 0.9710\n",
            "Epoch 9/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - accuracy: 0.6429 - loss: 0.5695 - val_accuracy: 0.0000e+00 - val_loss: 0.8819\n",
            "Epoch 10/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - accuracy: 0.7143 - loss: 0.4927 - val_accuracy: 0.5000 - val_loss: 0.8009\n",
            "Epoch 11/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - accuracy: 0.8571 - loss: 0.4370 - val_accuracy: 0.5000 - val_loss: 0.7828\n",
            "Epoch 12/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - accuracy: 0.7857 - loss: 0.4330 - val_accuracy: 0.5000 - val_loss: 0.8055\n",
            "Epoch 13/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - accuracy: 0.7143 - loss: 0.4435 - val_accuracy: 0.5000 - val_loss: 0.8247\n",
            "Epoch 14/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - accuracy: 0.7143 - loss: 0.4263 - val_accuracy: 0.5000 - val_loss: 0.8335\n",
            "Epoch 15/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - accuracy: 0.8571 - loss: 0.3860 - val_accuracy: 0.5000 - val_loss: 0.8575\n",
            "Epoch 16/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - accuracy: 1.0000 - loss: 0.3585 - val_accuracy: 0.0000e+00 - val_loss: 0.9040\n",
            "Epoch 17/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - accuracy: 0.7857 - loss: 0.3620 - val_accuracy: 0.0000e+00 - val_loss: 0.9249\n",
            "Epoch 18/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - accuracy: 0.7857 - loss: 0.3572 - val_accuracy: 0.5000 - val_loss: 0.9186\n",
            "Epoch 19/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - accuracy: 0.8571 - loss: 0.3302 - val_accuracy: 0.5000 - val_loss: 0.9249\n",
            "Epoch 20/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - accuracy: 1.0000 - loss: 0.3053 - val_accuracy: 0.5000 - val_loss: 0.9603\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 0.0000e+00 - loss: 2.5471\n",
            "Test accuracy: 0.0\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
            "AUC: 0.0\n",
            "\n",
            "Model: 2-conv-32-nodes-0-dense-1724925844\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m320\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_2 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │           \u001b[38;5;34m9,248\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_3 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16928\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │          \u001b[38;5;34m16,929\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_4 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16928</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │          <span style=\"color: #00af00; text-decoration-color: #00af00\">16,929</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m26,497\u001b[0m (103.50 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">26,497</span> (103.50 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m26,497\u001b[0m (103.50 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">26,497</span> (103.50 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step - accuracy: 0.4286 - loss: 0.7001 - val_accuracy: 0.5000 - val_loss: 0.6761\n",
            "Epoch 2/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - accuracy: 0.5714 - loss: 0.6722 - val_accuracy: 0.5000 - val_loss: 0.7384\n",
            "Epoch 3/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - accuracy: 0.5714 - loss: 0.6506 - val_accuracy: 0.5000 - val_loss: 0.7229\n",
            "Epoch 4/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - accuracy: 0.7857 - loss: 0.6242 - val_accuracy: 0.5000 - val_loss: 0.6961\n",
            "Epoch 5/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - accuracy: 0.7857 - loss: 0.6018 - val_accuracy: 0.5000 - val_loss: 0.7139\n",
            "Epoch 6/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - accuracy: 0.7143 - loss: 0.5767 - val_accuracy: 0.0000e+00 - val_loss: 0.7620\n",
            "Epoch 7/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - accuracy: 0.7857 - loss: 0.5546 - val_accuracy: 0.0000e+00 - val_loss: 0.7526\n",
            "Epoch 8/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - accuracy: 0.7143 - loss: 0.5292 - val_accuracy: 0.5000 - val_loss: 0.7286\n",
            "Epoch 9/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - accuracy: 0.7857 - loss: 0.5099 - val_accuracy: 0.0000e+00 - val_loss: 0.7683\n",
            "Epoch 10/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - accuracy: 0.7143 - loss: 0.4878 - val_accuracy: 0.0000e+00 - val_loss: 0.7862\n",
            "Epoch 11/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - accuracy: 0.7143 - loss: 0.4685 - val_accuracy: 0.5000 - val_loss: 0.7574\n",
            "Epoch 12/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - accuracy: 0.8571 - loss: 0.4503 - val_accuracy: 0.5000 - val_loss: 0.8003\n",
            "Epoch 13/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - accuracy: 0.7143 - loss: 0.4290 - val_accuracy: 0.5000 - val_loss: 0.8001\n",
            "Epoch 14/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - accuracy: 0.7857 - loss: 0.4080 - val_accuracy: 0.5000 - val_loss: 0.7967\n",
            "Epoch 15/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - accuracy: 0.8571 - loss: 0.3896 - val_accuracy: 0.5000 - val_loss: 0.8540\n",
            "Epoch 16/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - accuracy: 0.8571 - loss: 0.3733 - val_accuracy: 0.5000 - val_loss: 0.8280\n",
            "Epoch 17/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - accuracy: 0.7857 - loss: 0.3556 - val_accuracy: 0.5000 - val_loss: 0.8691\n",
            "Epoch 18/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - accuracy: 0.9286 - loss: 0.3328 - val_accuracy: 0.5000 - val_loss: 0.8666\n",
            "Epoch 19/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - accuracy: 0.9286 - loss: 0.3114 - val_accuracy: 0.5000 - val_loss: 0.8701\n",
            "Epoch 20/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - accuracy: 0.9286 - loss: 0.2966 - val_accuracy: 0.5000 - val_loss: 0.9003\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.2500 - loss: 3.7258\n",
            "Test accuracy: 0.25\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step\n",
            "AUC: 0.0\n",
            "\n",
            "Model: 3-conv-32-nodes-0-dense-1724925848\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m320\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_5 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │           \u001b[38;5;34m9,248\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_6 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_4 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │           \u001b[38;5;34m9,248\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_7 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_5 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_2 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3200\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │           \u001b[38;5;34m3,201\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_8 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3200</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">3,201</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m22,017\u001b[0m (86.00 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">22,017</span> (86.00 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m22,017\u001b[0m (86.00 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">22,017</span> (86.00 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.5714 - loss: 0.6937 - val_accuracy: 0.5000 - val_loss: 0.7063\n",
            "Epoch 2/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - accuracy: 0.5714 - loss: 0.6844 - val_accuracy: 0.5000 - val_loss: 0.7043\n",
            "Epoch 3/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - accuracy: 0.6429 - loss: 0.6755 - val_accuracy: 0.5000 - val_loss: 0.6980\n",
            "Epoch 4/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - accuracy: 0.7143 - loss: 0.6663 - val_accuracy: 0.5000 - val_loss: 0.6932\n",
            "Epoch 5/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - accuracy: 0.8571 - loss: 0.6560 - val_accuracy: 0.5000 - val_loss: 0.6935\n",
            "Epoch 6/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - accuracy: 0.8571 - loss: 0.6441 - val_accuracy: 0.0000e+00 - val_loss: 0.6977\n",
            "Epoch 7/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - accuracy: 0.7857 - loss: 0.6308 - val_accuracy: 0.0000e+00 - val_loss: 0.6991\n",
            "Epoch 8/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - accuracy: 0.7857 - loss: 0.6160 - val_accuracy: 0.5000 - val_loss: 0.6897\n",
            "Epoch 9/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - accuracy: 0.7857 - loss: 0.5993 - val_accuracy: 0.5000 - val_loss: 0.6746\n",
            "Epoch 10/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - accuracy: 0.9286 - loss: 0.5808 - val_accuracy: 0.5000 - val_loss: 0.6645\n",
            "Epoch 11/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - accuracy: 0.9286 - loss: 0.5600 - val_accuracy: 0.5000 - val_loss: 0.6574\n",
            "Epoch 12/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - accuracy: 0.9286 - loss: 0.5379 - val_accuracy: 0.5000 - val_loss: 0.6427\n",
            "Epoch 13/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - accuracy: 0.9286 - loss: 0.5133 - val_accuracy: 0.5000 - val_loss: 0.6220\n",
            "Epoch 14/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - accuracy: 0.8571 - loss: 0.4863 - val_accuracy: 0.5000 - val_loss: 0.6103\n",
            "Epoch 15/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - accuracy: 0.9286 - loss: 0.4581 - val_accuracy: 0.5000 - val_loss: 0.5917\n",
            "Epoch 16/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - accuracy: 0.9286 - loss: 0.4281 - val_accuracy: 0.5000 - val_loss: 0.5658\n",
            "Epoch 17/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - accuracy: 0.9286 - loss: 0.3961 - val_accuracy: 0.5000 - val_loss: 0.5330\n",
            "Epoch 18/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - accuracy: 0.9286 - loss: 0.3635 - val_accuracy: 0.5000 - val_loss: 0.5108\n",
            "Epoch 19/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - accuracy: 0.9286 - loss: 0.3358 - val_accuracy: 0.5000 - val_loss: 0.5061\n",
            "Epoch 20/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - accuracy: 0.8571 - loss: 0.3203 - val_accuracy: 1.0000 - val_loss: 0.4779\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - accuracy: 0.2500 - loss: 2.1586\n",
            "Test accuracy: 0.25\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step\n",
            "AUC: 0.25\n",
            "\n",
            "Model: 1-conv-64-nodes-0-dense-1724925852\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_3\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_3\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │             \u001b[38;5;34m640\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_9 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_6 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_3 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m153664\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │         \u001b[38;5;34m153,665\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_10 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">640</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">153664</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">153,665</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m154,305\u001b[0m (602.75 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">154,305</span> (602.75 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m154,305\u001b[0m (602.75 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">154,305</span> (602.75 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step - accuracy: 0.5000 - loss: 0.6948 - val_accuracy: 0.5000 - val_loss: 0.6760\n",
            "Epoch 2/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - accuracy: 0.5714 - loss: 0.7426 - val_accuracy: 0.5000 - val_loss: 1.6559\n",
            "Epoch 3/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - accuracy: 0.5000 - loss: 1.1453 - val_accuracy: 0.5000 - val_loss: 1.1421\n",
            "Epoch 4/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - accuracy: 0.5714 - loss: 0.7233 - val_accuracy: 0.5000 - val_loss: 0.7033\n",
            "Epoch 5/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - accuracy: 0.7143 - loss: 0.5512 - val_accuracy: 0.5000 - val_loss: 0.7720\n",
            "Epoch 6/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - accuracy: 0.5714 - loss: 0.7480 - val_accuracy: 0.5000 - val_loss: 0.7659\n",
            "Epoch 7/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - accuracy: 0.6429 - loss: 0.6369 - val_accuracy: 0.5000 - val_loss: 0.7675\n",
            "Epoch 8/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - accuracy: 0.7857 - loss: 0.4573 - val_accuracy: 0.0000e+00 - val_loss: 0.9235\n",
            "Epoch 9/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - accuracy: 0.7857 - loss: 0.4624 - val_accuracy: 0.0000e+00 - val_loss: 1.0799\n",
            "Epoch 10/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - accuracy: 0.6429 - loss: 0.5334 - val_accuracy: 0.0000e+00 - val_loss: 1.0706\n",
            "Epoch 11/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - accuracy: 0.6429 - loss: 0.4993 - val_accuracy: 0.0000e+00 - val_loss: 0.9819\n",
            "Epoch 12/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - accuracy: 0.7857 - loss: 0.4135 - val_accuracy: 0.5000 - val_loss: 0.9280\n",
            "Epoch 13/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - accuracy: 1.0000 - loss: 0.3636 - val_accuracy: 0.5000 - val_loss: 0.9488\n",
            "Epoch 14/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - accuracy: 0.8571 - loss: 0.3696 - val_accuracy: 0.5000 - val_loss: 0.9926\n",
            "Epoch 15/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - accuracy: 0.7857 - loss: 0.3819 - val_accuracy: 0.5000 - val_loss: 1.0083\n",
            "Epoch 16/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - accuracy: 0.8571 - loss: 0.3617 - val_accuracy: 0.5000 - val_loss: 0.9950\n",
            "Epoch 17/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - accuracy: 0.9286 - loss: 0.3194 - val_accuracy: 0.5000 - val_loss: 0.9850\n",
            "Epoch 18/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - accuracy: 1.0000 - loss: 0.2956 - val_accuracy: 0.0000e+00 - val_loss: 1.0045\n",
            "Epoch 19/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - accuracy: 0.8571 - loss: 0.3076 - val_accuracy: 0.0000e+00 - val_loss: 1.0256\n",
            "Epoch 20/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - accuracy: 0.8571 - loss: 0.2966 - val_accuracy: 0.5000 - val_loss: 1.0578\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.2500 - loss: 2.6966\n",
            "Test accuracy: 0.25\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
            "AUC: 0.0\n",
            "\n",
            "Model: 2-conv-64-nodes-0-dense-1724925856\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_4\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_4\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │             \u001b[38;5;34m640\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_11 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_7 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_8 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m36,928\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_12 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_8 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_4 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33856\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │          \u001b[38;5;34m33,857\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_13 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">640</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33856</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │          <span style=\"color: #00af00; text-decoration-color: #00af00\">33,857</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m71,425\u001b[0m (279.00 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">71,425</span> (279.00 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m71,425\u001b[0m (279.00 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">71,425</span> (279.00 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step - accuracy: 0.5000 - loss: 0.6957 - val_accuracy: 0.5000 - val_loss: 0.7512\n",
            "Epoch 2/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - accuracy: 0.5000 - loss: 0.6758 - val_accuracy: 0.5000 - val_loss: 0.6857\n",
            "Epoch 3/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - accuracy: 0.7143 - loss: 0.6464 - val_accuracy: 0.5000 - val_loss: 0.6770\n",
            "Epoch 4/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - accuracy: 0.7143 - loss: 0.6246 - val_accuracy: 0.0000e+00 - val_loss: 0.7223\n",
            "Epoch 5/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - accuracy: 0.7143 - loss: 0.5887 - val_accuracy: 0.0000e+00 - val_loss: 0.7826\n",
            "Epoch 6/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - accuracy: 0.7857 - loss: 0.5639 - val_accuracy: 0.5000 - val_loss: 0.7451\n",
            "Epoch 7/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - accuracy: 0.7143 - loss: 0.5241 - val_accuracy: 0.5000 - val_loss: 0.7243\n",
            "Epoch 8/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - accuracy: 0.7857 - loss: 0.4973 - val_accuracy: 0.5000 - val_loss: 0.7789\n",
            "Epoch 9/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - accuracy: 0.8571 - loss: 0.4577 - val_accuracy: 0.0000e+00 - val_loss: 0.8163\n",
            "Epoch 10/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - accuracy: 0.8571 - loss: 0.4292 - val_accuracy: 0.5000 - val_loss: 0.7747\n",
            "Epoch 11/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - accuracy: 0.7857 - loss: 0.3975 - val_accuracy: 0.5000 - val_loss: 0.8062\n",
            "Epoch 12/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 1.0000 - loss: 0.3583 - val_accuracy: 0.5000 - val_loss: 0.8388\n",
            "Epoch 13/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - accuracy: 0.8571 - loss: 0.3335 - val_accuracy: 0.5000 - val_loss: 0.8118\n",
            "Epoch 14/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - accuracy: 0.8571 - loss: 0.3065 - val_accuracy: 0.5000 - val_loss: 0.8231\n",
            "Epoch 15/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - accuracy: 0.9286 - loss: 0.2689 - val_accuracy: 0.5000 - val_loss: 0.8189\n",
            "Epoch 16/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - accuracy: 1.0000 - loss: 0.2406 - val_accuracy: 0.5000 - val_loss: 0.8435\n",
            "Epoch 17/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - accuracy: 0.9286 - loss: 0.2235 - val_accuracy: 0.5000 - val_loss: 0.7918\n",
            "Epoch 18/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - accuracy: 0.9286 - loss: 0.2022 - val_accuracy: 0.5000 - val_loss: 0.8335\n",
            "Epoch 19/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - accuracy: 1.0000 - loss: 0.1723 - val_accuracy: 0.5000 - val_loss: 0.8259\n",
            "Epoch 20/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - accuracy: 1.0000 - loss: 0.1520 - val_accuracy: 0.5000 - val_loss: 0.7577\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - accuracy: 0.2500 - loss: 3.6459\n",
            "Test accuracy: 0.25\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:5 out of the last 5 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7826e6946dd0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step\n",
            "AUC: 0.25\n",
            "\n",
            "Model: 3-conv-64-nodes-0-dense-1724925861\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_5\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_5\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_9 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │             \u001b[38;5;34m640\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_14 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_9 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_10 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m36,928\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_15 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_10 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_11 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m36,928\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_16 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_11 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_5 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6400\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │           \u001b[38;5;34m6,401\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_17 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">640</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6400</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">6,401</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m80,897\u001b[0m (316.00 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">80,897</span> (316.00 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m80,897\u001b[0m (316.00 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">80,897</span> (316.00 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.5000 - loss: 0.6912 - val_accuracy: 0.5000 - val_loss: 0.7065\n",
            "Epoch 2/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - accuracy: 0.5714 - loss: 0.6802 - val_accuracy: 0.0000e+00 - val_loss: 0.7113\n",
            "Epoch 3/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - accuracy: 0.6429 - loss: 0.6687 - val_accuracy: 0.0000e+00 - val_loss: 0.7148\n",
            "Epoch 4/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - accuracy: 0.7143 - loss: 0.6556 - val_accuracy: 0.0000e+00 - val_loss: 0.7101\n",
            "Epoch 5/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - accuracy: 0.7857 - loss: 0.6391 - val_accuracy: 0.5000 - val_loss: 0.7023\n",
            "Epoch 6/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - accuracy: 0.9286 - loss: 0.6188 - val_accuracy: 0.5000 - val_loss: 0.7056\n",
            "Epoch 7/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - accuracy: 0.9286 - loss: 0.5944 - val_accuracy: 0.5000 - val_loss: 0.7102\n",
            "Epoch 8/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - accuracy: 0.9286 - loss: 0.5663 - val_accuracy: 0.5000 - val_loss: 0.6959\n",
            "Epoch 9/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - accuracy: 0.9286 - loss: 0.5338 - val_accuracy: 0.5000 - val_loss: 0.7074\n",
            "Epoch 10/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - accuracy: 0.9286 - loss: 0.4978 - val_accuracy: 0.5000 - val_loss: 0.6840\n",
            "Epoch 11/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - accuracy: 0.9286 - loss: 0.4601 - val_accuracy: 0.5000 - val_loss: 0.7143\n",
            "Epoch 12/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - accuracy: 0.7857 - loss: 0.4302 - val_accuracy: 0.5000 - val_loss: 0.6845\n",
            "Epoch 13/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - accuracy: 0.8571 - loss: 0.3966 - val_accuracy: 0.5000 - val_loss: 0.6783\n",
            "Epoch 14/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - accuracy: 0.9286 - loss: 0.3517 - val_accuracy: 0.5000 - val_loss: 0.6720\n",
            "Epoch 15/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - accuracy: 0.9286 - loss: 0.3223 - val_accuracy: 0.5000 - val_loss: 0.7339\n",
            "Epoch 16/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - accuracy: 0.8571 - loss: 0.3025 - val_accuracy: 0.5000 - val_loss: 0.6632\n",
            "Epoch 17/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - accuracy: 0.9286 - loss: 0.2637 - val_accuracy: 0.5000 - val_loss: 0.7087\n",
            "Epoch 18/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - accuracy: 1.0000 - loss: 0.2227 - val_accuracy: 0.5000 - val_loss: 0.8085\n",
            "Epoch 19/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - accuracy: 0.9286 - loss: 0.2062 - val_accuracy: 0.5000 - val_loss: 0.6720\n",
            "Epoch 20/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - accuracy: 0.9286 - loss: 0.1949 - val_accuracy: 0.5000 - val_loss: 0.9040\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - accuracy: 0.5000 - loss: 3.2893\n",
            "Test accuracy: 0.5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:6 out of the last 6 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7826b0ba9360> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step\n",
            "AUC: 0.25\n",
            "\n",
            "Model: 1-conv-128-nodes-0-dense-1724925866\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_6\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_6\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_12 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │           \u001b[38;5;34m1,280\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_18 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_12 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_6 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m307328\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │         \u001b[38;5;34m307,329\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_19 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,280</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_18 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">307328</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">307,329</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_19 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m308,609\u001b[0m (1.18 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">308,609</span> (1.18 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m308,609\u001b[0m (1.18 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">308,609</span> (1.18 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step - accuracy: 0.5000 - loss: 0.6912 - val_accuracy: 0.5000 - val_loss: 1.0148\n",
            "Epoch 2/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - accuracy: 0.5714 - loss: 0.7990 - val_accuracy: 0.5000 - val_loss: 1.3355\n",
            "Epoch 3/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - accuracy: 0.5000 - loss: 2.1898 - val_accuracy: 0.5000 - val_loss: 0.8701\n",
            "Epoch 4/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - accuracy: 0.5714 - loss: 1.1228 - val_accuracy: 0.5000 - val_loss: 0.8913\n",
            "Epoch 5/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - accuracy: 0.5714 - loss: 0.6195 - val_accuracy: 0.5000 - val_loss: 1.6169\n",
            "Epoch 6/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - accuracy: 0.5714 - loss: 1.1269 - val_accuracy: 0.5000 - val_loss: 1.4247\n",
            "Epoch 7/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - accuracy: 0.5714 - loss: 0.9444 - val_accuracy: 0.0000e+00 - val_loss: 0.8876\n",
            "Epoch 8/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - accuracy: 0.7857 - loss: 0.5228 - val_accuracy: 0.5000 - val_loss: 0.7247\n",
            "Epoch 9/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - accuracy: 0.7857 - loss: 0.4628 - val_accuracy: 0.5000 - val_loss: 0.8361\n",
            "Epoch 10/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - accuracy: 0.6429 - loss: 0.6285 - val_accuracy: 0.5000 - val_loss: 0.8830\n",
            "Epoch 11/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - accuracy: 0.6429 - loss: 0.6459 - val_accuracy: 0.5000 - val_loss: 0.8465\n",
            "Epoch 12/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - accuracy: 0.7143 - loss: 0.5132 - val_accuracy: 0.5000 - val_loss: 0.7984\n",
            "Epoch 13/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - accuracy: 0.8571 - loss: 0.3644 - val_accuracy: 0.0000e+00 - val_loss: 0.8577\n",
            "Epoch 14/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - accuracy: 0.7857 - loss: 0.3549 - val_accuracy: 0.0000e+00 - val_loss: 0.9862\n",
            "Epoch 15/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - accuracy: 0.7143 - loss: 0.4268 - val_accuracy: 0.0000e+00 - val_loss: 1.0115\n",
            "Epoch 16/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - accuracy: 0.7143 - loss: 0.4050 - val_accuracy: 0.0000e+00 - val_loss: 1.0046\n",
            "Epoch 17/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - accuracy: 0.7857 - loss: 0.3385 - val_accuracy: 0.5000 - val_loss: 1.0353\n",
            "Epoch 18/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - accuracy: 0.9286 - loss: 0.2899 - val_accuracy: 0.5000 - val_loss: 1.1158\n",
            "Epoch 19/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - accuracy: 0.9286 - loss: 0.2771 - val_accuracy: 0.5000 - val_loss: 1.2222\n",
            "Epoch 20/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - accuracy: 0.8571 - loss: 0.2868 - val_accuracy: 0.5000 - val_loss: 1.2870\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - accuracy: 0.0000e+00 - loss: 3.1316\n",
            "Test accuracy: 0.0\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n",
            "AUC: 0.0\n",
            "\n",
            "Model: 2-conv-128-nodes-0-dense-1724925870\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_7\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_7\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_13 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │           \u001b[38;5;34m1,280\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_20 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_13 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_14 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │         \u001b[38;5;34m147,584\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_21 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_14 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_7 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m67712\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │          \u001b[38;5;34m67,713\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_22 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,280</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">67712</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │          <span style=\"color: #00af00; text-decoration-color: #00af00\">67,713</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_22 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m216,577\u001b[0m (846.00 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">216,577</span> (846.00 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m216,577\u001b[0m (846.00 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">216,577</span> (846.00 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.5000 - loss: 0.6913 - val_accuracy: 0.5000 - val_loss: 0.6893\n",
            "Epoch 2/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 284ms/step - accuracy: 0.7143 - loss: 0.6379 - val_accuracy: 0.5000 - val_loss: 0.9700\n",
            "Epoch 3/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 278ms/step - accuracy: 0.5714 - loss: 0.6944 - val_accuracy: 0.5000 - val_loss: 0.6539\n",
            "Epoch 4/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 280ms/step - accuracy: 0.5714 - loss: 0.6139 - val_accuracy: 0.5000 - val_loss: 0.6574\n",
            "Epoch 5/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 282ms/step - accuracy: 0.5714 - loss: 0.6025 - val_accuracy: 0.0000e+00 - val_loss: 0.7499\n",
            "Epoch 6/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 280ms/step - accuracy: 0.8571 - loss: 0.5582 - val_accuracy: 0.5000 - val_loss: 0.8291\n",
            "Epoch 7/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 277ms/step - accuracy: 0.6429 - loss: 0.5581 - val_accuracy: 0.5000 - val_loss: 0.7538\n",
            "Epoch 8/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 291ms/step - accuracy: 0.8571 - loss: 0.5024 - val_accuracy: 0.5000 - val_loss: 0.7327\n",
            "Epoch 9/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 284ms/step - accuracy: 0.7857 - loss: 0.4789 - val_accuracy: 0.5000 - val_loss: 0.7670\n",
            "Epoch 10/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 291ms/step - accuracy: 0.7857 - loss: 0.4332 - val_accuracy: 0.0000e+00 - val_loss: 0.8528\n",
            "Epoch 11/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 288ms/step - accuracy: 0.8571 - loss: 0.4070 - val_accuracy: 0.5000 - val_loss: 0.8475\n",
            "Epoch 12/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 281ms/step - accuracy: 0.9286 - loss: 0.3563 - val_accuracy: 0.5000 - val_loss: 0.8756\n",
            "Epoch 13/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 295ms/step - accuracy: 0.8571 - loss: 0.3263 - val_accuracy: 0.5000 - val_loss: 0.8937\n",
            "Epoch 14/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 283ms/step - accuracy: 1.0000 - loss: 0.2792 - val_accuracy: 0.5000 - val_loss: 0.9136\n",
            "Epoch 15/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 287ms/step - accuracy: 0.8571 - loss: 0.2556 - val_accuracy: 0.5000 - val_loss: 1.0079\n",
            "Epoch 16/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 283ms/step - accuracy: 0.9286 - loss: 0.2248 - val_accuracy: 0.5000 - val_loss: 0.9613\n",
            "Epoch 17/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 286ms/step - accuracy: 1.0000 - loss: 0.1798 - val_accuracy: 0.5000 - val_loss: 0.9133\n",
            "Epoch 18/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 285ms/step - accuracy: 0.9286 - loss: 0.1722 - val_accuracy: 0.5000 - val_loss: 1.1882\n",
            "Epoch 19/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 291ms/step - accuracy: 0.9286 - loss: 0.1601 - val_accuracy: 0.5000 - val_loss: 1.0712\n",
            "Epoch 20/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 287ms/step - accuracy: 1.0000 - loss: 0.1113 - val_accuracy: 0.5000 - val_loss: 0.9400\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.2500 - loss: 3.6052\n",
            "Test accuracy: 0.25\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step\n",
            "AUC: 0.25\n",
            "\n",
            "Model: 3-conv-128-nodes-0-dense-1724925878\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_8\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_8\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_15 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │           \u001b[38;5;34m1,280\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_23 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_15 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_16 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │         \u001b[38;5;34m147,584\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_24 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_16 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_17 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │         \u001b[38;5;34m147,584\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_25 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_17 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_8 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12800\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_8 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │          \u001b[38;5;34m12,801\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_26 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,280</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_23 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_24 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_25 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12800</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │          <span style=\"color: #00af00; text-decoration-color: #00af00\">12,801</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_26 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m309,249\u001b[0m (1.18 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">309,249</span> (1.18 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m309,249\u001b[0m (1.18 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">309,249</span> (1.18 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.5000 - loss: 0.6965 - val_accuracy: 0.5000 - val_loss: 0.7377\n",
            "Epoch 2/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 314ms/step - accuracy: 0.5000 - loss: 0.6834 - val_accuracy: 0.0000e+00 - val_loss: 0.7071\n",
            "Epoch 3/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 313ms/step - accuracy: 0.7857 - loss: 0.6676 - val_accuracy: 0.5000 - val_loss: 0.6948\n",
            "Epoch 4/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 308ms/step - accuracy: 0.8571 - loss: 0.6466 - val_accuracy: 0.5000 - val_loss: 0.7271\n",
            "Epoch 5/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 319ms/step - accuracy: 0.7857 - loss: 0.6179 - val_accuracy: 0.5000 - val_loss: 0.6944\n",
            "Epoch 6/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 312ms/step - accuracy: 0.8571 - loss: 0.5782 - val_accuracy: 0.5000 - val_loss: 0.6908\n",
            "Epoch 7/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 336ms/step - accuracy: 0.8571 - loss: 0.5309 - val_accuracy: 0.5000 - val_loss: 0.7138\n",
            "Epoch 8/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 359ms/step - accuracy: 0.8571 - loss: 0.4820 - val_accuracy: 0.5000 - val_loss: 0.6810\n",
            "Epoch 9/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 344ms/step - accuracy: 0.6429 - loss: 0.5520 - val_accuracy: 0.5000 - val_loss: 0.8087\n",
            "Epoch 10/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 353ms/step - accuracy: 0.7857 - loss: 0.4807 - val_accuracy: 0.5000 - val_loss: 0.7208\n",
            "Epoch 11/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 349ms/step - accuracy: 0.8571 - loss: 0.4021 - val_accuracy: 0.5000 - val_loss: 0.7029\n",
            "Epoch 12/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 361ms/step - accuracy: 0.8571 - loss: 0.3846 - val_accuracy: 0.5000 - val_loss: 0.7406\n",
            "Epoch 13/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 353ms/step - accuracy: 0.8571 - loss: 0.3752 - val_accuracy: 0.5000 - val_loss: 0.7182\n",
            "Epoch 14/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 354ms/step - accuracy: 0.9286 - loss: 0.3046 - val_accuracy: 0.5000 - val_loss: 0.7255\n",
            "Epoch 15/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 359ms/step - accuracy: 0.8571 - loss: 0.3291 - val_accuracy: 0.5000 - val_loss: 0.7483\n",
            "Epoch 16/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 324ms/step - accuracy: 0.8571 - loss: 0.2551 - val_accuracy: 0.5000 - val_loss: 0.9089\n",
            "Epoch 17/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 325ms/step - accuracy: 0.8571 - loss: 0.2431 - val_accuracy: 0.5000 - val_loss: 0.9759\n",
            "Epoch 18/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 323ms/step - accuracy: 0.8571 - loss: 0.2159 - val_accuracy: 0.5000 - val_loss: 0.8464\n",
            "Epoch 19/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 319ms/step - accuracy: 1.0000 - loss: 0.1694 - val_accuracy: 0.5000 - val_loss: 0.8422\n",
            "Epoch 20/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 315ms/step - accuracy: 0.8571 - loss: 0.1679 - val_accuracy: 0.5000 - val_loss: 1.1844\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.5000 - loss: 2.9506\n",
            "Test accuracy: 0.5\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step\n",
            "AUC: 0.25\n",
            "\n",
            "Model: 1-conv-32-nodes-1-dense-1724925887\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_9\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_9\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_18 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m320\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_27 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_18 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_9 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m76832\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_9 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │       \u001b[38;5;34m2,458,656\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_28 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_10 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m33\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_29 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_18 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_27 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_18 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">76832</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,458,656</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_28 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_29 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,459,009\u001b[0m (9.38 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,459,009</span> (9.38 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,459,009\u001b[0m (9.38 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,459,009</span> (9.38 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.3571 - loss: 0.6957 - val_accuracy: 0.5000 - val_loss: 0.6405\n",
            "Epoch 2/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - accuracy: 0.5000 - loss: 0.7490 - val_accuracy: 0.5000 - val_loss: 1.3401\n",
            "Epoch 3/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - accuracy: 0.5714 - loss: 0.9838 - val_accuracy: 0.5000 - val_loss: 0.9154\n",
            "Epoch 4/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - accuracy: 0.5714 - loss: 0.6774 - val_accuracy: 0.5000 - val_loss: 0.6843\n",
            "Epoch 5/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - accuracy: 0.6429 - loss: 0.7109 - val_accuracy: 0.5000 - val_loss: 0.7047\n",
            "Epoch 6/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - accuracy: 0.6429 - loss: 0.6859 - val_accuracy: 0.5000 - val_loss: 0.6899\n",
            "Epoch 7/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - accuracy: 0.7857 - loss: 0.4802 - val_accuracy: 0.0000e+00 - val_loss: 0.8966\n",
            "Epoch 8/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - accuracy: 0.6429 - loss: 0.5489 - val_accuracy: 0.5000 - val_loss: 0.9789\n",
            "Epoch 9/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - accuracy: 0.6429 - loss: 0.5711 - val_accuracy: 0.0000e+00 - val_loss: 0.8683\n",
            "Epoch 10/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - accuracy: 0.8571 - loss: 0.4559 - val_accuracy: 0.5000 - val_loss: 0.7989\n",
            "Epoch 11/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - accuracy: 0.8571 - loss: 0.3941 - val_accuracy: 0.5000 - val_loss: 0.8408\n",
            "Epoch 12/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - accuracy: 0.7143 - loss: 0.4198 - val_accuracy: 0.5000 - val_loss: 0.8807\n",
            "Epoch 13/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - accuracy: 0.7143 - loss: 0.4117 - val_accuracy: 0.5000 - val_loss: 0.8893\n",
            "Epoch 14/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - accuracy: 0.8571 - loss: 0.3597 - val_accuracy: 0.5000 - val_loss: 0.9173\n",
            "Epoch 15/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - accuracy: 0.8571 - loss: 0.3249 - val_accuracy: 0.0000e+00 - val_loss: 0.9937\n",
            "Epoch 16/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - accuracy: 0.7857 - loss: 0.3579 - val_accuracy: 0.0000e+00 - val_loss: 0.9970\n",
            "Epoch 17/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - accuracy: 0.7857 - loss: 0.3301 - val_accuracy: 0.5000 - val_loss: 0.9914\n",
            "Epoch 18/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - accuracy: 1.0000 - loss: 0.2790 - val_accuracy: 0.5000 - val_loss: 1.0552\n",
            "Epoch 19/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - accuracy: 0.9286 - loss: 0.2708 - val_accuracy: 0.5000 - val_loss: 1.1347\n",
            "Epoch 20/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - accuracy: 0.9286 - loss: 0.2791 - val_accuracy: 0.5000 - val_loss: 1.1597\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - accuracy: 0.0000e+00 - loss: 2.9006\n",
            "Test accuracy: 0.0\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step\n",
            "AUC: 0.0\n",
            "\n",
            "Model: 2-conv-32-nodes-1-dense-1724925891\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_10\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_10\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_19 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m320\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_30 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_19 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_20 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │           \u001b[38;5;34m9,248\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_31 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_20 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_10 (\u001b[38;5;33mFlatten\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16928\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_11 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │         \u001b[38;5;34m541,728\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_32 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_12 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m33\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_33 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_19 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_30 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_19 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_31 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16928</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │         <span style=\"color: #00af00; text-decoration-color: #00af00\">541,728</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_32 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_33 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m551,329\u001b[0m (2.10 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">551,329</span> (2.10 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m551,329\u001b[0m (2.10 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">551,329</span> (2.10 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.5000 - loss: 0.6915 - val_accuracy: 0.0000e+00 - val_loss: 0.7072\n",
            "Epoch 2/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - accuracy: 0.7857 - loss: 0.6523 - val_accuracy: 0.5000 - val_loss: 0.7063\n",
            "Epoch 3/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - accuracy: 0.7857 - loss: 0.5945 - val_accuracy: 0.0000e+00 - val_loss: 0.8136\n",
            "Epoch 4/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - accuracy: 0.7143 - loss: 0.5515 - val_accuracy: 0.5000 - val_loss: 0.7211\n",
            "Epoch 5/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - accuracy: 0.7143 - loss: 0.5285 - val_accuracy: 0.0000e+00 - val_loss: 0.8367\n",
            "Epoch 6/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - accuracy: 0.8571 - loss: 0.4645 - val_accuracy: 0.0000e+00 - val_loss: 0.9048\n",
            "Epoch 7/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - accuracy: 0.7857 - loss: 0.4411 - val_accuracy: 0.5000 - val_loss: 0.8506\n",
            "Epoch 8/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - accuracy: 0.7857 - loss: 0.3929 - val_accuracy: 0.5000 - val_loss: 0.8933\n",
            "Epoch 9/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - accuracy: 0.7857 - loss: 0.3580 - val_accuracy: 0.0000e+00 - val_loss: 0.9794\n",
            "Epoch 10/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - accuracy: 0.8571 - loss: 0.3203 - val_accuracy: 0.5000 - val_loss: 1.0096\n",
            "Epoch 11/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - accuracy: 0.9286 - loss: 0.2840 - val_accuracy: 0.5000 - val_loss: 1.0511\n",
            "Epoch 12/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - accuracy: 0.9286 - loss: 0.2523 - val_accuracy: 0.5000 - val_loss: 1.1054\n",
            "Epoch 13/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - accuracy: 1.0000 - loss: 0.2167 - val_accuracy: 0.5000 - val_loss: 1.1436\n",
            "Epoch 14/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - accuracy: 0.9286 - loss: 0.2000 - val_accuracy: 0.5000 - val_loss: 1.2039\n",
            "Epoch 15/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - accuracy: 1.0000 - loss: 0.1619 - val_accuracy: 0.5000 - val_loss: 1.3077\n",
            "Epoch 16/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - accuracy: 0.9286 - loss: 0.1489 - val_accuracy: 0.5000 - val_loss: 1.2680\n",
            "Epoch 17/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - accuracy: 1.0000 - loss: 0.1229 - val_accuracy: 0.5000 - val_loss: 1.3130\n",
            "Epoch 18/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - accuracy: 1.0000 - loss: 0.1075 - val_accuracy: 0.5000 - val_loss: 1.5217\n",
            "Epoch 19/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - accuracy: 1.0000 - loss: 0.0967 - val_accuracy: 0.5000 - val_loss: 1.5204\n",
            "Epoch 20/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - accuracy: 1.0000 - loss: 0.0761 - val_accuracy: 0.5000 - val_loss: 1.4999\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - accuracy: 0.2500 - loss: 3.4302\n",
            "Test accuracy: 0.25\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step\n",
            "AUC: 0.25\n",
            "\n",
            "Model: 3-conv-32-nodes-1-dense-1724925895\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_11\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_11\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_21 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m320\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_34 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_21 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_22 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │           \u001b[38;5;34m9,248\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_35 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_22 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_23 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │           \u001b[38;5;34m9,248\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_36 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_23 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_11 (\u001b[38;5;33mFlatten\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3200\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_13 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │         \u001b[38;5;34m102,432\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_37 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_14 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m33\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_38 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_34 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_22 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_35 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_22 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_23 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_36 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_23 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3200</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │         <span style=\"color: #00af00; text-decoration-color: #00af00\">102,432</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_37 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_38 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m121,281\u001b[0m (473.75 KB)\n"
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">121,281</span> (473.75 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m121,281\u001b[0m (473.75 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">121,281</span> (473.75 KB)\n",
              "</pre>\n"
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          },
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          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
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            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
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          "metadata": {}
        },
        {
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          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.5000 - loss: 0.6957 - val_accuracy: 0.5000 - val_loss: 0.6409\n",
            "Epoch 2/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - accuracy: 0.5000 - loss: 0.6940 - val_accuracy: 0.5000 - val_loss: 0.6863\n",
            "Epoch 3/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - accuracy: 0.6429 - loss: 0.6652 - val_accuracy: 0.5000 - val_loss: 0.7518\n",
            "Epoch 4/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - accuracy: 0.5714 - loss: 0.6543 - val_accuracy: 0.5000 - val_loss: 0.7744\n",
            "Epoch 5/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - accuracy: 0.5714 - loss: 0.6426 - val_accuracy: 0.5000 - val_loss: 0.7406\n",
            "Epoch 6/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - accuracy: 0.7857 - loss: 0.6163 - val_accuracy: 0.5000 - val_loss: 0.7029\n",
            "Epoch 7/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - accuracy: 0.8571 - loss: 0.5930 - val_accuracy: 0.5000 - val_loss: 0.6931\n",
            "Epoch 8/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - accuracy: 0.7857 - loss: 0.5685 - val_accuracy: 0.5000 - val_loss: 0.7094\n",
            "Epoch 9/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - accuracy: 0.8571 - loss: 0.5405 - val_accuracy: 0.0000e+00 - val_loss: 0.7361\n",
            "Epoch 10/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - accuracy: 0.8571 - loss: 0.5101 - val_accuracy: 0.5000 - val_loss: 0.7074\n",
            "Epoch 11/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - accuracy: 0.7857 - loss: 0.4753 - val_accuracy: 0.5000 - val_loss: 0.7027\n",
            "Epoch 12/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - accuracy: 0.7857 - loss: 0.4413 - val_accuracy: 0.5000 - val_loss: 0.7411\n",
            "Epoch 13/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - accuracy: 0.9286 - loss: 0.4092 - val_accuracy: 0.5000 - val_loss: 0.6823\n",
            "Epoch 14/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - accuracy: 0.7857 - loss: 0.3875 - val_accuracy: 0.0000e+00 - val_loss: 0.8285\n",
            "Epoch 15/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - accuracy: 0.7857 - loss: 0.3831 - val_accuracy: 0.5000 - val_loss: 0.7041\n",
            "Epoch 16/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - accuracy: 0.9286 - loss: 0.3199 - val_accuracy: 0.5000 - val_loss: 0.7156\n",
            "Epoch 17/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - accuracy: 0.8571 - loss: 0.3102 - val_accuracy: 0.5000 - val_loss: 0.7674\n",
            "Epoch 18/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - accuracy: 0.9286 - loss: 0.2860 - val_accuracy: 0.5000 - val_loss: 0.7451\n",
            "Epoch 19/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - accuracy: 0.9286 - loss: 0.2464 - val_accuracy: 0.5000 - val_loss: 0.7807\n",
            "Epoch 20/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - accuracy: 0.8571 - loss: 0.2456 - val_accuracy: 0.5000 - val_loss: 0.7620\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - accuracy: 0.2500 - loss: 2.7092\n",
            "Test accuracy: 0.25\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step\n",
            "AUC: 0.25\n",
            "\n",
            "Model: 1-conv-64-nodes-1-dense-1724925900\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_12\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_12\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_24 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │             \u001b[38;5;34m640\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_39 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_24 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_12 (\u001b[38;5;33mFlatten\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m153664\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_15 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │       \u001b[38;5;34m9,834,560\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_40 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_16 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m65\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_41 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_24 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">640</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_39 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_24 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">153664</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │       <span style=\"color: #00af00; text-decoration-color: #00af00\">9,834,560</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_40 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_41 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m9,835,265\u001b[0m (37.52 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">9,835,265</span> (37.52 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m9,835,265\u001b[0m (37.52 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">9,835,265</span> (37.52 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step - accuracy: 0.5714 - loss: 0.6919 - val_accuracy: 0.5000 - val_loss: 2.0834\n",
            "Epoch 2/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - accuracy: 0.5000 - loss: 1.5900 - val_accuracy: 0.5000 - val_loss: 2.6938\n",
            "Epoch 3/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - accuracy: 0.5000 - loss: 4.8405 - val_accuracy: 0.5000 - val_loss: 1.4998\n",
            "Epoch 4/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - accuracy: 0.5000 - loss: 2.3386 - val_accuracy: 0.5000 - val_loss: 1.8346\n",
            "Epoch 5/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - accuracy: 0.5000 - loss: 1.2528 - val_accuracy: 0.5000 - val_loss: 2.5716\n",
            "Epoch 6/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - accuracy: 0.5000 - loss: 1.8726 - val_accuracy: 0.5000 - val_loss: 1.5849\n",
            "Epoch 7/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - accuracy: 0.5714 - loss: 0.9860 - val_accuracy: 0.5000 - val_loss: 0.7207\n",
            "Epoch 8/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - accuracy: 0.7143 - loss: 0.5552 - val_accuracy: 0.5000 - val_loss: 0.9539\n",
            "Epoch 9/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - accuracy: 0.5714 - loss: 1.0726 - val_accuracy: 0.5000 - val_loss: 1.0255\n",
            "Epoch 10/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - accuracy: 0.5714 - loss: 1.1078 - val_accuracy: 0.5000 - val_loss: 0.8266\n",
            "Epoch 11/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - accuracy: 0.7143 - loss: 0.5297 - val_accuracy: 0.0000e+00 - val_loss: 1.0018\n",
            "Epoch 12/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - accuracy: 0.8571 - loss: 0.4062 - val_accuracy: 0.5000 - val_loss: 1.4498\n",
            "Epoch 13/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - accuracy: 0.6429 - loss: 0.6475 - val_accuracy: 0.0000e+00 - val_loss: 1.4177\n",
            "Epoch 14/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - accuracy: 0.6429 - loss: 0.5796 - val_accuracy: 0.0000e+00 - val_loss: 1.1109\n",
            "Epoch 15/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - accuracy: 0.8571 - loss: 0.3459 - val_accuracy: 0.5000 - val_loss: 1.0485\n",
            "Epoch 16/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - accuracy: 0.8571 - loss: 0.3226 - val_accuracy: 0.5000 - val_loss: 1.1768\n",
            "Epoch 17/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - accuracy: 0.7143 - loss: 0.4027 - val_accuracy: 0.5000 - val_loss: 1.2005\n",
            "Epoch 18/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - accuracy: 0.8571 - loss: 0.3443 - val_accuracy: 0.5000 - val_loss: 1.1577\n",
            "Epoch 19/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - accuracy: 0.9286 - loss: 0.2324 - val_accuracy: 0.0000e+00 - val_loss: 1.1982\n",
            "Epoch 20/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - accuracy: 0.8571 - loss: 0.2573 - val_accuracy: 0.0000e+00 - val_loss: 1.2731\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - accuracy: 0.2500 - loss: 4.1262\n",
            "Test accuracy: 0.25\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step\n",
            "AUC: 0.0\n",
            "\n",
            "Model: 2-conv-64-nodes-1-dense-1724925905\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_13\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_13\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_25 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │             \u001b[38;5;34m640\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_42 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_25 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_26 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m36,928\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_43 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_26 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_13 (\u001b[38;5;33mFlatten\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33856\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_17 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │       \u001b[38;5;34m2,166,848\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_44 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_18 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m65\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_45 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_25 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">640</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_42 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_25 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_26 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_43 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_26 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33856</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,166,848</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_44 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_18 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_45 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,204,481\u001b[0m (8.41 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,204,481</span> (8.41 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,204,481\u001b[0m (8.41 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,204,481</span> (8.41 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.5714 - loss: 0.6891 - val_accuracy: 0.5000 - val_loss: 0.8116\n",
            "Epoch 2/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - accuracy: 0.5714 - loss: 0.6509 - val_accuracy: 0.5000 - val_loss: 0.6461\n",
            "Epoch 3/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - accuracy: 0.5000 - loss: 0.7287 - val_accuracy: 0.5000 - val_loss: 0.7113\n",
            "Epoch 4/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - accuracy: 0.7857 - loss: 0.5875 - val_accuracy: 0.5000 - val_loss: 0.8777\n",
            "Epoch 5/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.5714 - loss: 0.5768 - val_accuracy: 0.0000e+00 - val_loss: 0.8660\n",
            "Epoch 6/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - accuracy: 0.7143 - loss: 0.4956 - val_accuracy: 0.5000 - val_loss: 0.8627\n",
            "Epoch 7/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 176ms/step - accuracy: 0.8571 - loss: 0.4252 - val_accuracy: 0.5000 - val_loss: 0.9704\n",
            "Epoch 8/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - accuracy: 0.9286 - loss: 0.3582 - val_accuracy: 0.0000e+00 - val_loss: 1.2931\n",
            "Epoch 9/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 179ms/step - accuracy: 0.7857 - loss: 0.4225 - val_accuracy: 0.5000 - val_loss: 1.2267\n",
            "Epoch 10/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - accuracy: 0.8571 - loss: 0.3167 - val_accuracy: 0.5000 - val_loss: 1.3124\n",
            "Epoch 11/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - accuracy: 0.8571 - loss: 0.2777 - val_accuracy: 0.5000 - val_loss: 1.3422\n",
            "Epoch 12/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 173ms/step - accuracy: 1.0000 - loss: 0.2180 - val_accuracy: 0.0000e+00 - val_loss: 1.4409\n",
            "Epoch 13/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - accuracy: 0.8571 - loss: 0.2152 - val_accuracy: 0.5000 - val_loss: 1.5899\n",
            "Epoch 14/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 179ms/step - accuracy: 1.0000 - loss: 0.1636 - val_accuracy: 0.5000 - val_loss: 1.7962\n",
            "Epoch 15/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - accuracy: 0.9286 - loss: 0.1546 - val_accuracy: 0.5000 - val_loss: 1.8021\n",
            "Epoch 16/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - accuracy: 1.0000 - loss: 0.1113 - val_accuracy: 0.5000 - val_loss: 1.8432\n",
            "Epoch 17/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - accuracy: 0.9286 - loss: 0.1174 - val_accuracy: 0.5000 - val_loss: 2.1869\n",
            "Epoch 18/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - accuracy: 1.0000 - loss: 0.0781 - val_accuracy: 0.5000 - val_loss: 2.4684\n",
            "Epoch 19/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - accuracy: 1.0000 - loss: 0.0776 - val_accuracy: 0.5000 - val_loss: 2.4324\n",
            "Epoch 20/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - accuracy: 1.0000 - loss: 0.0524 - val_accuracy: 0.5000 - val_loss: 2.4703\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - accuracy: 0.2500 - loss: 5.3380\n",
            "Test accuracy: 0.25\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step\n",
            "AUC: 0.25\n",
            "\n",
            "Model: 3-conv-64-nodes-1-dense-1724925911\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_14\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_14\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_27 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │             \u001b[38;5;34m640\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_46 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_27 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_28 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m36,928\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_47 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_28 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_29 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m36,928\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_48 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_29 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_14 (\u001b[38;5;33mFlatten\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6400\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_19 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │         \u001b[38;5;34m409,664\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_49 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_20 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m65\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_50 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_27 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">640</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_46 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_27 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_28 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_47 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_28 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_29 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_48 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_29 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6400</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_19 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │         <span style=\"color: #00af00; text-decoration-color: #00af00\">409,664</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_49 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_50 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m484,225\u001b[0m (1.85 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">484,225</span> (1.85 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m484,225\u001b[0m (1.85 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">484,225</span> (1.85 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.4286 - loss: 0.6952 - val_accuracy: 0.5000 - val_loss: 0.7023\n",
            "Epoch 2/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 172ms/step - accuracy: 0.5714 - loss: 0.6753 - val_accuracy: 0.5000 - val_loss: 0.6719\n",
            "Epoch 3/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.7143 - loss: 0.6502 - val_accuracy: 0.5000 - val_loss: 0.7096\n",
            "Epoch 4/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step - accuracy: 0.7143 - loss: 0.6170 - val_accuracy: 0.5000 - val_loss: 0.6397\n",
            "Epoch 5/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - accuracy: 0.6429 - loss: 0.5835 - val_accuracy: 0.5000 - val_loss: 0.7424\n",
            "Epoch 6/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - accuracy: 0.7143 - loss: 0.5513 - val_accuracy: 0.5000 - val_loss: 0.6565\n",
            "Epoch 7/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - accuracy: 0.8571 - loss: 0.4851 - val_accuracy: 0.5000 - val_loss: 0.6563\n",
            "Epoch 8/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - accuracy: 0.8571 - loss: 0.4410 - val_accuracy: 0.5000 - val_loss: 0.7249\n",
            "Epoch 9/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.7857 - loss: 0.4270 - val_accuracy: 0.5000 - val_loss: 0.7173\n",
            "Epoch 10/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - accuracy: 0.8571 - loss: 0.3730 - val_accuracy: 0.5000 - val_loss: 0.7233\n",
            "Epoch 11/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - accuracy: 1.0000 - loss: 0.3010 - val_accuracy: 0.5000 - val_loss: 0.7426\n",
            "Epoch 12/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - accuracy: 0.7857 - loss: 0.3123 - val_accuracy: 0.5000 - val_loss: 0.9272\n",
            "Epoch 13/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.8571 - loss: 0.2523 - val_accuracy: 0.5000 - val_loss: 1.0230\n",
            "Epoch 14/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - accuracy: 0.9286 - loss: 0.2167 - val_accuracy: 0.5000 - val_loss: 0.9112\n",
            "Epoch 15/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.9286 - loss: 0.2048 - val_accuracy: 0.5000 - val_loss: 1.1364\n",
            "Epoch 16/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 178ms/step - accuracy: 1.0000 - loss: 0.1420 - val_accuracy: 0.5000 - val_loss: 1.5124\n",
            "Epoch 17/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step - accuracy: 0.9286 - loss: 0.1467 - val_accuracy: 0.5000 - val_loss: 1.4722\n",
            "Epoch 18/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - accuracy: 1.0000 - loss: 0.0906 - val_accuracy: 0.5000 - val_loss: 1.4461\n",
            "Epoch 19/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - accuracy: 1.0000 - loss: 0.1006 - val_accuracy: 0.5000 - val_loss: 2.0459\n",
            "Epoch 20/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 1.0000 - loss: 0.0640 - val_accuracy: 0.5000 - val_loss: 2.3037\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - accuracy: 0.2500 - loss: 3.6598\n",
            "Test accuracy: 0.25\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step\n",
            "AUC: 0.25\n",
            "\n",
            "Model: 1-conv-128-nodes-1-dense-1724925917\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_15\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_15\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_30 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │           \u001b[38;5;34m1,280\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_51 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_30 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_15 (\u001b[38;5;33mFlatten\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m307328\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_21 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │      \u001b[38;5;34m39,338,112\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_52 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_22 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │             \u001b[38;5;34m129\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_53 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_30 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,280</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_51 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_30 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">307328</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │      <span style=\"color: #00af00; text-decoration-color: #00af00\">39,338,112</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_52 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_22 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">129</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_53 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m39,339,521\u001b[0m (150.07 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">39,339,521</span> (150.07 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m39,339,521\u001b[0m (150.07 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">39,339,521</span> (150.07 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.5000 - loss: 0.6904 - val_accuracy: 0.5000 - val_loss: 4.0444\n",
            "Epoch 2/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 367ms/step - accuracy: 0.5000 - loss: 7.3455 - val_accuracy: 0.5000 - val_loss: 2.8996\n",
            "Epoch 3/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 395ms/step - accuracy: 0.5000 - loss: 2.2315 - val_accuracy: 0.5000 - val_loss: 2.3853\n",
            "Epoch 4/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 403ms/step - accuracy: 0.5000 - loss: 1.7817 - val_accuracy: 0.5000 - val_loss: 0.6505\n",
            "Epoch 5/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 381ms/step - accuracy: 0.6429 - loss: 0.5732 - val_accuracy: 0.5000 - val_loss: 0.9239\n",
            "Epoch 6/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 424ms/step - accuracy: 0.5714 - loss: 1.2145 - val_accuracy: 0.5000 - val_loss: 0.8123\n",
            "Epoch 7/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 412ms/step - accuracy: 0.5714 - loss: 0.7973 - val_accuracy: 0.0000e+00 - val_loss: 0.8407\n",
            "Epoch 8/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 396ms/step - accuracy: 0.7857 - loss: 0.4206 - val_accuracy: 0.5000 - val_loss: 1.3390\n",
            "Epoch 9/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 399ms/step - accuracy: 0.6429 - loss: 0.6892 - val_accuracy: 0.0000e+00 - val_loss: 1.4047\n",
            "Epoch 10/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 377ms/step - accuracy: 0.6429 - loss: 0.6791 - val_accuracy: 0.0000e+00 - val_loss: 1.1394\n",
            "Epoch 11/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 362ms/step - accuracy: 0.7857 - loss: 0.4181 - val_accuracy: 0.5000 - val_loss: 1.0709\n",
            "Epoch 12/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 364ms/step - accuracy: 0.8571 - loss: 0.3163 - val_accuracy: 0.5000 - val_loss: 1.2843\n",
            "Epoch 13/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 372ms/step - accuracy: 0.7143 - loss: 0.4382 - val_accuracy: 0.5000 - val_loss: 1.2842\n",
            "Epoch 14/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 370ms/step - accuracy: 0.8571 - loss: 0.3354 - val_accuracy: 0.5000 - val_loss: 1.2446\n",
            "Epoch 15/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 359ms/step - accuracy: 0.9286 - loss: 0.2424 - val_accuracy: 0.0000e+00 - val_loss: 1.2998\n",
            "Epoch 16/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 362ms/step - accuracy: 0.7857 - loss: 0.2718 - val_accuracy: 0.0000e+00 - val_loss: 1.3693\n",
            "Epoch 17/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 372ms/step - accuracy: 0.7857 - loss: 0.2875 - val_accuracy: 0.0000e+00 - val_loss: 1.4053\n",
            "Epoch 18/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 359ms/step - accuracy: 0.9286 - loss: 0.2245 - val_accuracy: 0.5000 - val_loss: 1.5617\n",
            "Epoch 19/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 368ms/step - accuracy: 0.9286 - loss: 0.2119 - val_accuracy: 0.5000 - val_loss: 1.7091\n",
            "Epoch 20/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 366ms/step - accuracy: 0.8571 - loss: 0.2431 - val_accuracy: 0.5000 - val_loss: 1.6712\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - accuracy: 0.0000e+00 - loss: 4.4853\n",
            "Test accuracy: 0.0\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step\n",
            "AUC: 0.0\n",
            "\n",
            "Model: 2-conv-128-nodes-1-dense-1724925929\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_16\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_16\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_31 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │           \u001b[38;5;34m1,280\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_54 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_31 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_32 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │         \u001b[38;5;34m147,584\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_55 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_32 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_16 (\u001b[38;5;33mFlatten\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m67712\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_23 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │       \u001b[38;5;34m8,667,264\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_56 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_24 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │             \u001b[38;5;34m129\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_57 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_31 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,280</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_54 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_31 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_32 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_55 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_32 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">67712</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_23 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │       <span style=\"color: #00af00; text-decoration-color: #00af00\">8,667,264</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_56 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_24 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">129</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_57 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,816,257\u001b[0m (33.63 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">8,816,257</span> (33.63 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,816,257\u001b[0m (33.63 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">8,816,257</span> (33.63 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3s/step - accuracy: 0.5000 - loss: 0.6945 - val_accuracy: 0.5000 - val_loss: 0.7413\n",
            "Epoch 2/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 328ms/step - accuracy: 0.5000 - loss: 0.9647 - val_accuracy: 0.5000 - val_loss: 1.7468\n",
            "Epoch 3/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 330ms/step - accuracy: 0.5000 - loss: 1.2366 - val_accuracy: 0.5000 - val_loss: 0.8276\n",
            "Epoch 4/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 342ms/step - accuracy: 0.5000 - loss: 0.6558 - val_accuracy: 0.5000 - val_loss: 0.6979\n",
            "Epoch 5/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 370ms/step - accuracy: 0.6429 - loss: 0.6198 - val_accuracy: 0.5000 - val_loss: 0.7630\n",
            "Epoch 6/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 381ms/step - accuracy: 0.9286 - loss: 0.5446 - val_accuracy: 0.0000e+00 - val_loss: 1.0549\n",
            "Epoch 7/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 366ms/step - accuracy: 0.6429 - loss: 0.5616 - val_accuracy: 0.5000 - val_loss: 0.8982\n",
            "Epoch 8/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 359ms/step - accuracy: 0.7857 - loss: 0.4426 - val_accuracy: 0.5000 - val_loss: 0.9769\n",
            "Epoch 9/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 364ms/step - accuracy: 0.8571 - loss: 0.3817 - val_accuracy: 0.0000e+00 - val_loss: 1.1491\n",
            "Epoch 10/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 364ms/step - accuracy: 0.7857 - loss: 0.3643 - val_accuracy: 0.5000 - val_loss: 1.2691\n",
            "Epoch 11/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 362ms/step - accuracy: 0.8571 - loss: 0.2972 - val_accuracy: 0.5000 - val_loss: 1.4324\n",
            "Epoch 12/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 359ms/step - accuracy: 0.9286 - loss: 0.2423 - val_accuracy: 0.0000e+00 - val_loss: 1.5329\n",
            "Epoch 13/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 337ms/step - accuracy: 0.8571 - loss: 0.2462 - val_accuracy: 0.5000 - val_loss: 1.8257\n",
            "Epoch 14/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 341ms/step - accuracy: 1.0000 - loss: 0.1564 - val_accuracy: 0.5000 - val_loss: 2.1895\n",
            "Epoch 15/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 324ms/step - accuracy: 0.9286 - loss: 0.1443 - val_accuracy: 0.5000 - val_loss: 2.1066\n",
            "Epoch 16/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 329ms/step - accuracy: 0.9286 - loss: 0.1188 - val_accuracy: 0.5000 - val_loss: 2.5430\n",
            "Epoch 17/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 331ms/step - accuracy: 1.0000 - loss: 0.0718 - val_accuracy: 0.5000 - val_loss: 3.0536\n",
            "Epoch 18/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 328ms/step - accuracy: 1.0000 - loss: 0.0715 - val_accuracy: 0.5000 - val_loss: 3.0588\n",
            "Epoch 19/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 330ms/step - accuracy: 1.0000 - loss: 0.0462 - val_accuracy: 0.5000 - val_loss: 3.3421\n",
            "Epoch 20/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 334ms/step - accuracy: 1.0000 - loss: 0.0384 - val_accuracy: 0.5000 - val_loss: 4.0341\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.5000 - loss: 6.4589\n",
            "Test accuracy: 0.5\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step\n",
            "AUC: 0.25\n",
            "\n",
            "Model: 3-conv-128-nodes-1-dense-1724925939\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_17\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_17\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_33 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │           \u001b[38;5;34m1,280\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_58 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_33 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_34 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │         \u001b[38;5;34m147,584\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_59 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_34 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_35 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │         \u001b[38;5;34m147,584\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_60 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_35 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_17 (\u001b[38;5;33mFlatten\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12800\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_25 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │       \u001b[38;5;34m1,638,528\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_61 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_26 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │             \u001b[38;5;34m129\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_62 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_33 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,280</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_58 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_33 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_34 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_59 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_34 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_35 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_60 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_35 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12800</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_25 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │       <span style=\"color: #00af00; text-decoration-color: #00af00\">1,638,528</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_61 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_26 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">129</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_62 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,935,105\u001b[0m (7.38 MB)\n"
            ],
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,935,105</span> (7.38 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,935,105\u001b[0m (7.38 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,935,105</span> (7.38 MB)\n",
              "</pre>\n"
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          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
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            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
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          "metadata": {}
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          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.5000 - loss: 0.6906 - val_accuracy: 0.5000 - val_loss: 0.6910\n",
            "Epoch 2/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 328ms/step - accuracy: 0.8571 - loss: 0.6472 - val_accuracy: 0.5000 - val_loss: 0.7321\n",
            "Epoch 3/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 327ms/step - accuracy: 0.7143 - loss: 0.5818 - val_accuracy: 0.5000 - val_loss: 0.9747\n",
            "Epoch 4/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 332ms/step - accuracy: 0.5000 - loss: 1.5282 - val_accuracy: 0.5000 - val_loss: 0.6919\n",
            "Epoch 5/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 319ms/step - accuracy: 0.9286 - loss: 0.5177 - val_accuracy: 0.5000 - val_loss: 1.3206\n",
            "Epoch 6/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 323ms/step - accuracy: 0.5000 - loss: 0.8905 - val_accuracy: 0.5000 - val_loss: 0.8607\n",
            "Epoch 7/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 323ms/step - accuracy: 0.5714 - loss: 0.5963 - val_accuracy: 0.0000e+00 - val_loss: 0.7592\n",
            "Epoch 8/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 328ms/step - accuracy: 0.7143 - loss: 0.5840 - val_accuracy: 0.0000e+00 - val_loss: 0.7314\n",
            "Epoch 9/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 319ms/step - accuracy: 0.7143 - loss: 0.5830 - val_accuracy: 0.5000 - val_loss: 0.7281\n",
            "Epoch 10/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 323ms/step - accuracy: 0.8571 - loss: 0.5574 - val_accuracy: 0.0000e+00 - val_loss: 0.7701\n",
            "Epoch 11/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 322ms/step - accuracy: 0.9286 - loss: 0.5083 - val_accuracy: 0.0000e+00 - val_loss: 0.8222\n",
            "Epoch 12/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 322ms/step - accuracy: 0.8571 - loss: 0.4429 - val_accuracy: 0.5000 - val_loss: 0.8750\n",
            "Epoch 13/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 336ms/step - accuracy: 0.9286 - loss: 0.3593 - val_accuracy: 0.5000 - val_loss: 1.0408\n",
            "Epoch 14/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 346ms/step - accuracy: 0.9286 - loss: 0.2765 - val_accuracy: 0.5000 - val_loss: 1.3593\n",
            "Epoch 15/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 358ms/step - accuracy: 0.8571 - loss: 0.2035 - val_accuracy: 0.5000 - val_loss: 1.8061\n",
            "Epoch 16/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 357ms/step - accuracy: 0.9286 - loss: 0.1501 - val_accuracy: 0.5000 - val_loss: 3.2564\n",
            "Epoch 17/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 357ms/step - accuracy: 0.9286 - loss: 0.2791 - val_accuracy: 0.0000e+00 - val_loss: 2.1478\n",
            "Epoch 18/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 353ms/step - accuracy: 0.7857 - loss: 1.0666 - val_accuracy: 0.5000 - val_loss: 3.4183\n",
            "Epoch 19/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 371ms/step - accuracy: 0.9286 - loss: 0.1211 - val_accuracy: 0.5000 - val_loss: 4.3884\n",
            "Epoch 20/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 367ms/step - accuracy: 0.8571 - loss: 0.5840 - val_accuracy: 0.5000 - val_loss: 3.7646\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.2500 - loss: 4.4516\n",
            "Test accuracy: 0.25\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step\n",
            "AUC: 0.0\n",
            "\n",
            "Model: 1-conv-32-nodes-2-dense-1724925949\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_18\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_18\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_36 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m320\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_63 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_36 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_18 (\u001b[38;5;33mFlatten\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m76832\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_27 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │       \u001b[38;5;34m2,458,656\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_64 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_28 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │           \u001b[38;5;34m1,056\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_65 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_29 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m33\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_66 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_36 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_63 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_36 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_18 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">76832</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_27 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,458,656</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_64 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_28 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,056</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_65 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_29 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_66 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,460,065\u001b[0m (9.38 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,460,065</span> (9.38 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,460,065\u001b[0m (9.38 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,460,065</span> (9.38 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.5000 - loss: 0.6981 - val_accuracy: 0.5000 - val_loss: 0.6706\n",
            "Epoch 2/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - accuracy: 0.6429 - loss: 0.6523 - val_accuracy: 0.5000 - val_loss: 1.2264\n",
            "Epoch 3/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - accuracy: 0.5714 - loss: 0.8842 - val_accuracy: 0.5000 - val_loss: 0.6715\n",
            "Epoch 4/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - accuracy: 0.6429 - loss: 0.6403 - val_accuracy: 0.5000 - val_loss: 0.6606\n",
            "Epoch 5/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - accuracy: 0.6429 - loss: 0.6298 - val_accuracy: 0.5000 - val_loss: 0.7236\n",
            "Epoch 6/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - accuracy: 0.7143 - loss: 0.5198 - val_accuracy: 0.5000 - val_loss: 0.8782\n",
            "Epoch 7/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - accuracy: 0.6429 - loss: 0.5386 - val_accuracy: 0.0000e+00 - val_loss: 0.8240\n",
            "Epoch 8/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - accuracy: 0.8571 - loss: 0.4913 - val_accuracy: 0.5000 - val_loss: 0.7463\n",
            "Epoch 9/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - accuracy: 0.7857 - loss: 0.4642 - val_accuracy: 0.5000 - val_loss: 0.7576\n",
            "Epoch 10/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - accuracy: 0.7143 - loss: 0.4593 - val_accuracy: 0.5000 - val_loss: 0.8017\n",
            "Epoch 11/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - accuracy: 0.8571 - loss: 0.4149 - val_accuracy: 0.0000e+00 - val_loss: 0.8978\n",
            "Epoch 12/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - accuracy: 0.7857 - loss: 0.4130 - val_accuracy: 0.0000e+00 - val_loss: 0.9131\n",
            "Epoch 13/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - accuracy: 0.7857 - loss: 0.3919 - val_accuracy: 0.5000 - val_loss: 0.8838\n",
            "Epoch 14/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - accuracy: 1.0000 - loss: 0.3574 - val_accuracy: 0.5000 - val_loss: 0.9305\n",
            "Epoch 15/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - accuracy: 0.8571 - loss: 0.3511 - val_accuracy: 0.5000 - val_loss: 0.9697\n",
            "Epoch 16/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - accuracy: 0.9286 - loss: 0.3298 - val_accuracy: 0.5000 - val_loss: 0.9911\n",
            "Epoch 17/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - accuracy: 1.0000 - loss: 0.3063 - val_accuracy: 0.5000 - val_loss: 1.0317\n",
            "Epoch 18/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - accuracy: 0.8571 - loss: 0.3019 - val_accuracy: 0.5000 - val_loss: 1.0783\n",
            "Epoch 19/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - accuracy: 1.0000 - loss: 0.2796 - val_accuracy: 0.5000 - val_loss: 1.1630\n",
            "Epoch 20/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - accuracy: 0.9286 - loss: 0.2652 - val_accuracy: 0.5000 - val_loss: 1.2594\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.0000e+00 - loss: 3.1111\n",
            "Test accuracy: 0.0\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step\n",
            "AUC: 0.0\n",
            "\n",
            "Model: 2-conv-32-nodes-2-dense-1724925953\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_19\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_19\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_37 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m320\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_67 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_37 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_38 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │           \u001b[38;5;34m9,248\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_68 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_38 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_19 (\u001b[38;5;33mFlatten\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16928\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_30 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │         \u001b[38;5;34m541,728\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_69 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_31 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │           \u001b[38;5;34m1,056\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_70 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_32 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m33\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_71 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_37 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_67 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_37 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_38 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_68 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_38 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_19 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16928</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_30 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │         <span style=\"color: #00af00; text-decoration-color: #00af00\">541,728</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_69 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_31 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,056</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_70 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_32 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_71 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m552,385\u001b[0m (2.11 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">552,385</span> (2.11 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m552,385\u001b[0m (2.11 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">552,385</span> (2.11 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.5000 - loss: 0.6932 - val_accuracy: 0.5000 - val_loss: 0.7474\n",
            "Epoch 2/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - accuracy: 0.5000 - loss: 0.6832 - val_accuracy: 0.5000 - val_loss: 0.6833\n",
            "Epoch 3/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - accuracy: 0.6429 - loss: 0.6670 - val_accuracy: 0.5000 - val_loss: 0.7238\n",
            "Epoch 4/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - accuracy: 0.7143 - loss: 0.6313 - val_accuracy: 0.5000 - val_loss: 0.7865\n",
            "Epoch 5/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - accuracy: 0.5714 - loss: 0.6044 - val_accuracy: 0.5000 - val_loss: 0.6954\n",
            "Epoch 6/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - accuracy: 0.7857 - loss: 0.5762 - val_accuracy: 0.5000 - val_loss: 0.7143\n",
            "Epoch 7/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - accuracy: 0.7857 - loss: 0.5454 - val_accuracy: 0.5000 - val_loss: 0.8510\n",
            "Epoch 8/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - accuracy: 0.7143 - loss: 0.5373 - val_accuracy: 0.5000 - val_loss: 0.7089\n",
            "Epoch 9/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - accuracy: 0.7857 - loss: 0.5091 - val_accuracy: 0.5000 - val_loss: 0.7547\n",
            "Epoch 10/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - accuracy: 0.7857 - loss: 0.4664 - val_accuracy: 0.0000e+00 - val_loss: 0.9424\n",
            "Epoch 11/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - accuracy: 0.7857 - loss: 0.4817 - val_accuracy: 0.5000 - val_loss: 0.7698\n",
            "Epoch 12/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - accuracy: 0.7857 - loss: 0.4213 - val_accuracy: 0.5000 - val_loss: 0.7752\n",
            "Epoch 13/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - accuracy: 0.7857 - loss: 0.4085 - val_accuracy: 0.0000e+00 - val_loss: 0.9157\n",
            "Epoch 14/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - accuracy: 0.8571 - loss: 0.3866 - val_accuracy: 0.0000e+00 - val_loss: 0.8721\n",
            "Epoch 15/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - accuracy: 0.9286 - loss: 0.3466 - val_accuracy: 0.5000 - val_loss: 0.8308\n",
            "Epoch 16/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - accuracy: 0.7857 - loss: 0.3432 - val_accuracy: 0.5000 - val_loss: 0.8599\n",
            "Epoch 17/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - accuracy: 1.0000 - loss: 0.2977 - val_accuracy: 0.0000e+00 - val_loss: 0.9326\n",
            "Epoch 18/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - accuracy: 0.9286 - loss: 0.3021 - val_accuracy: 0.5000 - val_loss: 0.8789\n",
            "Epoch 19/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - accuracy: 1.0000 - loss: 0.2584 - val_accuracy: 0.5000 - val_loss: 0.8982\n",
            "Epoch 20/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - accuracy: 0.9286 - loss: 0.2545 - val_accuracy: 0.5000 - val_loss: 0.9057\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - accuracy: 0.2500 - loss: 2.9615\n",
            "Test accuracy: 0.25\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step\n",
            "AUC: 0.25\n",
            "\n",
            "Model: 3-conv-32-nodes-2-dense-1724925957\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_20\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_20\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_39 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m320\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_72 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_39 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_40 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │           \u001b[38;5;34m9,248\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_73 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_40 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_41 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │           \u001b[38;5;34m9,248\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_74 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_41 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_20 (\u001b[38;5;33mFlatten\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3200\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_33 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │         \u001b[38;5;34m102,432\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_75 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_34 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │           \u001b[38;5;34m1,056\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_76 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_35 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m33\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_77 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_39 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_72 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_39 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_40 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_73 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_40 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_41 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_74 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_41 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3200</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_33 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │         <span style=\"color: #00af00; text-decoration-color: #00af00\">102,432</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_75 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_34 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,056</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_76 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_35 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_77 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m122,337\u001b[0m (477.88 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">122,337</span> (477.88 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m122,337\u001b[0m (477.88 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">122,337</span> (477.88 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.5000 - loss: 0.6944 - val_accuracy: 0.5000 - val_loss: 0.7011\n",
            "Epoch 2/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - accuracy: 0.5000 - loss: 0.6899 - val_accuracy: 0.5000 - val_loss: 0.6975\n",
            "Epoch 3/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - accuracy: 0.6429 - loss: 0.6831 - val_accuracy: 0.0000e+00 - val_loss: 0.6963\n",
            "Epoch 4/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - accuracy: 0.8571 - loss: 0.6752 - val_accuracy: 0.5000 - val_loss: 0.7071\n",
            "Epoch 5/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - accuracy: 0.7143 - loss: 0.6666 - val_accuracy: 0.0000e+00 - val_loss: 0.7035\n",
            "Epoch 6/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - accuracy: 0.8571 - loss: 0.6532 - val_accuracy: 0.0000e+00 - val_loss: 0.7083\n",
            "Epoch 7/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - accuracy: 0.9286 - loss: 0.6369 - val_accuracy: 0.0000e+00 - val_loss: 0.7201\n",
            "Epoch 8/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - accuracy: 0.7857 - loss: 0.6183 - val_accuracy: 0.0000e+00 - val_loss: 0.7177\n",
            "Epoch 9/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - accuracy: 0.8571 - loss: 0.5963 - val_accuracy: 0.5000 - val_loss: 0.7224\n",
            "Epoch 10/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - accuracy: 0.9286 - loss: 0.5680 - val_accuracy: 0.5000 - val_loss: 0.7165\n",
            "Epoch 11/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - accuracy: 0.7857 - loss: 0.5446 - val_accuracy: 0.0000e+00 - val_loss: 0.7777\n",
            "Epoch 12/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - accuracy: 0.7857 - loss: 0.5145 - val_accuracy: 0.5000 - val_loss: 0.7121\n",
            "Epoch 13/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - accuracy: 0.8571 - loss: 0.4802 - val_accuracy: 0.5000 - val_loss: 0.7144\n",
            "Epoch 14/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - accuracy: 0.8571 - loss: 0.4496 - val_accuracy: 0.0000e+00 - val_loss: 0.7733\n",
            "Epoch 15/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - accuracy: 0.8571 - loss: 0.4234 - val_accuracy: 0.5000 - val_loss: 0.7419\n",
            "Epoch 16/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - accuracy: 0.9286 - loss: 0.3827 - val_accuracy: 0.5000 - val_loss: 0.7746\n",
            "Epoch 17/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - accuracy: 0.9286 - loss: 0.3467 - val_accuracy: 0.5000 - val_loss: 0.7935\n",
            "Epoch 18/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - accuracy: 0.9286 - loss: 0.3172 - val_accuracy: 0.5000 - val_loss: 0.8397\n",
            "Epoch 19/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - accuracy: 0.9286 - loss: 0.2901 - val_accuracy: 0.0000e+00 - val_loss: 0.8977\n",
            "Epoch 20/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - accuracy: 0.7857 - loss: 0.3495 - val_accuracy: 0.5000 - val_loss: 0.9880\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - accuracy: 0.2500 - loss: 2.4552\n",
            "Test accuracy: 0.25\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step\n",
            "AUC: 0.25\n",
            "\n",
            "Model: 1-conv-64-nodes-2-dense-1724925962\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_21\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_21\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_42 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │             \u001b[38;5;34m640\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_78 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_42 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_21 (\u001b[38;5;33mFlatten\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m153664\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_36 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │       \u001b[38;5;34m9,834,560\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_79 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_37 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │           \u001b[38;5;34m4,160\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_80 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_38 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m65\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_81 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_42 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">640</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_78 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_42 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">153664</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_36 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │       <span style=\"color: #00af00; text-decoration-color: #00af00\">9,834,560</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_79 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_37 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,160</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_80 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_38 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_81 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m9,839,425\u001b[0m (37.53 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">9,839,425</span> (37.53 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m9,839,425\u001b[0m (37.53 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">9,839,425</span> (37.53 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.5000 - loss: 0.6934 - val_accuracy: 0.5000 - val_loss: 1.1609\n",
            "Epoch 2/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 177ms/step - accuracy: 0.5000 - loss: 2.0031 - val_accuracy: 0.5000 - val_loss: 0.9971\n",
            "Epoch 3/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - accuracy: 0.5000 - loss: 0.7435 - val_accuracy: 0.5000 - val_loss: 0.9426\n",
            "Epoch 4/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - accuracy: 0.5714 - loss: 0.6757 - val_accuracy: 0.5000 - val_loss: 0.6919\n",
            "Epoch 5/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - accuracy: 0.7143 - loss: 0.5682 - val_accuracy: 0.5000 - val_loss: 0.6838\n",
            "Epoch 6/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - accuracy: 0.6429 - loss: 0.5982 - val_accuracy: 0.5000 - val_loss: 0.7098\n",
            "Epoch 7/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - accuracy: 0.7857 - loss: 0.5078 - val_accuracy: 0.0000e+00 - val_loss: 0.8749\n",
            "Epoch 8/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - accuracy: 0.7857 - loss: 0.4996 - val_accuracy: 0.0000e+00 - val_loss: 0.8811\n",
            "Epoch 9/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - accuracy: 0.8571 - loss: 0.4558 - val_accuracy: 0.5000 - val_loss: 0.8154\n",
            "Epoch 10/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - accuracy: 0.8571 - loss: 0.4027 - val_accuracy: 0.5000 - val_loss: 0.8399\n",
            "Epoch 11/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - accuracy: 0.7857 - loss: 0.3907 - val_accuracy: 0.5000 - val_loss: 0.8931\n",
            "Epoch 12/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - accuracy: 0.8571 - loss: 0.3492 - val_accuracy: 0.5000 - val_loss: 0.9737\n",
            "Epoch 13/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - accuracy: 0.9286 - loss: 0.3182 - val_accuracy: 0.5000 - val_loss: 1.0129\n",
            "Epoch 14/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - accuracy: 0.9286 - loss: 0.2945 - val_accuracy: 0.5000 - val_loss: 1.0341\n",
            "Epoch 15/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - accuracy: 0.9286 - loss: 0.2579 - val_accuracy: 0.5000 - val_loss: 1.1018\n",
            "Epoch 16/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - accuracy: 0.9286 - loss: 0.2415 - val_accuracy: 0.5000 - val_loss: 1.0921\n",
            "Epoch 17/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - accuracy: 0.9286 - loss: 0.2441 - val_accuracy: 0.5000 - val_loss: 1.3349\n",
            "Epoch 18/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - accuracy: 0.8571 - loss: 0.2629 - val_accuracy: 0.5000 - val_loss: 1.1553\n",
            "Epoch 19/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - accuracy: 1.0000 - loss: 0.1855 - val_accuracy: 0.5000 - val_loss: 1.1674\n",
            "Epoch 20/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - accuracy: 0.9286 - loss: 0.2036 - val_accuracy: 0.5000 - val_loss: 1.2914\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - accuracy: 0.2500 - loss: 4.3093\n",
            "Test accuracy: 0.25\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step\n",
            "AUC: 0.0\n",
            "\n",
            "Model: 2-conv-64-nodes-2-dense-1724925968\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_22\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_22\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_43 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │             \u001b[38;5;34m640\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_82 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_43 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_44 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m36,928\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_83 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_44 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_22 (\u001b[38;5;33mFlatten\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33856\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_39 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │       \u001b[38;5;34m2,166,848\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_84 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_40 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │           \u001b[38;5;34m4,160\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_85 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_41 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m65\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_86 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_43 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">640</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_82 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_43 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_44 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_83 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_44 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_22 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33856</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_39 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,166,848</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_84 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_40 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,160</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_85 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_41 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_86 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,208,641\u001b[0m (8.43 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,208,641</span> (8.43 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,208,641\u001b[0m (8.43 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,208,641</span> (8.43 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.5000 - loss: 0.6949 - val_accuracy: 0.5000 - val_loss: 0.6669\n",
            "Epoch 2/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - accuracy: 0.5000 - loss: 0.6855 - val_accuracy: 0.5000 - val_loss: 0.7395\n",
            "Epoch 3/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - accuracy: 0.6429 - loss: 0.6500 - val_accuracy: 0.5000 - val_loss: 0.7061\n",
            "Epoch 4/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - accuracy: 0.7857 - loss: 0.5968 - val_accuracy: 0.0000e+00 - val_loss: 0.8000\n",
            "Epoch 5/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - accuracy: 0.7857 - loss: 0.5383 - val_accuracy: 0.5000 - val_loss: 0.7726\n",
            "Epoch 6/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - accuracy: 0.7143 - loss: 0.5301 - val_accuracy: 0.0000e+00 - val_loss: 1.1114\n",
            "Epoch 7/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 176ms/step - accuracy: 0.6429 - loss: 0.5680 - val_accuracy: 0.5000 - val_loss: 0.8650\n",
            "Epoch 8/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 174ms/step - accuracy: 0.7857 - loss: 0.4241 - val_accuracy: 0.5000 - val_loss: 0.8748\n",
            "Epoch 9/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - accuracy: 0.7857 - loss: 0.4566 - val_accuracy: 0.5000 - val_loss: 0.9154\n",
            "Epoch 10/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 176ms/step - accuracy: 0.8571 - loss: 0.3755 - val_accuracy: 0.0000e+00 - val_loss: 1.1197\n",
            "Epoch 11/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 177ms/step - accuracy: 0.7857 - loss: 0.3885 - val_accuracy: 0.0000e+00 - val_loss: 1.0979\n",
            "Epoch 12/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step - accuracy: 0.9286 - loss: 0.3026 - val_accuracy: 0.5000 - val_loss: 1.1416\n",
            "Epoch 13/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - accuracy: 0.9286 - loss: 0.2621 - val_accuracy: 0.5000 - val_loss: 1.2475\n",
            "Epoch 14/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 181ms/step - accuracy: 0.9286 - loss: 0.2128 - val_accuracy: 0.0000e+00 - val_loss: 1.3511\n",
            "Epoch 15/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 174ms/step - accuracy: 0.7857 - loss: 0.2333 - val_accuracy: 0.5000 - val_loss: 1.5426\n",
            "Epoch 16/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - accuracy: 0.9286 - loss: 0.1608 - val_accuracy: 0.5000 - val_loss: 1.7241\n",
            "Epoch 17/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 179ms/step - accuracy: 0.9286 - loss: 0.1495 - val_accuracy: 0.5000 - val_loss: 1.5764\n",
            "Epoch 18/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 186ms/step - accuracy: 0.9286 - loss: 0.1266 - val_accuracy: 0.5000 - val_loss: 1.7897\n",
            "Epoch 19/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 178ms/step - accuracy: 1.0000 - loss: 0.0835 - val_accuracy: 0.5000 - val_loss: 2.2262\n",
            "Epoch 20/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 178ms/step - accuracy: 0.9286 - loss: 0.0933 - val_accuracy: 0.5000 - val_loss: 2.1848\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - accuracy: 0.2500 - loss: 4.5446\n",
            "Test accuracy: 0.25\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step\n",
            "AUC: 0.25\n",
            "\n",
            "Model: 3-conv-64-nodes-2-dense-1724925974\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_23\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_23\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_45 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │             \u001b[38;5;34m640\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_87 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_45 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_46 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m36,928\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_88 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_46 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_47 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m36,928\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_89 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_47 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_23 (\u001b[38;5;33mFlatten\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6400\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_42 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │         \u001b[38;5;34m409,664\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_90 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_43 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │           \u001b[38;5;34m4,160\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_91 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_44 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m65\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_92 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_45 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">640</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_87 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_45 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_46 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_88 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_46 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_47 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_89 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_47 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_23 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6400</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_42 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │         <span style=\"color: #00af00; text-decoration-color: #00af00\">409,664</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_90 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_43 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,160</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_91 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_44 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_92 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m488,385\u001b[0m (1.86 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">488,385</span> (1.86 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m488,385\u001b[0m (1.86 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">488,385</span> (1.86 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.5000 - loss: 0.6955 - val_accuracy: 0.5000 - val_loss: 0.7085\n",
            "Epoch 2/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - accuracy: 0.5000 - loss: 0.6848 - val_accuracy: 0.5000 - val_loss: 0.6645\n",
            "Epoch 3/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - accuracy: 0.5714 - loss: 0.6760 - val_accuracy: 0.5000 - val_loss: 0.7030\n",
            "Epoch 4/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - accuracy: 0.7143 - loss: 0.6574 - val_accuracy: 0.5000 - val_loss: 0.7113\n",
            "Epoch 5/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - accuracy: 0.7143 - loss: 0.6336 - val_accuracy: 0.5000 - val_loss: 0.6718\n",
            "Epoch 6/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step - accuracy: 0.7143 - loss: 0.5974 - val_accuracy: 0.0000e+00 - val_loss: 0.7160\n",
            "Epoch 7/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - accuracy: 0.7857 - loss: 0.5607 - val_accuracy: 0.5000 - val_loss: 0.6437\n",
            "Epoch 8/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - accuracy: 0.7143 - loss: 0.5307 - val_accuracy: 0.5000 - val_loss: 0.8006\n",
            "Epoch 9/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - accuracy: 0.7857 - loss: 0.5197 - val_accuracy: 0.5000 - val_loss: 0.6651\n",
            "Epoch 10/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 176ms/step - accuracy: 0.7857 - loss: 0.4580 - val_accuracy: 0.5000 - val_loss: 0.6821\n",
            "Epoch 11/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - accuracy: 0.8571 - loss: 0.4085 - val_accuracy: 0.0000e+00 - val_loss: 0.8006\n",
            "Epoch 12/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - accuracy: 0.7857 - loss: 0.4182 - val_accuracy: 0.5000 - val_loss: 0.7436\n",
            "Epoch 13/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - accuracy: 0.7857 - loss: 0.3594 - val_accuracy: 0.5000 - val_loss: 0.7709\n",
            "Epoch 14/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - accuracy: 0.8571 - loss: 0.3109 - val_accuracy: 0.5000 - val_loss: 0.8160\n",
            "Epoch 15/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - accuracy: 0.8571 - loss: 0.3290 - val_accuracy: 0.5000 - val_loss: 0.8699\n",
            "Epoch 16/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - accuracy: 0.8571 - loss: 0.2461 - val_accuracy: 0.5000 - val_loss: 0.9890\n",
            "Epoch 17/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 172ms/step - accuracy: 0.8571 - loss: 0.2362 - val_accuracy: 0.5000 - val_loss: 0.9394\n",
            "Epoch 18/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - accuracy: 0.8571 - loss: 0.1998 - val_accuracy: 0.5000 - val_loss: 1.0584\n",
            "Epoch 19/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - accuracy: 1.0000 - loss: 0.1481 - val_accuracy: 0.5000 - val_loss: 1.3235\n",
            "Epoch 20/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - accuracy: 0.9286 - loss: 0.1455 - val_accuracy: 0.5000 - val_loss: 1.2320\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - accuracy: 0.2500 - loss: 3.6393\n",
            "Test accuracy: 0.25\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step\n",
            "AUC: 0.25\n",
            "\n",
            "Model: 1-conv-128-nodes-2-dense-1724925981\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_24\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_24\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_48 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │           \u001b[38;5;34m1,280\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_93 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_48 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_24 (\u001b[38;5;33mFlatten\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m307328\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_45 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │      \u001b[38;5;34m39,338,112\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_94 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_46 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │          \u001b[38;5;34m16,512\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_95 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_47 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │             \u001b[38;5;34m129\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_96 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_48 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,280</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_93 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_48 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_24 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">307328</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_45 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │      <span style=\"color: #00af00; text-decoration-color: #00af00\">39,338,112</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_94 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_46 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │          <span style=\"color: #00af00; text-decoration-color: #00af00\">16,512</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_95 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_47 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">129</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_96 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m39,356,033\u001b[0m (150.13 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">39,356,033</span> (150.13 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m39,356,033\u001b[0m (150.13 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">39,356,033</span> (150.13 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.5000 - loss: 0.6931 - val_accuracy: 0.5000 - val_loss: 0.8978\n",
            "Epoch 2/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 379ms/step - accuracy: 0.6429 - loss: 0.6142 - val_accuracy: 0.5000 - val_loss: 3.5043\n",
            "Epoch 3/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 401ms/step - accuracy: 0.5000 - loss: 6.4347 - val_accuracy: 0.5000 - val_loss: 1.5891\n",
            "Epoch 4/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 407ms/step - accuracy: 0.5000 - loss: 2.4515 - val_accuracy: 0.5000 - val_loss: 2.6100\n",
            "Epoch 5/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 396ms/step - accuracy: 0.5000 - loss: 1.8436 - val_accuracy: 0.5000 - val_loss: 2.5573\n",
            "Epoch 6/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 401ms/step - accuracy: 0.5000 - loss: 1.7871 - val_accuracy: 0.5000 - val_loss: 1.1758\n",
            "Epoch 7/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 403ms/step - accuracy: 0.5714 - loss: 0.6607 - val_accuracy: 0.5000 - val_loss: 0.8127\n",
            "Epoch 8/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 412ms/step - accuracy: 0.6429 - loss: 0.7381 - val_accuracy: 0.5000 - val_loss: 0.8123\n",
            "Epoch 9/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 407ms/step - accuracy: 0.5714 - loss: 0.7271 - val_accuracy: 0.5000 - val_loss: 0.7876\n",
            "Epoch 10/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 386ms/step - accuracy: 0.8571 - loss: 0.4615 - val_accuracy: 0.0000e+00 - val_loss: 0.9555\n",
            "Epoch 11/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 375ms/step - accuracy: 0.6429 - loss: 0.5297 - val_accuracy: 0.0000e+00 - val_loss: 1.0103\n",
            "Epoch 12/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 372ms/step - accuracy: 0.6429 - loss: 0.5560 - val_accuracy: 0.0000e+00 - val_loss: 0.8894\n",
            "Epoch 13/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 372ms/step - accuracy: 0.7143 - loss: 0.4627 - val_accuracy: 0.5000 - val_loss: 0.8732\n",
            "Epoch 14/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 356ms/step - accuracy: 0.8571 - loss: 0.4252 - val_accuracy: 0.5000 - val_loss: 0.9717\n",
            "Epoch 15/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 354ms/step - accuracy: 0.7857 - loss: 0.3962 - val_accuracy: 0.5000 - val_loss: 0.9777\n",
            "Epoch 16/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 390ms/step - accuracy: 0.8571 - loss: 0.3440 - val_accuracy: 0.0000e+00 - val_loss: 1.0591\n",
            "Epoch 17/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 371ms/step - accuracy: 0.7857 - loss: 0.3599 - val_accuracy: 0.5000 - val_loss: 1.1415\n",
            "Epoch 18/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 369ms/step - accuracy: 1.0000 - loss: 0.2928 - val_accuracy: 0.5000 - val_loss: 1.3012\n",
            "Epoch 19/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 378ms/step - accuracy: 0.8571 - loss: 0.2870 - val_accuracy: 0.5000 - val_loss: 1.3656\n",
            "Epoch 20/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 373ms/step - accuracy: 1.0000 - loss: 0.2445 - val_accuracy: 0.5000 - val_loss: 1.4179\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - accuracy: 0.2500 - loss: 3.9226\n",
            "Test accuracy: 0.25\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step\n",
            "AUC: 0.0\n",
            "\n",
            "Model: 2-conv-128-nodes-2-dense-1724925993\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_25\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_25\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_49 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │           \u001b[38;5;34m1,280\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_97 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_49 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_50 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │         \u001b[38;5;34m147,584\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_98 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_50 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_25 (\u001b[38;5;33mFlatten\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m67712\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_48 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │       \u001b[38;5;34m8,667,264\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_99 (\u001b[38;5;33mActivation\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_49 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │          \u001b[38;5;34m16,512\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_100 (\u001b[38;5;33mActivation\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_50 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │             \u001b[38;5;34m129\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_101 (\u001b[38;5;33mActivation\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_49 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,280</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_97 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_49 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_50 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_98 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_50 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_25 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">67712</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_48 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │       <span style=\"color: #00af00; text-decoration-color: #00af00\">8,667,264</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_99 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_49 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │          <span style=\"color: #00af00; text-decoration-color: #00af00\">16,512</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_100 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_50 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">129</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_101 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,832,769\u001b[0m (33.69 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">8,832,769</span> (33.69 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,832,769\u001b[0m (33.69 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">8,832,769</span> (33.69 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.4286 - loss: 0.6939 - val_accuracy: 0.5000 - val_loss: 0.6673\n",
            "Epoch 2/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 342ms/step - accuracy: 0.5714 - loss: 0.6489 - val_accuracy: 0.5000 - val_loss: 1.9185\n",
            "Epoch 3/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 335ms/step - accuracy: 0.5000 - loss: 1.3959 - val_accuracy: 0.5000 - val_loss: 0.6714\n",
            "Epoch 4/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 349ms/step - accuracy: 0.5714 - loss: 0.6234 - val_accuracy: 0.5000 - val_loss: 0.6638\n",
            "Epoch 5/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 379ms/step - accuracy: 0.5000 - loss: 0.6550 - val_accuracy: 0.0000e+00 - val_loss: 0.7443\n",
            "Epoch 6/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 374ms/step - accuracy: 0.7857 - loss: 0.5854 - val_accuracy: 0.5000 - val_loss: 0.8778\n",
            "Epoch 7/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 363ms/step - accuracy: 0.6429 - loss: 0.5928 - val_accuracy: 0.5000 - val_loss: 0.7907\n",
            "Epoch 8/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 362ms/step - accuracy: 0.9286 - loss: 0.5204 - val_accuracy: 0.5000 - val_loss: 0.8447\n",
            "Epoch 9/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 357ms/step - accuracy: 0.7857 - loss: 0.4715 - val_accuracy: 0.0000e+00 - val_loss: 1.0134\n",
            "Epoch 10/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 355ms/step - accuracy: 0.7857 - loss: 0.4348 - val_accuracy: 0.5000 - val_loss: 1.0422\n",
            "Epoch 11/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 375ms/step - accuracy: 1.0000 - loss: 0.3301 - val_accuracy: 0.5000 - val_loss: 1.2645\n",
            "Epoch 12/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 358ms/step - accuracy: 0.8571 - loss: 0.3032 - val_accuracy: 0.0000e+00 - val_loss: 1.3914\n",
            "Epoch 13/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 350ms/step - accuracy: 0.7857 - loss: 0.3202 - val_accuracy: 0.5000 - val_loss: 1.6126\n",
            "Epoch 14/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 335ms/step - accuracy: 0.9286 - loss: 0.1963 - val_accuracy: 0.5000 - val_loss: 1.9662\n",
            "Epoch 15/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 331ms/step - accuracy: 0.8571 - loss: 0.2039 - val_accuracy: 0.5000 - val_loss: 1.8098\n",
            "Epoch 16/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 332ms/step - accuracy: 0.9286 - loss: 0.1390 - val_accuracy: 0.5000 - val_loss: 1.9797\n",
            "Epoch 17/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 346ms/step - accuracy: 1.0000 - loss: 0.1096 - val_accuracy: 0.5000 - val_loss: 2.5209\n",
            "Epoch 18/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 324ms/step - accuracy: 0.9286 - loss: 0.0996 - val_accuracy: 0.5000 - val_loss: 2.5993\n",
            "Epoch 19/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 326ms/step - accuracy: 1.0000 - loss: 0.0568 - val_accuracy: 0.5000 - val_loss: 2.4764\n",
            "Epoch 20/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 333ms/step - accuracy: 0.9286 - loss: 0.0789 - val_accuracy: 0.5000 - val_loss: 3.0441\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.2500 - loss: 5.4487\n",
            "Test accuracy: 0.25\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step\n",
            "AUC: 0.25\n",
            "\n",
            "Model: 3-conv-128-nodes-2-dense-1724926003\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_26\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_26\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_51 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │           \u001b[38;5;34m1,280\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_102 (\u001b[38;5;33mActivation\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_51 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_52 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │         \u001b[38;5;34m147,584\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_103 (\u001b[38;5;33mActivation\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_52 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_53 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │         \u001b[38;5;34m147,584\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_104 (\u001b[38;5;33mActivation\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_53 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_26 (\u001b[38;5;33mFlatten\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12800\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_51 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │       \u001b[38;5;34m1,638,528\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_105 (\u001b[38;5;33mActivation\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_52 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │          \u001b[38;5;34m16,512\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_106 (\u001b[38;5;33mActivation\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_53 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │             \u001b[38;5;34m129\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_107 (\u001b[38;5;33mActivation\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_51 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,280</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_102 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_51 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_52 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_103 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_52 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_53 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_104 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_53 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_26 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12800</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_51 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │       <span style=\"color: #00af00; text-decoration-color: #00af00\">1,638,528</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_105 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_52 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │          <span style=\"color: #00af00; text-decoration-color: #00af00\">16,512</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_106 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_53 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">129</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_107 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,951,617\u001b[0m (7.44 MB)\n"
            ],
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,951,617</span> (7.44 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,951,617\u001b[0m (7.44 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,951,617</span> (7.44 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.5000 - loss: 0.6935 - val_accuracy: 0.5000 - val_loss: 0.7030\n",
            "Epoch 2/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 330ms/step - accuracy: 0.5714 - loss: 0.6843 - val_accuracy: 0.5000 - val_loss: 0.6606\n",
            "Epoch 3/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 330ms/step - accuracy: 0.5714 - loss: 0.6637 - val_accuracy: 0.5000 - val_loss: 0.8872\n",
            "Epoch 4/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 319ms/step - accuracy: 0.5000 - loss: 0.7071 - val_accuracy: 0.5000 - val_loss: 0.6238\n",
            "Epoch 5/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 320ms/step - accuracy: 0.5714 - loss: 0.6380 - val_accuracy: 0.5000 - val_loss: 0.6359\n",
            "Epoch 6/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 323ms/step - accuracy: 0.5714 - loss: 0.6212 - val_accuracy: 0.0000e+00 - val_loss: 0.7198\n",
            "Epoch 7/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 314ms/step - accuracy: 0.7857 - loss: 0.5810 - val_accuracy: 0.0000e+00 - val_loss: 0.7478\n",
            "Epoch 8/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 321ms/step - accuracy: 0.7857 - loss: 0.5255 - val_accuracy: 0.5000 - val_loss: 0.7014\n",
            "Epoch 9/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 325ms/step - accuracy: 0.7857 - loss: 0.4802 - val_accuracy: 0.5000 - val_loss: 0.7582\n",
            "Epoch 10/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 324ms/step - accuracy: 0.8571 - loss: 0.3675 - val_accuracy: 0.5000 - val_loss: 0.8136\n",
            "Epoch 11/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 328ms/step - accuracy: 0.9286 - loss: 0.2750 - val_accuracy: 0.5000 - val_loss: 1.0981\n",
            "Epoch 12/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 341ms/step - accuracy: 0.8571 - loss: 0.2331 - val_accuracy: 0.0000e+00 - val_loss: 1.0357\n",
            "Epoch 13/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 332ms/step - accuracy: 0.7857 - loss: 0.3747 - val_accuracy: 0.5000 - val_loss: 1.8355\n",
            "Epoch 14/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 368ms/step - accuracy: 0.8571 - loss: 0.3124 - val_accuracy: 0.5000 - val_loss: 1.9106\n",
            "Epoch 15/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 363ms/step - accuracy: 0.8571 - loss: 0.2338 - val_accuracy: 0.5000 - val_loss: 1.6954\n",
            "Epoch 16/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 360ms/step - accuracy: 1.0000 - loss: 0.0908 - val_accuracy: 0.0000e+00 - val_loss: 1.8592\n",
            "Epoch 17/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 349ms/step - accuracy: 0.8571 - loss: 0.1997 - val_accuracy: 0.0000e+00 - val_loss: 2.0621\n",
            "Epoch 18/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 376ms/step - accuracy: 1.0000 - loss: 0.1040 - val_accuracy: 0.5000 - val_loss: 2.1737\n",
            "Epoch 19/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 356ms/step - accuracy: 1.0000 - loss: 0.0499 - val_accuracy: 0.5000 - val_loss: 2.2514\n",
            "Epoch 20/20\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 371ms/step - accuracy: 1.0000 - loss: 0.0736 - val_accuracy: 0.5000 - val_loss: 2.4074\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.5000 - loss: 3.6062\n",
            "Test accuracy: 0.5\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step\n",
            "AUC: 0.25\n"
          ]
        }
      ],
      "source": [
        "for dense_layer in dense_layers:\n",
        "    for layer_size in layer_sizes:\n",
        "        for conv_layer in conv_layers:\n",
        "            NAME = f\"{conv_layer}-conv-{layer_size}-nodes-{dense_layer}-dense-{int(time.time())}\"\n",
        "            print(f'\\nModel: {NAME}')\n",
        "\n",
        "            model = Sequential()\n",
        "\n",
        "            # Add an Input layer\n",
        "            model.add(Input(shape=X.shape[1:]))\n",
        "\n",
        "            # First convolutional layer\n",
        "            model.add(Conv2D(layer_size, (3, 3)))\n",
        "            model.add(Activation('relu'))\n",
        "            model.add(MaxPool2D(pool_size=(2, 2)))\n",
        "\n",
        "            # Additional convolutional layers\n",
        "            for l in range(conv_layer - 1):\n",
        "                model.add(Conv2D(layer_size, (3, 3)))\n",
        "                model.add(Activation('relu'))\n",
        "                model.add(MaxPool2D(pool_size=(2, 2)))\n",
        "\n",
        "            model.add(Flatten())\n",
        "\n",
        "            # Dense layers\n",
        "            for _ in range(dense_layer):\n",
        "                model.add(Dense(layer_size))\n",
        "                model.add(Activation('relu'))\n",
        "\n",
        "            # Output layer\n",
        "            model.add(Dense(1))\n",
        "            model.add(Activation('sigmoid'))\n",
        "\n",
        "            model.compile(loss='binary_crossentropy',\n",
        "                          optimizer='adam',\n",
        "                          metrics=['accuracy'])\n",
        "\n",
        "            model.summary()\n",
        "\n",
        "            model.fit(X_train, y_train,\n",
        "                      batch_size=16,\n",
        "                      epochs=20,\n",
        "                      validation_split=0.1)\n",
        "\n",
        "            # Evaluate the model\n",
        "            loss, accuracy = model.evaluate(X_test, y_test)\n",
        "            print(f\"Test accuracy: {accuracy}\")\n",
        "\n",
        "            # Calculate AUC\n",
        "            y_pred = model.predict(X_test)\n",
        "            y_pred = y_pred.flatten()\n",
        "\n",
        "            if roc_auc_score is not None:\n",
        "                try:\n",
        "                    if len(np.unique(y_pred)) == 1:\n",
        "                        print(\"AUC: undefined (all predictions are the same class)\")\n",
        "                    else:\n",
        "                        auc = roc_auc_score(y_test, y_pred)\n",
        "                        print(f\"AUC: {auc}\")\n",
        "                except Exception as e:\n",
        "                    print(f\"Error calculating AUC: {str(e)}\")\n",
        "            else:\n",
        "                print(\"AUC calculation skipped: roc_auc_score not available\")\n",
        "\n",
        "            model.save(f'04_energy_meters_recognition_cnn/model_energy_meter_{NAME}.keras')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KEb1SPXQMJHN"
      },
      "source": [
        "This method will convert images to be used for predictions into the correct format"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "uj_koklqMJHN"
      },
      "outputs": [],
      "source": [
        "def prepare(filepath):\n",
        "    img_array = cv2.imread(filepath, cv2.IMREAD_GRAYSCALE)\n",
        "    new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))\n",
        "    plt.imshow(new_array, cmap='gray')\n",
        "    return new_array.reshape(-1, IMG_SIZE, IMG_SIZE, 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2UBzZozgMJHN",
        "outputId": "f2e13754-24d7-4d8c-ddc3-6e4569786954"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Modelo cargado: 04_energy_meters_recognition_cnn/model_energy_meter_3-conv-128-nodes-2-dense-1724926003.keras\n"
          ]
        }
      ],
      "source": [
        "##model = tf.keras.models.load_model(\"04_energy_meters_recognition_cnn/model_energy_meter.model\")\n",
        "\n",
        "# Directorio donde se guardaron los modelos\n",
        "model_dir = \"04_energy_meters_recognition_cnn\"\n",
        "\n",
        "# Buscar el archivo de modelo más reciente\n",
        "model_files = [f for f in os.listdir(model_dir) if f.endswith('.keras')]\n",
        "if not model_files:\n",
        "    raise FileNotFoundError(\"No se encontraron archivos de modelo en el directorio especificado.\")\n",
        "\n",
        "# Ordenar los archivos por fecha de modificación (el más reciente primero)\n",
        "latest_model = max(model_files, key=lambda f: os.path.getmtime(os.path.join(model_dir, f)))\n",
        "\n",
        "# Cargar el modelo\n",
        "model_path = os.path.join(model_dir, latest_model)\n",
        "model = tf.keras.models.load_model(model_path)\n",
        "\n",
        "print(f\"Modelo cargado: {model_path}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VlzPL4CyMJHO"
      },
      "source": [
        "The first first prediction is being made on an image of my own energy meter which was not part of the original data train or test sample."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "CQdJJ5bvMJHO",
        "outputId": "194c6da6-809b-4199-9af8-46664c51b75b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "prediction = model.predict([prepare('./img/meter-test-1.jpg')])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9sbZlyNGMJHO",
        "outputId": "b464f487-e2b4-4f03-d610-6b659fe42b7f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "meter-readings\n"
          ]
        }
      ],
      "source": [
        "print(CATEGORIES[int(prediction[0][0])])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZZfM-3AaMJHO"
      },
      "source": [
        "This second prediction is being made on something I think might confuse the CNN as it contains numbers in digit form"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "evkRfe22MJHO",
        "outputId": "ca5f4e42-aca5-4e91-e182-f89321e31b08"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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