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Neural network tryouts to produce a image channel's formula learning
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Neural network tryouts to produce a image channel's formula learning" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "/usr/local/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n", | |
| " warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "import numpy as np\n", | |
| "import matplotlib.pylab as plt\n", | |
| "%matplotlib inline" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Let's simulate data such that a sort of NDVI index segments good objects\n", | |
| "\n", | |
| "Let input image have 4 channels, e.g. R,G,B,NIR. Each channel is normalized between [0, 1]. We fill all channels with random uniform noise. However, in R channel pixels of interest will have smaller than average noise values (absorption) and in NIR channel they will have larger than average noise values (emission). \n", | |
| "\n", | |
| "A NDVI-like image is composed as `(NIR - R) / (NIR + R)`. A binary mask of this NDVI-like image will serve as target image." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "n_images = 100\n", | |
| "input_height = 20\n", | |
| "input_width = 20\n", | |
| "\n", | |
| "inputs = np.random.rand(n_images,4,input_height, input_width) * 0.6 + 0.2\n", | |
| "targets = np.zeros((n_images,1,input_height, input_width), dtype=np.uint8)\n", | |
| "mean = 0.5\n", | |
| "\n", | |
| "max_size = 7\n", | |
| "objects = []\n", | |
| "\n", | |
| "for i in range(n_images):\n", | |
| " \n", | |
| " xs = np.random.randint(1, input_width-max_size)\n", | |
| " xe = xs + np.random.randint(5, max_size)\n", | |
| " ys = np.random.randint(1, input_height-max_size)\n", | |
| " ye = ys + np.random.randint(5, max_size)\n", | |
| " \n", | |
| " objects.append((xs, ys, xe, ye))\n", | |
| " \n", | |
| " inputs[i, 0, ys:ye, xs:xe] = np.clip(inputs[i, 0, ys:ye, xs:xe] - 0.75*mean, 0, 1)\n", | |
| " inputs[i, 3, ys:ye, xs:xe] = np.clip(inputs[i, 3, ys:ye, xs:xe] + 0.65*mean, 0, 1)\n", | |
| " targets[i, 0, ys:ye, xs: xe] = 1" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Show first 5 images" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "collapsed": false, | |
| "scrolled": true | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Object : (1, 2, 6, 7)\n", | |
| "Object : (4, 9, 10, 15)\n", | |
| "Object : (12, 7, 18, 12)\n", | |
| "Object : (2, 6, 8, 12)\n", | |
| "Object : (2, 9, 8, 14)\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
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fVEt/oDr6B3daJjPCvhDAOQCmEFF1GAER/ipsp9ppLi2oUv5BtfQPqqV/UC39geroH1xo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\ntiRxlYsuo71iUsL+1Ueau4USziJ1WsS1jAfXMh5a17LWCnDjqzNIehc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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x10c8655d0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
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SH6iO/iHz/Z7WAPoDADP/QUQrADQGMCdekdSnubTgbhbpbACNiKg+ER0EoBMAZxSMMQDO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\nuCMs2VLHdXa45wUCjqWb7T6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98tAlrwu7/9kyzNbJ5aVPEIWk1ieQ5QJAlWXazHwZerCV\nh7X8tL0Vto++kWl7HMsj/7NLhtbsa1s5u/fnct9nqsprEr5Funo87RBke750f/kKc6PbXRxD+A8j\nRx57dS1hB6rJNjQwTNqwudddGZb3TCgrKPPavY1ie9kYZXChZTLtKxEFAbwEoAyATczczpkn3NUq\nw5XHfyDSpkyTodyyl9t+O+vIsvd1RH3bHJZLi//VS4Ye3OWIKvnCQZa2Kx3ta+9XpdtIVie5c4vw\nVGEPRK/o9pAO94i0j8OXCns8zhf2GtQV9nf/s32Pqijgbgd4o04WDVnPejvjdqaRvuiTOJOHyHS/\nh4jaAhgDIOJHOJqZ+xV2TH3TXEpw2aBnAXgdwNkA1gOYTURjmHlJjHzPAlagU8e+K1MuhBKl9DXq\nSjxUS/+QqpbJtK9EVBnAIADnMfM6IqqWhiIrMdA66R+Ko98DYDIzX1LgAHGIF7PffuKhRLSRyHot\nQ0SHEdEEIvqdiMabDYKSQfIQSOovDi0BLAPQG8Z7lxoALgWklgAWAvgKwN/OfZl5Vaa+24GGSy0B\naL30Cqqlf3ChZcL2FcDvAMoB2AkAzBxnmrDilmR1LKxeap30Buno9zDzKmbeD2AkzHrpgGJ8FpeE\nnWYA7wKOMRPgEQDfM/OxMNZ+e7QoJ1WKThjZSf3FoTaANbC03G9+BphaAmgH4CAYDT7F2FdJEy61\njKD10gOolv7BhZbJtK//A7ARwK9ENJuIbsj8NzowSVbHBPVS66QHSFO/J8JaWPXSzulENI+Iviai\nJjHSBQlbc2aeQkSOQFK4FEBbc3s4gBCMGyomeYdap7nyPOmnlTfQSgucK/1cUXGl40gyLE7PH3Nk\n8lfOM1tx0PrnyWVbO2XfKOwy4SrCrgUZWuxNWM6wu3fKZ42D3pZn3dtfXtaD68rvRTNk/t/6WX7M\n0gPRIt4wxcrQKqwKJfcSOIGWrwHoAcP/Z3q8Y2z9zyphxYNsPnVdpJ9rz3uls9nrT8swRXyhfLj7\noe6Fwm6z2lpedeotconuEcNkGLhVaCDsnU3l9b/qEGup1c/Wy5BbdJX0Ic93OK86yxnoJX0r8/Zb\nuuw4WCTFrRDpGD5MR73cdqXho5r1nSNe4Fhp5t3WQNiBpxz1NGCPLyh92VeFpT8/jZJL0AOdhcVz\n5FyDJjZX2BO7yGt/6//J67jraDniPf5dOXfg1ZduE/bVsMoSyJfHDr+QnEZe0PIhPB/dvuqiJ2Ui\nOdazv0VeI3tYvr6OEF59ukt77huNhV1xqBzl3B+Q80YOzm8f3a7Om0TaI9+8IuzaHeQ8BeyQ5t6g\nvM53X2KFQXynh5xL0LSbDHO4Jt/6ztk4GkcU0T892TY2gY5PwPBl3gugA4DpRDSdmZfbMwf+sN2H\n0t0f2a/I0Kav590c3V7uKHoDRyGOnbpafiAj+mHAHml3y7OuWcMn7hBpZ/aXoe8+xrXCbolZwh7c\n2nJALztOLpPd6f0xws698SFh/+xYP/2Sk63JMSehQYxFtNPqB3sQAHurfimAtkTUGUZw2KOI6EwA\ndzLzgoJHSvH86ToQnN7SpY909HsS8DOAesy8m4guAPAFgGMK2yFVn+bDmXkjADDzBiI6PMXjKEkS\n7+apG2yIusGGUXty7pRY2dYBsM8EKWN+BphaEtEpAE4BUB/G09gFRJQXY1/FJRn0udN6Wcyolv7B\nRRubTPu6FsZrnXuZeQsRTQbQDMByKGkljX6w2QB+I6LGph9sRMs/AZwBYAWAfgCGADgt3jGV1Elz\nv6cOrHoJAGDmnbbtb4joDSKqwsxyEQAb6ZoIWOgDTe6XVvKmYxahejDhG/ADhtAa4Pv+ifPtw0Fu\nTjMbQCPzLUgZAIfC8T6RmRsCABFtMdO+ZOaxRBSw7Ytn+1pvGRkhEIJuyuUrfsoHpuQnzudSy6JQ\neL1cbibz9wAaAtSwsOwHFKE/gNCfifN5QcutOfaoQk0BtMh8aUoR00P7MT20HwCQJUJpSFxombB9\nhTFD/zUATESHAGgFYCCcjMyxtvOCQN1gqmXyJZtDi7A5tAgAsA+Hxszjsk5G/WBNPf+F8YY5OqzC\nzDMAgIgYwAzEHvKHMTgUoQEKvv8/kFmJZOIKpLHf8xeATgCus2cgohqRlxNE1BIAFdZhBlLvNG+M\nnIyIakJOHCtAn47WgMOv2mEWBOsCp9kWw3r6mdj53KzBzsxhIuoJYAKMIad/mXkxEd0OYF8MLTnO\nvnikt1WO5/sFUy6THzkry/iL8Fw4dj43WgKFDh866+V2ItoPGT7wUQBdAKBPI6Ne9l0h3V8UIHiU\n8Reh7/ex83lAS1TJsVzH/u3r8GtQcHqwDE4PGu5j2aiDF/uui5kvVS2TbF+XENE0AG1gdLQGM/Oi\nAgfrlGNt/5hScXxNtWATVDP7EO3QAOP7DiuQx2WddPrB2v3TY/V7bgXgCPQYIeimHD6nAeRDROyb\nPaopErUAACAASURBVI39nkjIuUi9ZGYeDOAqIroThs57AIevUQySLRFButqMBdANwHMAboTxFB2X\n/663XpKMntlFHtgWTzLcXhYngN6Fl+ohh/fPLEd6Oysm4RPZOSLpURnSFXt3y4eLyw6RX+mSsZYf\n19MO37+RYen/vHjjccL+tcbRwl59tfQrrNcp8URqt8PAzPwtgGOJqAGAL83P3iaihnBoycyPxNmX\nq9xoOb81CVtxcLPflH6uUxy+aIE2sgc5ra38Ptmj5f5Dsqz75ISh0l2sS1Y3YT9DUpANYbke8CdX\nWf7rXcPSAf3DS6SPc1ZL2ekgkqOnj9ws41yut7nJ1m4ESZyB1zQOH5YBsMA2fGivl91g1Fl7+MD6\nAG4D0BjAvsCvpiYXOeLxHiXLF9jl6P3L8Mjo18aKk/tEQHZIbnpzpLC7jpA+zZ+HOwr7kpoyXveX\ntuaCL5L1/SW58jryhjnaj3cdvte9pH/1L/nNo9vv9ZdtZVbgD7kvjkIsPKAlTiErTnwo/IA4R/P8\necJ+LEsuF97LHlZbTisASSnQYqH0YZaercDXeSFhz367bXT7ju5yKfbuP8i5LVjvOJjDs/Pg36WW\nextb1/2aQZ+ItMDD8n5d0MqqmAehovNEUdxomWT7uhvAS8721U6Vi636c9ylMoD61JPlUuPP0NLo\n9t+QcwGc0BuODz6SZp+zpJ2bbfmgtwvLirbM4fLZYMtKYe9/pZI82AwrbvPVleVzwoCu8n69i2VB\n5w+WXg9/dLHqYY1suYx7hDS6TCXq9/wC4CYAZ6brhIokXf0ex2dv27YHwQgFmTQJO81E9BGMR6aq\nRLQaQB8YsXw/JaKbAawCHAvfK2knHQ2BaukNXGoZCW/1DAwty8CYVNQLUksG8CqA4237bgfwH4Dy\nbgqgWHhASyVNpGECmbavHqAwHdeFlmNdyPlALLMAqGfT8nAANxDRL5BaboERaeo8Zv4n3sEUd3gx\n5nYy0TM6x0nSMd1iJFGs12RQLb2BSy1rA1jDzN0BgIi6AGjJzO+a6ecQUS0AHzLzQCKKfA5m/oeI\nXgSwusBRlZTwgJaD3RRAsXDbxmr76g0K07FG8FjUCFovHufkTnBmmQ2gEYzRn79gjF9fx8yR1/7n\nEFE9ABMBXM/MhfbAFXeko9+TbnRFwFKCW99JxTvE0/Kv0FL8FVoWM62IvAzgYZtNAGAOFd8PI0LK\nlnSc6EDHA1oqaULbWH9QDH6wTwKoAuANIiIA+5m5ZRqKrjjwYp0slhI1Kz83uv37IDmzm22OcTf2\nesuxp8PR7WHHuunPvyrMpqecLuyFv5xinefrHJGW3ckRN3ikjJC88GYZSWDjZda2M+L5pQHpd/VB\nWPpiN31ITsMf9YKMH7vnY5vP7qexXwJ6ZZiC19gmdS5vFd0O3C/9CE9fL30p86rIW21rXll53Gzp\nw/h72Hob8HZ2e5GWN0veQ5UbbxD2o1njhf0jLJ/mDx13fI08Gft0Y+0Gwg7fJ+MOy7m3gH0y/qu1\nZBxgZA1BLOJpeXjwOBwetPzhf8mNOb8kYRgdGKEDR5oNejUY4QP3w1iRbCozbyUihI8zyhH4Ssa7\npI3Sx3nRTBkfvfEkR3xMm0v5/Y55aBPKy4k6k+6SdfTyWdLPuM4G2dFc18TyR508TgbYbT1NmMhu\nJS/Drm1S7PL3SyfzbrDiCh9Bf8mDLZDfGTKcd5SS1hLAcWeyFW6pXrb0WUY1ObH/wdHSIb3Mx29G\nty/6qKZIe/j654R9fEDGtm8LyeWBoLDFnJQX7xFplyyRvu5jz+kk7PH7pJPtOZOnCjvwkxWmhnrJ\n+/XN37sJ+/i3rZeBjQ8F4q3p5YU29u0sKzj21VmOK0xnCLPW9Y571kYfx+32nsNfvZsznLfjp7e3\n7Scp8H/y9+qOgdI/fXwV+Yu4on9TebAJraObo7bL+T6hSkFhr3XM/+m08V1hjxx7U3R7X13EpBj8\nYG+DOZ9AySxeqJNOvNeNV2LyX/GFtlIyjEstE4bRiYQPBABzSD8SPrAZgCeJKPYMGqXIlLSWbk6u\nSLSN9Qeqo3/wopbaaS4leNG3R0kNN1omOXwodrHtO5+I3oexCpKSBjyg5fNQ0oK2sf5AdfQPXtRS\nO82lBC/69iip4VbLRMOHjs9vdtgvAHjBDMyvuKSktSQi7TSnCW1j/YHq6B+8qGWxlGjpr1Y81Kxe\n0vf18xHWk8RHfzsnH++V5nO5KIyF80+RH5xky09Xi6T9Vxwv7K13yF2r1pE223yaj2jliFt7/T5h\n0mDpr/vcBlnuK/83TtiB1fbjedffDgAa/rgwup31ptXvCj8gy5d1nFwaj6bKPlrehY5b72NpHhLI\nsfJeIK9fdpaM2brtIDmEc0L4V2G3HmEzbjxMpN1G0u84Z4H048w6wfE9msvvMamW5aN732hnX6do\nPs3FTd8fzO/yjfR7zXMsCHvseunDnHXObGGTzbF7VUUZM/sKyOu5nqSPeHiEvN+nnC592wfb4mp3\n5t9F2trs7bKgZ0uf3N4VHO3FB9JPuf23061y9JCaDFjeQ9gymqyFF7Tsvm1odPselqsjPb5BBqTv\nP7ifsLfabueGDeTcgE+PlD7MzrjMZRz20dMcz2FnWD647ZbIWL9js2XM5w/D8r7Z4YinnP2HrPNz\nb2oc3W7xnDzWi+gl973ScrTPDsRuXwFvaNlxu83H/01nIHJpnmwbMHL8fCH3gsLPw47VLnMvlnZP\n+6UI7xFpb1VxzD/tLeNkY6n0Ww7vtdr6eyvJ+/OVJY8KO1BWtrcfT+om7EWXWHW4Mq7A+yiIF3RU\n0oMXtfReN16JiRdvHiU1VEv/oFr6B9XSH6iO/sGLWmqnuZTgRd8eJTVUS/+gWvoH1dIfqI7+wYta\nFk+nOWANufJvMunwcLPodv4w50JlMm74wHwZYudF/j9h79gnhxe3w7aucdkmIu2Luh2Effn9MvTV\n0zIZj9minu27QwpZRkbgAe5y2I4RQecQf69O/aPbA+MssOoV357lP1mxt9beVSW63ZYda8fXl8Ps\nXEEmf/W7vAaffyMv+Jl5VoCHQOBFufM4eewXSbrLrHjUEfLIvhptoytEUkt6U9gfV71E7vuU43s4\nitL2FWvt9vyBUuh4A8Fe0XJN2AzZ6BiyPqi6wyXl/xzLbH/lWK56jTVuXGenSELeUBke8LlF9wr7\nHHwv7DMPXyjsYMByHZmfL+NmvZZ3t7CHBOQwcpeADFH54r0y2ATbRvXpelnuXtPkfRHfPaPktSz3\njv3+l+EWX9zuCB97pwyxKAbSZRQ45MoIZwXo85rjg/7SfDY8Nrr94Ci5PHKA/xb2+/hJ2N/8LOvp\nbTfL8KI1Ydtfeu3gTUcD3H6h5YaDQxp7ul4+UtlynynfZbNIC+fJkk+D5Rq20XGc2mF5Q9/684cy\nwy5pyloKVLV5UV0xa7RI28g1hD3VGcNlpmwzb+5shTkcPvdOkbbyJEdoxzzZ1pzafrKwF5N1x9ZD\nLcTCCzoq6cGLWnqvREpMvBh6RUkN1dI/qJb+QbX0B6qjf/CilvFnRZgQ0VAi2khEv9o+60NEa4lo\nrvnXobBjKO7JQyCpv8JQLb2BaukfVEv/4FZL1dEbJKujaul90tG+ppuEnWYA76LgIngAMJCZW5h/\n38ZIV9JIGNlJ/cXDrODnwAhJUt2RPBBADoAAgGeIaA4RtTf3q0NEk4hoIZS04FZLE62XHkC19A9p\n0FJ19ADJ6qhaep909HuIaAkRLSWihwvJdyoR7SeiK+LliZCwNWfmKeaKVQXOk2jfKL9ZfkrTbpBP\nBa2XW/6Qm/rJPnxVZzwZh+/URU/J0G2NOsoVaC8NW056Y5bL87YnGc5n8LPdhT2uvLx2zXrPiG53\n4/dE2tV95Rqkq56S5Tw8T4b0+ipblnNi/tk2K/YiX25mkRJRFoDXAbSBoflvRNSYOerVSQC+Z+ax\nZv4TAHwOoBGMQEe9mHkeEfGJbWZGj7twphXiL+8LeSvdvFou1/v6Pul/esi/soyBwHBZ5h8Oj26/\nGpYrlvY4V16LwH3SB7dvD6cPri3/2fK2vZ9fFvbSeScK+/rbpBNz+HK5/Hpgn+VPmv+aQ6PLEJN0\nzAhOR718p56xtPGQC6SfceDbAcLu/ZD0P82eLGNfvXu7bRG7z+U5liyRWjzS9BVh/7XwUHnucx3h\nHPFldKvpiTJO1ui5so6Ga8nL8eoax0q3jzvuC9vchJM6ymWa51ZvjWTwhJYTrWw1w3IZ4vXLq8m8\nclVzwBYts1cr6ZT8ck05waLK6vXCzp0pfUrpnkXCXpZ1bnR70NUixDRAVYU5vpWsLFltHX71P0rt\n3qpghT07ccNMkXZOkynCxhLL+bpx48MAyFB6EdKw/LLrOsm2rLvvl9fIGaaz9uXWkvN9wtJnecwi\nsbAkug/8QBboeHk924Xl7+Fl2dbS2V9Te5G2Ag2EPfUBudR1pSulh/Xw+TY/5ptEEn6aLZdLP8xx\nj80KyMt55bXWb37XswBAtseAR+pkkZCHzLUvP3+Ak6Z+z9kA1gOYTURjbP0ee75n4ZwQEodk3jTH\noycRzSOid4iosovjKEkQRiCpvzi0BLCMmVfB6AT/C+BSW3pPANNsWlYAsBkAmHkDM8/L3Dc78HCp\nZSK0XhYjqqV/yKCWqmMxkqyOqqX3SVe/h5n3w5jqfGmMfHcDGAXg7xhpBUh1IuAbAPoyMxNRPxjD\n+7fEzT0qJ7o5lxktKAMPa6WU5QxsyHknYT6XT8+1Aayx2fvNzwCp5ScAVsFYrjfW0BQ25lgLdnCN\nnaAWQTfl8hWhBUDot8T5Mhh7skj1MieyNsgyRtsqQLCq1ssIof3GXyI8oeWynOjmvtAxKBtslaky\nlVKWAVgOANi06eC4uTKkZZHq5IwcK5oMr28FqhXMRJlKLxtDwN8hAMC87bGzeKJOAgBCtu0G5p9i\nsNL8K5w093vWwuhIRyGiWgAuY+Z2ROQINRSblDrNzLzJZg6BfQw1FlflRDdbjH4qfr4DkEYE1My5\nNWpvzHWOnxrsQ9mYn+8KzcHu0JyUz+/Q8kEAjWEEzfsAtuV9iagCANTIsYa8N890rMB4gBM8wfiL\nkPtx7HzxtHRLUetlTiVzv6O1s+wkWMb4i9B3b+x8ntDy6JzoZtngH3GzHbgcbf4B1asfhi1bvoiZ\nKxNaFrVOnpZzTnR7Vvdg2stT6qkRNP4AND8L+HVc3wJZPFEnAQDBjJTDHzSAfIj4MWauTGlp42UA\ndl/nhD+GyXaayX4wIqrJzJGgyFcAKPT92pprLb+62k0cfoXdbU8ScvVj/HOsvGCHXSuXqz44sFbY\na/Kk/96g7E7R7Wvz3hNpw7JkzNfK4XLCzpIudZg30oqJWbmjjBe9H9Kn+aEGct8/SfqAnuRYbXYE\nnH6cBYn3xFUu2ArlbG+WNucOjpVtHYB65jYBOMj8LKaWpj9XNhFVZeYtRJQNY/gCZ8LyF/xt4qnR\n7cbPzBUnrAOpTaV58rVdeLT8PkNZ+kfe0sm6Jps3yAeJbMjA2E/9II/VOCzXkK1nD+Q6V/Z+lnZv\nJuzsoXKZ13pheZ9k15RLyPYaZfMDTfIFXxrfhLiql4ctM3zrt5f/SnxePyz9DBfYlusFAL5Atitd\n7/80uh2oIu/li8KjhD1xu/SP/HufjPm6o7xskioebR0vMNYRXPYgeU9lfyTr2cNZOcI+r/8YYc8K\nWBVxPm0Sab+H6wkbWasRC09oOf7p6OZx/9/emYfbUZV7+v2dc5BmUGRoQInMCFebgFEgEloSVAxw\nGa5cRlsEHgYv4wWbq9B6Id0t01UvoHAZDDTYeKFxCEEBAfFEmUIgIUwJkxIJkHAJkxiGnJOv/6ja\nZ++1s/fZtXftfU7tdb73eeo5tapWrVp1frXW/qrqW99SeD/3bhe2nXGDYfDlPfjt0Pp1l4STMc98\nac8gfQv7BunnJ20epNccDPV5zspx8k/pqZ5i/vKqdOgHO3hpaAz1Lg075J7FFemqX7FpA6Gf69m9\nf1+RCvv54Jzt0TJXm7y4tyJO/KLw9653s3CcCHO2HFp9ZML74b4zq377q2Ktb7t76HF397nhPNqn\nDpw/tL73Pb8LywpDLcPj4TTab31/eZBeMlAeM7HxivD18JtTNw7LqjZZqn/O3qjIsDk1KUSbzH1a\nB+prubx/TpaXhZV2D8C4dFslnwFukCRgA2AvSStK47tq0dBolvRTkkem9SX9GTgbmCJpR2AlyTv2\n4xuV4+QjZ0cwB9ha0gySzxMbAqdKeoWyln3A08DxkiYAmNmy9PirgSep47LhNEc7OnVvl8XAtYyH\nnIOOpgI3Amul6Wod1yAx8xZImgv8wsz+d53inBx4m4yHelquPnkiq0+eOJR+bVr1QzhQtns2A14G\nDgWCEbJmNvTkKeka4JbhDGbIFj3j8Bqbr6mxzekgeWIRmtmgpJOAi0nmgjrLzM6XdDxwj5kdIemf\ngCOA29I8hwBImgR8BXgs5yU4Ke2IK+ntshi4lvHQqpYVo/THk47SBw5NR+lfk+bZHfiGme1XtyCn\nLXibjIc22T13kAS9mG5mC1K7x8ys+juGrVJIDXxGwC4h73SSaUzJbau2XVGxfiFwYY3j7iWJ34yk\nTDeVMzxFnBrUaQ3XMh5yaFkZnQhJpVH6C6vy+Xf3EcDbZDx02u6p2n50re3VjMjdte6HXhtav+6t\ng4J9O943b2h9zb5ng31bzwh9uu66MYyd+oVdwtiq9r1lQbr3wbI/pK4J7b1vDITxd7c4InSotqr/\nzL7nlP223ngt9MOqDif9tWfDTwXX7hL6Ct42GGqzr8o+pPOoTQdHBDfFFVf+49D64PJynY5W6G/3\nPfvvQfo/vxv6O/78X8Jyj5kc+qMeffelQ+sP2C+CfQM/PjBI9/6PKp/wMOQ2VLo+vRjeB6qK3fvg\nQBjPdKe1w3jo+7/970H622ueXS7rvZsIOYhaFEXLd99L/Dtt72OC7Wf0hjf/STuE2lz6Thip8rUL\nyut2WGgXrK6wDb+9Vzivzuv3hT6mr/wl9HHegnLs3z8tCmMQY3eGyQ+HPrffPTn8+r3tJWHr2vX+\n8n2zeOf1gn3397xJFoqg5W8Gy3MszOb1YN/vLAyM+0LvakH64MHyPXvtr04I9u0//Y4g/etHQ3/0\n/2lhTPm7ekN3wR8PVvjCP1oV3fTCsE889tpLgvRab4U+5lwZttMnriz78/6uasDcP68MB5t/6J2y\nRmurt65bcw4tG47ST/mspEdI/CrPMLMnV8lx3yeGVg8aF/6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MhwJq6UZzt1BA3x6nRVzL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\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x110326850>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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H0zIemI7xofT9nm4AbgQAEZlK8gcAnQCMzlYk82luLISbRToKwJYkO5BsCuAkAMEoGK8B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\n6nwxA9Ljn75GfnXbF8eE8SPv/nQ+Asiv/zw+uPajS88K0q1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lvU6/8JGQeHAt\n48G1jAPXMR4G3O5Zkpxzo7n5GfDQK75LUsPIXhgdp1Zcy3hwLePAdYyH7GnpRnNzsBjaN6syb6kn\npCXAiIL08ORccZ6Pl8izehX3OtXTHy0XN7QmTlpcy3hwLeOgPzom+Z2MMph2T1ncaG4CzGzzlEXM\nB0ZK2gx4BZgMHFaUZxYwFbhB0ljgDTNbKmlZFfc6VVIHLZ2M4FrGg2sZB65jPAym3dNXoW40rwKY\nWZek44G7yIUZvNLMFkk6LnfZLjez2ZL2k/Qs8A4wpa97B+mjOI7jOI7j9Ekau6cvZGaNrbnjOI7j\nOI7jNDm+uYnjOI7jOI7jVMCNZsdxHMdxHMepgBvNjuM4juM4jlMBN5odx3Ecx3EcpwJuNDuO4ziO\n4zhOBdxodhzHcRzHcZwKuNHsOI7jOI7jOBVwo9lxHMdxHMdxKvD/AbRrUjJTBzkQAAAAAElFTkSu\nQmCC\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x11098e690>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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ZN6epSEpGcakUM/8Awz/Z/t3bDvsFAC8UcowJACa4K4mitc5HqJb+QbX0B6qjf0hzv8eM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\nUqdpkJ1O6O2YbJtJL00W8nS6vVTSIpJzpEhGwIlJBedXN5r7hQquInW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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x110fd03d0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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6rfuYeWuWiqSUKKqUf1At/YNq6R9US3+gOvqHLA8WmqF1Ly7BIiklhk45+QfV0j+olv5B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\nRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x111640ad0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "for j in range(5):\n", | |
| " print(\"Object : {}\".format(objects[j]))\n", | |
| " plt.figure(figsize=(12,4))\n", | |
| " plt.suptitle(\"Image %i\" % j)\n", | |
| " for i in range(inputs.shape[1]):\n", | |
| " plt.subplot(1,6,i+1)\n", | |
| " plt.imshow(inputs[j,i,:,:], interpolation='none')\n", | |
| " plt.colorbar()\n", | |
| " plt.title(\"Channel %s\" % str(i+1))\n", | |
| " ndvi = (inputs[j,3,:,:] - inputs[j,0,:,:]) / (inputs[j,3,:,:] + inputs[j,0,:,:])\n", | |
| " plt.subplot(1,6,5)\n", | |
| " plt.imshow(ndvi, interpolation='none')\n", | |
| " plt.colorbar()\n", | |
| " plt.title(\"NDVI\")\n", | |
| " plt.subplot(1,6,6)\n", | |
| " plt.imshow(targets[j,0,:,:], interpolation='none')\n", | |
| " plt.colorbar()\n", | |
| " plt.title(\"Targets\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "(75, 4, 20, 20) (25, 4, 20, 20) (75, 1, 20, 20) (25, 1, 20, 20)\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "ll = int(n_images * 0.75)\n", | |
| "X_train = inputs[:ll,:,:,:]\n", | |
| "X_test = inputs[ll:,:,:,:]\n", | |
| "Y_train = targets[:ll,:,:,:]\n", | |
| "Y_test = targets[ll:,:,:,:]\n", | |
| "print X_train.shape, X_test.shape, Y_train.shape, Y_test.shape" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Define an neural network architecture\n", | |
| "\n", | |
| "Let's take a small U-net type network" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": { | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "WARNING (theano.sandbox.cuda): The cuda backend is deprecated and will be removed in the next release (v0.10). Please switch to the gpuarray backend. You can get more information about how to switch at this URL:\n", | |
| " https://github.com/Theano/Theano/wiki/Converting-to-the-new-gpu-back-end%28gpuarray%29\n", | |
| "\n", | |
| "Using gpu device 0: GeForce GT 750M (CNMeM is enabled with initial size: 50.0% of memory, cuDNN 5103)\n", | |
| "Using Theano backend.\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "gpu0\n", | |
| "th\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "import theano\n", | |
| "from keras.backend import image_dim_ordering\n", | |
| "print theano.config.device\n", | |
| "print image_dim_ordering()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from keras.layers import Convolution2D, MaxPooling2D, UpSampling2D, Input, merge\n", | |
| "from keras.models import Sequential, Model\n", | |
| "from keras.optimizers import Adam" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "n_filters_0 = 8\n", | |
| "n_classes = 1\n", | |
| "n_channels = 4\n", | |
| "\n", | |
| "_inputs = Input((n_channels, input_height, input_width))\n", | |
| "x = _inputs\n", | |
| "\n", | |
| "x1 = Convolution2D(n_filters_0, 3, 3, border_mode='same', activation='relu')(x)\n", | |
| "\n", | |
| "x = MaxPooling2D()(x1)\n", | |
| "x2 = Convolution2D(n_filters_0 * 2, 3, 3, border_mode='same', activation='relu')(x)\n", | |
| "# size/2\n", | |
| "\n", | |
| "x = MaxPooling2D()(x2)\n", | |
| "x3 = Convolution2D(n_filters_0 * 4, 3, 3, border_mode='same', activation='relu')(x)\n", | |
| "# size/4\n", | |
| "\n", | |
| "x = UpSampling2D()(x3)\n", | |
| "x = merge([x2, x], mode='concat', concat_axis=1)\n", | |
| "x = Convolution2D(n_filters_0 * 2, 3, 3, border_mode='same', activation='relu')(x)\n", | |
| "# size/2\n", | |
| "\n", | |
| "x = UpSampling2D()(x)\n", | |
| "x = merge([x1, x], mode='concat', concat_axis=1)\n", | |
| "x = Convolution2D(n_filters_0, 3, 3, border_mode='same', activation='relu')(x)\n", | |
| "\n", | |
| "x = Convolution2D(n_classes, 1, 1, border_mode='same', activation='sigmoid')(x)\n", | |
| "\n", | |
| "_outputs = x\n", | |
| "\n", | |
| "model = Model(input=_inputs, output=_outputs)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "____________________________________________________________________________________________________\n", | |
| "Layer (type) Output Shape Param # Connected to \n", | |
| "====================================================================================================\n", | |
| "input_1 (InputLayer) (None, 4, 20, 20) 0 \n", | |
| "____________________________________________________________________________________________________\n", | |
| "convolution2d_1 (Convolution2D) (None, 8, 20, 20) 296 input_1[0][0] \n", | |
| "____________________________________________________________________________________________________\n", | |
| "maxpooling2d_1 (MaxPooling2D) (None, 8, 10, 10) 0 convolution2d_1[0][0] \n", | |
| "____________________________________________________________________________________________________\n", | |
| "convolution2d_2 (Convolution2D) (None, 16, 10, 10) 1168 maxpooling2d_1[0][0] \n", | |
| "____________________________________________________________________________________________________\n", | |
| "maxpooling2d_2 (MaxPooling2D) (None, 16, 5, 5) 0 convolution2d_2[0][0] \n", | |
| "____________________________________________________________________________________________________\n", | |
| "convolution2d_3 (Convolution2D) (None, 32, 5, 5) 4640 maxpooling2d_2[0][0] \n", | |
| "____________________________________________________________________________________________________\n", | |
| "upsampling2d_1 (UpSampling2D) (None, 32, 10, 10) 0 convolution2d_3[0][0] \n", | |
| "____________________________________________________________________________________________________\n", | |
| "merge_1 (Merge) (None, 48, 10, 10) 0 convolution2d_2[0][0] \n", | |
| " upsampling2d_1[0][0] \n", | |
| "____________________________________________________________________________________________________\n", | |
| "convolution2d_4 (Convolution2D) (None, 16, 10, 10) 6928 merge_1[0][0] \n", | |
| "____________________________________________________________________________________________________\n", | |
| "upsampling2d_2 (UpSampling2D) (None, 16, 20, 20) 0 convolution2d_4[0][0] \n", | |
| "____________________________________________________________________________________________________\n", | |
| "merge_2 (Merge) (None, 24, 20, 20) 0 convolution2d_1[0][0] \n", | |
| " upsampling2d_2[0][0] \n", | |
| "____________________________________________________________________________________________________\n", | |
| "convolution2d_5 (Convolution2D) (None, 8, 20, 20) 1736 merge_2[0][0] \n", | |
| "____________________________________________________________________________________________________\n", | |
| "convolution2d_6 (Convolution2D) (None, 1, 20, 20) 9 convolution2d_5[0][0] \n", | |
| "====================================================================================================\n", | |
| "Total params: 14,777\n", | |
| "Trainable params: 14,777\n", | |
| "Non-trainable params: 0\n", | |
| "____________________________________________________________________________________________________\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "model.summary()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "model.compile(optimizer=Adam(lr=0.01), loss='binary_crossentropy', metrics=['recall', 'precision', 'accuracy'])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": { | |
| "collapsed": false, | |
| "scrolled": true | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Train on 75 samples, validate on 25 samples\n", | |
| "Epoch 1/10\n", | |
| "0s - loss: 0.4676 - recall: 0.2049 - precision: 0.0190 - acc: 0.7805 - val_loss: 0.3164 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_acc: 0.9244\n", | |
| "Epoch 2/10\n", | |
| "0s - loss: 0.3077 - recall: 0.0000e+00 - precision: 0.0000e+00 - acc: 0.9234 - val_loss: 0.2858 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_acc: 0.9244\n", | |
| "Epoch 3/10\n", | |
| "0s - loss: 0.2562 - recall: 0.0000e+00 - precision: 0.0000e+00 - acc: 0.9234 - val_loss: 0.2238 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_acc: 0.9244\n", | |
| "Epoch 4/10\n", | |
| "0s - loss: 0.2081 - recall: 0.0000e+00 - precision: 0.0000e+00 - acc: 0.9234 - val_loss: 0.1857 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_acc: 0.9244\n", | |
| "Epoch 5/10\n", | |
| "0s - loss: 0.1735 - recall: 0.0000e+00 - precision: 0.0000e+00 - acc: 0.9234 - val_loss: 0.1509 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_acc: 0.9244\n", | |
| "Epoch 6/10\n", | |
| "0s - loss: 0.1424 - recall: 0.0000e+00 - precision: 0.0000e+00 - acc: 0.9234 - val_loss: 0.1263 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_acc: 0.9244\n", | |
| "Epoch 7/10\n", | |
| "0s - loss: 0.1173 - recall: 0.0000e+00 - precision: 0.0000e+00 - acc: 0.9234 - val_loss: 0.1071 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_acc: 0.9244\n", | |
| "Epoch 8/10\n", | |
| "0s - loss: 0.1004 - recall: 0.0000e+00 - precision: 0.0000e+00 - acc: 0.9234 - val_loss: 0.0943 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_acc: 0.9244\n", | |
| "Epoch 9/10\n", | |
| "0s - loss: 0.0907 - recall: 0.0000e+00 - precision: 0.0000e+00 - acc: 0.9234 - val_loss: 0.0864 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_acc: 0.9244\n", | |
| "Epoch 10/10\n", | |
| "0s - loss: 0.0839 - recall: 4.3521e-04 - precision: 0.1467 - acc: 0.9235 - val_loss: 0.0807 - val_recall: 0.0040 - val_precision: 1.0000 - val_acc: 0.9247\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "batch_size=16\n", | |
| "hist = model.fit(X_train, Y_train, nb_epoch=10, batch_size=batch_size, validation_data=[X_test, Y_test], shuffle=True, verbose=2)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "((25, 1, 20, 20), 1.2736384e-09, 0.51110464)" | |
| ] | |
| }, | |
| "execution_count": 11, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "Y_pred = model.predict(X_test)\n", | |
| "Y_pred.shape, Y_pred.min(), Y_pred.max()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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6aD+zzqvZoOWU17703cfmNx9E8t+br3Ir+ZUcuvBbyfrJb2o+1+DX9n13ct2j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\nzGvPjxAP+gc0S8poSph+cF5t6DmzK3F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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x124651710>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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xcM5uwI0RsQhA0pnAgcANY5Y7Cjgb2LXKyswsr0OwZnVXJa8NO532Ae4AFkg6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\nQmCC\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x1249c8110>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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zP13mg+nyO49pXovm/yWWMTS12gAnSbsDx7LkVrBHN1nuFcAfgLdFxM8qbdQS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\npJN1G7lDbLXmI8RmeWmW2Sc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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x124d00950>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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lJK0H7BcR36S465SZVVAhr2bWY1XzKmmWpOskXS/pqHHq+0i6WtKVki6XtHu7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\n+E5XazGrk4ymcTKrvYzy2rMBsaRZkq6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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x125036850>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
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FvdVcuq3unt18/z/4Wv3srf6dq0W9xX0Okiaua5hTXjM6mG028UYq3FdS0jTg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\ntgAWA/sDBzQuIGlz4AfAWyL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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x12536e750>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "for j in range(5):\n", | |
| " plt.figure(figsize=(12,4))\n", | |
| " plt.suptitle(\"Image %i\" % j)\n", | |
| " plt.subplot(131)\n", | |
| " ndvi = (X_test[j,3,:,:] - X_test[j,0,:,:]) / (X_test[j,3,:,:] + X_test[j,0,:,:])\n", | |
| " plt.imshow(ndvi, interpolation='none')\n", | |
| " plt.colorbar()\n", | |
| " plt.title(\"NDVI\")\n", | |
| " plt.subplot(132)\n", | |
| " plt.imshow(Y_test[j,0,:,:], interpolation='none')\n", | |
| " plt.colorbar()\n", | |
| " plt.title(\"Targets\")\n", | |
| " plt.subplot(133)\n", | |
| " plt.imshow(Y_pred[j,0,:,:], interpolation='none')\n", | |
| " plt.colorbar()\n", | |
| " plt.title(\"Predictions\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Look inside the network\n", | |
| "\n", | |
| "Let's take a look on feature maps created by the trained network" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 26, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import keras.backend as K\n", | |
| "\n", | |
| "def get_layer_output_func(layer_name, model):\n", | |
| " inputs = [K.learning_phase()] + model.inputs\n", | |
| " output_layer = model.get_layer(name=layer_name)\n", | |
| " outputs = output_layer.output\n", | |
| " return K.function(inputs, [outputs])\n", | |
| " \n", | |
| " \n", | |
| "def compute_layer_output(input_data, layer_output_f):\n", | |
| " return layer_output_f([0] + [input_data])\n", | |
| "\n", | |
| "\n", | |
| "def compute_layer_outputs(input_data, model, layer_output_f_dict={}, layer_names=None):\n", | |
| " \"\"\"\n", | |
| " Method to compute (all or only those specified by `layer_names`) layer outputs on `input_data` for a given `model`\n", | |
| " :return: tuple of pairs: [(\"layer_name_1\", ndarray), ...]\n", | |
| " \"\"\"\n", | |
| " if layer_names is None:\n", | |
| " inputs_outputs = model.input_layers\n", | |
| " inputs_outputs.extend(model.output_layers)\n", | |
| " layer_names = [layer.name for layer in model.layers if layer not in inputs_outputs] \n", | |
| " else:\n", | |
| " all_layer_names = [layer.name for layer in model.layers]\n", | |
| " assert set(layer_names) & set(all_layer_names) == set(layer_names), \\\n", | |
| " \"Items {} of layer_names are not in model\".format(set(layer_names) - set(layer_names))\n", | |
| "\n", | |
| " layer_outputs = []\n", | |
| " for layer_name in layer_names:\n", | |
| " print(\"-- %s\" % layer_name)\n", | |
| " if layer_name not in layer_output_f_dict:\n", | |
| " layer_output_f_dict[layer_name] = get_layer_output_func(layer_name, model)\n", | |
| " layer_outputs.append((layer_name, compute_layer_output(input_data, layer_output_f_dict[layer_name])))\n", | |
| " return layer_outputs\n", | |
| "\n", | |
| "\n", | |
| "def display_layer_output(layer_name, layer_output, layer_plots_limit=None, **kwargs):\n", | |
| " assert len(layer_output.shape) == 3, \"Layer output should be 3D : (n_channels, height, width), {}\".format(layer_output.shape)\n", | |
| " nc = layer_output.shape[0] if layer_plots_limit is None else min(layer_plots_limit, layer_output.shape[0])\n", | |
| " n_cols = 4 if nc >= 4 else nc\n", | |
| " n_rows = int(np.floor(nc / n_cols))\n", | |
| " plt.suptitle(\"First output for layer %s : %i - %i\" % (layer_name, 1, nc))\n", | |
| " for i in range(nc):\n", | |
| " plt.subplot(n_rows,n_cols,i+1)\n", | |
| " plt.imshow(layer_output[i,:,:], interpolation='none')\n", | |
| " plt.axis('off') " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 24, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "-- convolution2d_1\n", | |
| "-- maxpooling2d_1\n", | |
| "-- convolution2d_2\n", | |
| "-- maxpooling2d_2\n", | |
| "-- convolution2d_3\n", | |
| "-- upsampling2d_1\n", | |
| "-- merge_1\n", | |
| "-- convolution2d_4\n", | |
| "-- upsampling2d_2\n", | |
| "-- merge_2\n", | |
| "-- convolution2d_5\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "layer_output_f_dict = {}\n", | |
| "layer_outputs = compute_layer_outputs(X_test[:1,:,:,:], model, layer_output_f_dict)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 27, | |
| "metadata": { | |
| "collapsed": false, | |
| "scrolled": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x129850f50>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x12d376b90>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x12e156fd0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x127ae7cd0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x13d3ed190>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x13d795dd0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x13df60950>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x13e73a4d0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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ye3eNYgdrbt3zE3V57Ykfb+5Gk6N+Nx17RLS2yU8+GLXTznEIg4AvVnfBOXnB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\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x13f6dce90>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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c6+Pp7w5nZYD/Z/DnogOD7j0OS8nzSFGTqIHH7OD57ZUvsmdSy5KIiIhIhJS3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\nSURERCSCKksiIiIiEVRZEhEREYnw/3ndvM6lBrnlAAAAAElFTkSuQmCC\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x14068b490>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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AYIoACQDAFE00sIJjq3+yyto127rarXdu6ncePWAm3xzURg0zqzV4ss3dg81OGzTHPKav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\nf6RDcJe7J0e9AAAAAElFTkSuQmCC\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x141de2490>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "for layer_name, layer_output in layer_outputs:\n", | |
| " plt.figure(figsize=(12,4))\n", | |
| " display_layer_output(layer_name, layer_output[0][0,:,:,:])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 2", | |
| "language": "python", | |
| "name": "python2" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 2 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython2", | |
| "version": "2.7.13" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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