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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"Using TensorFlow backend.\n" | |
] | |
} | |
], | |
"source": [ | |
"import numpy as np\n", | |
"import matplotlib.pyplot as plt\n", | |
"\n", | |
"from keras.models import Sequential\n", | |
"from keras.layers import Dense, Activation\n", | |
"from keras.datasets import mnist\n", | |
"import keras.backend as K\n", | |
"from keras.utils import to_categorical\n", | |
"%matplotlib inline" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"60000 train samples\n", | |
"10000 test samples\n" | |
] | |
} | |
], | |
"source": [ | |
"(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", | |
"\n", | |
"x_train = x_train.reshape(60000, 784)\n", | |
"x_test = x_test.reshape(10000, 784)\n", | |
"x_train = x_train.astype('float32')\n", | |
"x_test = x_test.astype('float32')\n", | |
"y_train = to_categorical(y_train, 10)\n", | |
"y_test = to_categorical(y_test, 10)\n", | |
"\n", | |
"x_train /= 255\n", | |
"x_test /= 255\n", | |
"print(x_train.shape[0], 'train samples')\n", | |
"print(x_test.shape[0], 'test samples')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"epochs = 30\n", | |
"\n", | |
"activations = {'cos': K.cos,\n", | |
" 'sin': K.sin,\n", | |
" 'relu': 'relu',\n", | |
" 'tanh': 'tanh',\n", | |
" 'sigmoid': 'sigmoid',\n", | |
" 'None': None\n", | |
" }" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"res = {}\n", | |
"for name, activation in activations.items():\n", | |
" model = Sequential()\n", | |
" model.add(Dense(512, input_dim=784, activation=activation))\n", | |
" model.add(Dense(10, activation='softmax'))\n", | |
" model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['acc'])\n", | |
" res[name] = model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=epochs)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.legend.Legend at 0x7f004560b400>" | |
] | |
}, | |
"execution_count": 5, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
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Uko9bP+bJ6idZ3bQavdAzwb6E3rYF1Fa7mV+exb9+eRqTCkZpKAgpoeb/4M1f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\nrDDvGlhwqxbvSaEY5fg6O9i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A/ZaN9La19A0D1RsMZBWXUjp5Gjll5eSUjiGnbAzO7FzV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\nAAAASUVORK5CYII=\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7f0084a17128>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"x = range(epochs)\n", | |
"for k, v in res.items():\n", | |
" plt.plot(x, v.history['val_acc'], label=k)\n", | |
"\n", | |
"plt.xlabel('epoch')\n", | |
"plt.ylabel('val acc')\n", | |
"plt.title('hidden 512 acc')\n", | |
"plt.legend()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 6, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.legend.Legend at 0x7f00448708d0>" | |
] | |
}, | |
"execution_count": 6, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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WJ5ajj1qUiwvak+6y8e9FhXF5/37f6cDVvx5BRgcXn764kblPr8VdLa0GIUTL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\ndngaBpszk7EAAAAASUVORK5CYII=\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7f00449acbe0>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"for k, v in res.items():\n", | |
" plt.plot(x, v.history['val_loss'], label=k)\n", | |
"\n", | |
"plt.xlabel('epoch')\n", | |
"plt.ylabel('val loss')\n", | |
"plt.title('hidden 512 loss')\n", | |
"plt.legend()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"res = {}\n", | |
"for name, activation in activations.items():\n", | |
" model = Sequential()\n", | |
" model.add(Dense(512, input_dim=784, activation=activation))\n", | |
" model.add(Dense(512, activation=activation))\n", | |
" model.add(Dense(10, activation='softmax'))\n", | |
" model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['acc'])\n", | |
" res[name] = model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=30)\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 8, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.legend.Legend at 0x7f00281104e0>" | |
] | |
}, | |
"execution_count": 8, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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mYBzDw4ce5qMPfJSyV+YftvwDmzteyRfvfpZ/fnAvparHm8/r4kNXrGVF65Se\nT9US7P0Fuu12Sr+4i+zuCrmDScrDUSBKdNVKWq+9gvSWLSTOPde8ItMwTnGVUpHhg70MH+zBrVTo\nWLma1q5l2JGF9egcEzCmoar82zP/xhcf/iLLMsv4yuVfocXp4l3f+DVP9Y7yxk1L+dAV61jTnp7c\naawXnvsx3pN3kX/oV+QPQO5gArcYAytO8rzz6Lji1cFYh1Wr5u7LGTNWLZXIDg2SGxrAc13aV6wi\n3dzygo5ZGB2hZ8d2ep7Zzsjhg1i2jR1xgsmJYEccrEiEiONg2RHsSATbcRCxEEsAQQRELJDaNIBg\nRyI48TjReIJoIoETTxyRjhzl2V6qilet4lYquJXyEXPP87AsCxFBLGtyCj8H64JlVqT2+zjYtn3M\nnnq+51HK5yjnc5TzeUr5XPg5SFcKwWtxbSc8ZiSCFc6DZZNzENxKmWq5jFsuUy2XgnS4rFouBcsr\n5aCjyQzYEYdEpoFEQwOJTCOJhgaSmQYSDY3hsgacWFAj4LkuY/2HGT7Yy1BvN8MHe4Ig0dtNbnho\nmmNHaFu+ko6Vq+lYtZZFq9bQtmIlTvT4A2N9z6MwNkphdITCyDButcral718Rt/phTABY4qSW+Jz\nv/ocd+66k8uWXcZfv/KvqVSjvOsbv2ZnX45/+sPNXL5hUdBofeBh9NkfUX7wv8k9dYD8wRiFgRj4\nKaxEjNQrLyHz6leTuvRSIs3NL+r3UN+flS615UKBnh3bOLDtSYZ6u7Htyf+gViQy8Z948j9uuM62\nEdvGsuwgbVlYto0VzsfXAfiui+dW8Wrn1WBeuy7iOEdeABMJorEgXXtxjESjqO/j+x6+F06+h3o+\nvu/jex7qe3iuS2FslNzgANnBAbJDA0F6aJDc4AClfO55v0e6uYWO1WtZtGoti1YH09GCiKoycqiX\nnme2TwSJ4YPB03Fsx6F5SSfq+xPfNfiek9/b97xpj/tCWHYk+K1icdT3wsBQwa1WZv1ck+e0J/9O\nnCAgolDK56iWisfdF3hBv0XEiRKJxYjEYjjRGJETeImWV61SzI5RzI6hUx7LP3H8WIx4Kk1hdOSI\nfMbTGZqXdrLinPNoXtJJ89JOmpd0Ykci9O3dTd+eXRzes4vnfvMQT977YwDEsmjtXEbHqjW0L1+J\n53lBUBgdoTA6TH4kSBdz2SOCXjzd8KIEDDNwr8bB3EFuuO8Gnh56mg+c+wH++Jw/Zjhf5fe++Wt2\nD+T5p3fmbFITAAAgAElEQVRt5BJ9DO+3/03+5/eT21shfzCOWwz+qGNrVpDe8hpSl1xC8rxz69pQ\nraoUx0YZPnSQkUO9jBw+yMihgxPzSrFIa9cy2lesomPlatpXrKJ9xSoSmYZjHrc2QHRvf5LDu3eh\n6mNHIrQs7QruRKdc1MfTvufW5buKZWFHHLxqFdXp/9POhmRjE+mWVjKtbaRb2si0tJJpayfT0oqI\nRd/eXRzavZPDu3cy1Ns98R821dzColVrWLR6Le0rVjHW3zcRJAqjwWty4+kMnRs20rl+I50bNtKx\nau1R7/bH+b6H73p4bhX1FUUnL1qqaDjVpn3Po1IsUC2VqJSKVIpFKqXwc7E4saxaKmLZNpFolEh4\nEY04Nema5bZtB8f2/eA8vhfOgwBMONcwGHtu9ci/jZq/Fz9cFvwmKWKpNLFkmng6TSyZIp5KE0tN\nzn3fRj2A4Pie76Kei++7+N745OG7Lur7OPE4kWgMJwwQkWh04sbkaDzXJztUIjtQYmywyNhAibGB\nImMDRcoFF9uxsGxBpAIUEYqoV8T3C/heAd8t4LtFYqkGUi2LybQuobFjCammRpyYjRO1ceI2kaiN\nE7MRESpFl3LRDeaFKqN9fQz17mXk0D6yA/vJj3TjlrMAiBXFiqSw7BQiSZAkShI0AZJErCSJhibe\n93dXn9Tf/YkM3DMBI/TwoYf5yP0foepX+etL/prLll1Gf7bM733zV1iDO/nmusdI3f9DxnZCYSAK\nKljJOKmLXkH6sstJXXIJzqJF0x67UiqSGxoiPzJEbniI/PDkPD88RKmQx7ZtrIhDxKkpckcm7+Qj\njoNYNoWRYYbDoFB7dyZikWlrp2nxEpoWLcaJJxg8sI/+fXvIjwxPbJdubaMjDB7tK1bRtmwFo32H\nObD9SQ5se5K+PZMBYsm6DXRtPJtlG89myRnrj1tUDoJJcGGYeoev42nfR8O573nhuZwjSicTVQ0R\nBysyWRJRVdxK+cgLX7FIpRxeGMMLoVupBKUZ20bCEo5lWYhtTZR4rLCEk2hoINPaRqq59bgX8Kn/\npn17d9MXBpBDU4JI46LFE8Ghc/1ZtCztPGUHUaoqpXyVwliFWMIh2RjFOonXARfGKvQfyNK/P8vA\n/iz9B7KMDZRmtK9YgmVLcHGO2URiwdyJWTixCE7UmlgecWwKY+WJwJAfKR9RQ2VZQro1TkNrnETa\nwfMUz/XxquHk+rg1ac/1cSs+bsWbaU3XUVmWEE1EiCYj2JEy0XiMaDyO7dhEohYRxyLi2NhRi0jE\nIhK1sB2bWDLCxouXntQ5TcA4AarKt5/+Nl965Essb1jOV7Z8hVWNq+gbyXHjTV/l9f13sHJnDyO7\nk2QlyuiKJdjrz8ResRxaWvA8d6JYXz2i/rdCcWyU3PDQtMXuSDRGqrmZdHMLsVQaHb8zm3rnPuXu\nLNnYRNOixTQtXhoEh8VLaFq0hIb2RUe94BVGR+jbt4f+vbvp37eH/n17GOw5cEQR+2QChHGkSqnI\n4IH9YQmlda6zc1LU18mLYHhRrJQ8CqNl8qNl8qMVCiNl8mMV8iPBssJYBd+dvI6IJaQao6SaYqSb\nY6Sb4qSax9MxUs0xRIT+MCgM7A+CRH50slqsoT1B+7IMbV1pnLgNCr4flqR8RZUpc8V3lWrFo1r2\ncMvBfPzz+OSWPdyKTyLj0NCWINMWp6EtQUNrgoYwnWqKnVTAC26YfNyyT6Xs4pb9I/IwnidVDYJC\nIkKsdp6MEHGsF318lQkYJ2C0PMqb7ngT57Sdw/965f8iXc4x9otvkr3jW/jP+mR7YwynExzYuJae\ncongvU4Bsaxpi/CRaBQnGiWeaSTd3EKqqZl0S2swb24l1dxMLJma04F3bqXCYPd+Bg7sI9PabgLE\nAuVWPUb7ioz0FRg5XCA/UsFzffzwoj9+dxx8DtO1d8y1wcH1j7jwH00sGSHZGAuCQmOMZDhPNDhU\nih75kTK54RK54TL5kTLZ4TJuefo2CBFoWpyifXma9mWZIEgsSxNLLqzeQwuZCRgnqDfbw+KBXejP\nb2bk7vsZeTZOKetweHELB9asYCA3RjyVZtNrX89LtryWRKYhqNuNmD4DRv2pr2SHS4wcLjByuBjM\nwwCRHSrV3sMQS0awIxZ2xMKKyGTaHk8LVsTCti1sJ5gi4Tbjn+2IBNUeTrDcidmkGqMTQSISPbFu\n4KpKpeSRGy6RHy6TGynje0pbV5rWzjROzHQrn0vm4YMnopyl9e+voe83fYzsSVL1G/htVxcjZzVT\nKuZpSqW4/O3v4iWvejWOGVBnHIVb9SbqxKtlb+Iu/sg7+5q0q7huUO8dTEH1hVvxcKvB8mpYfTK1\nbtyJ2zR1JFm8upENr1hC06IETR1JmhYlicbn339pESGWiBBLpGldmj7+Dsa8Nf/+ul5kXlXY859F\ninYzD65bSy7mEVGXpctXsPkNb2LN5guP28vCOPWNN+yO9ge9Z8b6i4wOlIJ5f9BwOlO1d/pONGyg\njVpEojbxtBMsi4aNnGEvm3RzjKZFQVBINkTNc8SMOWEChm3x29dfxb69zwIVOs+5gCve9naWrFs/\n11kz6sBzfUr5KsVslVKuQrnoUi4E3RuDyaNcrIZzd6L7YzFboVo6sh4+1RiloT3Bsg3NNLQngsbT\ntgSxROSo1UFiibnYGwvWaR8wcp7NE32j9LVs4o8/8Ie87Ox1c50lY4bcikcpH/RjL+WrlAsupXyQ\nLmWrFPNVStkKxVyVYq5KKVelUjz2WBEnbh/RcyXZGKVpUZJEOuhV09CeoDHsXeOcYF2+YSx0dQ0Y\nInIV8BWCV7R+U1W/MGV9M3ALsAYoAe9V1admsu9saU5F6fyDT/AHZy5iw+JjD2ozXjylXJXhw4WJ\nBt7RvgKFsQrlgks5X6VUcPGqRx/EZ0WERDpKIuMQTwUX+3jaIRFO8XSURNohlooQjUeIJSM48chJ\ndac0jNNF3QKGiNjAjcBrgG7gYRG5U1W312z2P4EnVPXNIrIh3P6KGe47W/nkg1tMqeLFpr5SLrrk\nhsuTQeFwIQgSfQXK+cmSgGUJDe0Jkg1RmjqSxFIR4sngYh9POcTG0zXLxkfUGoYxe+pZwrgA2Kmq\nuwFE5DbgGqD2or8R+AKAqj4jIitFZBGwegb7GvNQtewxfChPfqRMMVulkK1QCufFbIVitkoxW6GU\nq+L7R3bpTjXFaFqUYO35HRMNvE0dSRra4lj2qTlK2jAWknoGjE7gQM3nbuDCKdtsBd4C/FxELgBW\nAF0z3NeYQ27VY/hQgaHefDjlGDqYn/ZRDk7cDqqCMlEyrXEWrcwQz0RJZqJBG0FHksaOxLzsEmoY\nxqS5/h/6BeArIvIE8CTwOHBCj6UUkeuA6wCWL18+6xk0oJircGjXKH37skFwOJhntK8wMTbAsoWm\nRUk6VjZw5kVLaF6SItMSJ5EJ2glOdKCXYRjzUz0DRg+wrOZzV7hsgqqOAdcCSFDhvAfYDSSOt2/N\nMW4GboZgpPcs5f20paqMHC5wcNcoB3eNcmjXKCOHC0DwjKCmjgStS1Os3dxB69I0LUtSNC5KYJsq\nI8M45dUzYDwMrBORVQQX+3cA76rdQESagIKqVoD3AT9T1TEROe6+xuxwKx59+8YmgsPB3aMTDc7x\nlMPiNY2cedESFq9ppGN5xpQWDOM0VreAoaquiFwP3E3QNfYWVd0mIu8P198EnAn8s4gosA34o2Pt\nW6+8nk48z6dvzxjdO4bpfmaYQ3tGJx4417w4yepN7Sxe08iSNY00LUqankaGYUwwDx88xamvDHTn\nJgJE786R4MmhAu3LMnSe0cTSM5pZvLqBRLp+L3wyDGN+Mg8fPM0VxirseqyPnh3DdD87PFHF1Lw4\nyYaXL6ZrQzOd65qJp80jpA3DmDkTME4RqsrhPWP89r5udj3Wh+8p6ZYYqza107W+mc4zmkk3m/dd\nGIZx8kzAWODcqsfOR/p48v5u+vZlceI2L7m0k7Mu6aR5iWmDMAxj9piAsUBlh0o89bMetv+il1Ku\nSvPiJJe+4wzWv3yxGQBnGEZdmCvLAqKq9D47wpP3d7N76wCosvKcNs7e0kXX+mZTmjAMo65MwFgg\nskMlfvSPT9K3L0ssFeHcVy/jJZd20tCWmOusGYZxmjABYwEY7Mnx/a9upVpy2fLuDZxxwSIzgM4w\njBedCRjzXM+OYe666UmcqMWbP/pS2rrMO5ENw5gbJmDMY889cpif3LqdxrYEV3/oXDIt8bnOkmEY\npzETMOapJ36yn1/+106WrG3k9X9yDvGUGWRnGMbcMgFjnlFf+eV/7WTrvQdYc347r752IxHHtFcY\nhjH3TMCYR9yqx09vfZqdj/ZxzpYuLn7bOvOOacMw5g0TMOaJUr7KD296kt7nRrjoLWs59zXLzLgK\nwzDmFRMw5oHsUIkffG0rI4cLvOa9GznjgsVznSXDMIznMQFjjg325vj+PwRjLK7+0010bWiZ6ywZ\nhmFMywSMOTR0MM8dX34cscSMsTAMY94zAWOODB8KggUivOnPzqN5cWqus2QYhnFM1lxn4HQ00lfg\nji8/jqrypg+bYGEYxsJQ14AhIleJyA4R2Skin5xmfaOIfF9EtorINhG5tmbdXhF5UkSeEJFT5r2r\no/1F7vjy43iecs2Hz6NlqQkWhmEsDHWrkhIRG7gReA3QDTwsIneq6vaazT4IbFfVq0WkHdghIt9W\n1Uq4fouqDtQrjy+2sYEit3/5MaoVjzf92Xm0dpo2C8MwFo56ljAuAHaq6u4wANwGXDNlGwUyEgw4\nSANDgFvHPM2Z7FCJO/7+caolj2tuOI+2rsxcZ8kwDOOE1DNgdAIHaj53h8tqfQ04E+gFngRuUFU/\nXKfAT0TkURG57mgnEZHrROQREXmkv79/9nI/i3LDZW7/8uOU8i5vvOFc2pebYGEYxsIz143eVwJP\nAEuBc4GviUhDuO6Vqnou8DrggyJy6XQHUNWbVXWzqm5ub29/UTJ9IvKjZW7/8mMUsxWu/tAmOlY0\nHH8nwzCMeaieAaMHWFbzuStcVuta4Lsa2AnsATYAqGpPOO8DvkdQxbWgFMYq3PHlxymMVrj6T89l\n8arGuc6SYRjGSatnwHgYWCciq0QkCrwDuHPKNvuBKwBEZBGwHtgtIikRyYTLU8BrgafqmNdZV8xW\nuP3Lj5MdKvGG6zexZI0JFoZhLGx16yWlqq6IXA/cDdjALaq6TUTeH66/CfgccKuIPAkI8AlVHRCR\n1cD3wofvRYB/U9Uf1Suvs01V+cHXtpIdKPKG6zexdF3TXGfJMAzjBavrSG9VvQu4a8qym2rSvQSl\nh6n77QY21TNv9TTUm6dvX5ZL33EGneub5zo7hmEYs2KuG71PSfu2DQKwalPbHOfEMAxj9piAUQf7\ntw3S2pki3WzewW0YxqnDBIxZVim5HNw5yvKzWuc6K4ZhGLPKBIxZ1v3MML6nJmAYhnHKMQFjlu3f\nNogTs003WsMwTjkmYMwiVWX/tiG6NjRjR8xPaxjGqcVc1WbR8KEC2aGSqY4yDOOUZALGLNofdqdd\nfpZ5L7dhGKee4wYMEXmziDTWfG4SkTfVN1sL0/5tgzQvTtLQmpjrrBiGYcy6mZQwPq2qo+MfVHUE\n+HT9srQwVcsePc+NsPwlpjrKMIxT00wCxnTb1PWRIgtRz45hfFdZYdovDMM4Rc0kYDwiIn8nImvC\n6e+AR+udsYVm/7ZBIlGLpWvNgwYNwzg1zSRg/ClQAf6D4DWrJYJ3cRshVWXftkG61jdjO6YfgWEY\np6bjVi2pah745IuQlwVrtK/I2ECJc1+9fK6zYhiGUTcz6SV1j4g01XxuFpG765uthWXfRHda035h\nGMapayb1J21hzygAVHUY6Khflhae/dsGaVqUpLHddKc1DOPUNZOA4YvIRF2LiKwAtH5ZWljcikfP\nsyNmsJ5hGKe8mQSMTwG/EJF/FZH/C/wM+POZHFxErhKRHSKyU0Se1w4iIo0i8n0R2Soi20Tk2pnu\nO1/0PDeCV/VNdZRhGKe8mTR6/0hEzgdeHi76sKoOHG8/EbGBG4HXAN3AwyJyp6pur9nsg8B2Vb1a\nRNqBHSLybcCbwb7zwv6nBrEdi07z3m7DME5xM+0D6gF9wBiwUUQuncE+FwA7VXW3qlYIuuReM2Ub\nBTIiIkAaGALcGe47L+zbNkjnGc1EovZcZ8UwDKOuZtJL6n0E1VB3A58J5381g2N3AgdqPneHy2p9\nDTgT6AWeBG5QVX+G+47n7zoReUREHunv759BtmbPaH+B0b6iab8wDOO0MJMSxg3Ay4B9qroFOA8Y\nOfYuM3Yl8ASwFDgX+JqINJzIAVT1ZlXdrKqb29vbZylbM7N/2xCAeRyIYRinhZkEjJKqlgBEJKaq\nzwDrZ7BfD7Cs5nNXuKzWtcB3NbAT2ANsmOG+c27ftkEa2hM0LUrOdVYMwzDqbiYBozscuHc7cI+I\n3AHsm8F+DwPrRGSViESBdwB3TtlmP3AFgIgsIghEu2e475xyqx49O4ZZsdFURxmGcXqYSS+pN4fJ\nvxKR+4BG4Ecz2M8VkesJ2jxs4BZV3SYi7w/X3wR8DrhVRJ4EBPjEeA+s6fY94W9XRwefG8Wt+OZx\n5oZhnDZO6DHlqvrACW5/F3DXlGU31aR7gdfOdN/5ZN+2QeyIRecZzXOdFcMwjBeFebTqSdq/bZCl\n6xpxYqY7rWEYpwcTME7C2GCR4UMFM7rbMIzTigkYJ2G8O60JGIZhnE6O2oYhIlmmf8igAKqqJzRe\n4lSyf9sgmZY4zYtNd1rDME4fRw0Yqpp5MTOyUHiuT/czw5xx4WKCJ5oYhmGcHmbcS0pEOoD4+GdV\n3V+XHM1zB3eOUC17LDfjLwzDOM3M5FlSbxSR5whGYT8A7AV+WOd8zVv7tw1h2ULXBtOd1jCM08tM\nGr0/R/Bo82dVdRXByOxf1TVX89i+bYMsWdtENH5CQ1gMwzAWvJkEjKqqDgKWiFiqeh+wuc75mpey\nQyWGevPm6bSGYZyWZnKbPCIiaYJHnH9bRPqAfH2zNT8deNo8ndYw5ptqtUp3dzelUmmuszKvxeNx\nurq6cBznpI8xk4BxDVAE/gz4PYJnSX32pM+4gA0fzGM7Fi1LU3OdFcMwQt3d3WQyGVauXGl6Lh6F\nqjI4OEh3dzerVq066ePMpErqj4Elquqq6j+r6j+EVVSnnexQiUxL3PxRGsY8UiqVaG1tNf8vj0FE\naG1tfcGlsJkEjAzwYxH5uYhcHz6G/LSUHSyRaYnNdTYMw5jCBIvjm43f6LgBQ1U/o6pnAR8ElgAP\niMhPXvCZF6DxEoZhGMbp6ESeJdUHHAIGgY76ZGf+cisexWyVTKsJGIZhnJ5mMnDvAyJyP/BToBX4\nH6p6Tr0zNt9kh4K6v7QpYRiGcZqaSS+pZcCHVfWJemdmPhsPGKZKyjDmr898fxvbe8dm9Zgblzbw\n6avPOu52//Iv/8KXvvQlRIRzzjmHz33uc7z3ve9lYGCA9vZ2vvWtb7F8+XK+853v8JnPfAbbtmls\nbORnP/vZrOa3nmbyitY/P9mDi8hVwFcIXrP6TVX9wpT1HyPoqjuelzOBdlUdEpG9QBbwAFdV53Sw\nYG6oDJiAYRjG823bto3Pf/7zPPjgg7S1tTE0NMQf/uEfTky33HILH/rQh7j99tv57Gc/y913301n\nZycjIyNznfUTUrfnW4iIDdwIvAboBh4WkTtVdfv4Nqr6ReCL4fZXA3+mqkM1h9ky/o7vuZYdKiEC\nqWbTS8ow5quZlATq4d577+Vtb3sbbW1tALS0tPDQQw/x3e9+F4Df//3f5+Mf/zgAF198Me95z3t4\n+9vfzlve8pY5ye/JqucLlC4AdqrqblWtALcRDAI8mncC/17H/Lwg2cESqaYYtm3eOWUYxsm76aab\n+PznP8+BAwd46UtfyuDgwhnWVs+rXydwoOZzd7jseUQkCVwF/L+axQr8REQeFZHrjnYSEblORB4R\nkUf6+/tnIdvTyw6VTA8pwzCmdfnll/Od73xn4uI/NDTERRddxG233QbAt7/9bS655BIAdu3axYUX\nXshnP/tZ2tvbOXDgwFGPO9/Ml0euXg38ckp11CtVtSd8D8c9IvKMqj6vdUhVbwZuBti8efN0bwic\nFdnBEkvWNtbr8IZhLGBnnXUWn/rUp3jVq16Fbducd955fPWrX+Xaa6/li1/84kSjN8DHPvYxnnvu\nOVSVK664gk2bNs1x7meungGjh6CH1biucNl03sGU6ihV7QnnfSLyPYIqrjnpTuB7PrmRsmnwNgzj\nqMYbuGvde++9z9tuvF1jIapnldTDwDoRWSUiUYKgcOfUjUSkEXgVcEfNspSIZMbTwGuBp+qY12PK\nj1ZQX80YDMMwTmt1K2Goqisi1wN3E3SrvUVVt4nI+8P1N4Wbvhn4sarWPjJ9EfC98NknEeDfVPVH\n9crr8UyMwTBtGIZhnMbq2oahqncBd01ZdtOUz7cCt05ZthuYNxV72UEzaM8wDMP0EZ0BM8rbMAzD\nBIwZyQ6ViKcdnJg911kxDMOYMyZgzEBu0DzW3DAMwwSMGTCD9gzDOFHve9/72L59+/E3XEDmy8C9\neUtVyQ6WWL6xda6zYhjGAvLNb35zrrMw60zAOI5Sropb9U0JwzAWgh9+Eg49ObvHXHw2vO4Lx9wk\nn8/z9re/ne7ubjzP4y//8i/5+te/zpe+9CU2b95MOp3mhhtu4Ac/+AGJRII77riDRYsW3tuuTZXU\ncZgeUoZhHM+PfvQjli5dytatW3nqqae46qqrjlifz+d5+ctfztatW7n00kv5xje+MUc5fWFMCeM4\nzKA9w1hAjlMSqJezzz6bj3zkI3ziE5/gDW94w8SDBsdFo1He8IY3APDSl76Ue+65Zy6y+YKZgHEc\nZtCeYRjHc8YZZ/DYY49x11138Rd/8RdcccUVR6x3HIfwyRXYto3runORzRfMBIzjyA6ViMRsYinz\nUxmGMb3e3l5aWlp497vfTVNT0ynZ4A0mYBxXNhyDMX53YBiGMdWTTz7Jxz72MSzLwnEcvv71r/PR\nj350rrM160zAOI7sUIlMi3ktq2EYR3fllVdy5ZVXHrHs/vvvn0jncrmJ9Fvf+lbe+ta3vlhZm1Wm\nl9RxBAHDtF8YhmGYgHEMlZJLOe+aHlKGYRiYgHFMuaEyYHpIGYZhgAkYx2QG7RmGYUwyAeMYzKA9\nwzCMSXUNGCJylYjsEJGdIvLJadZ/TESeCKenRMQTkZaZ7PtiyA6WsCwh2Wh6SRmGYdQtYIiIDdwI\nvA7YCLxTRDbWbqOqX1TVc1X1XODPgQdUdWgm+74YskMl0i0xLMuMwTAM44W77LLLeOSRR+Y6Gyet\nniWMC4CdqrpbVSvAbcA1x9j+ncC/n+S+dZEdLJFuNtVRhmHMnKri+/5cZ6Mu6jlwrxM4UPO5G7hw\nug1FJAlcBVx/EvteB1wHsHz58heW4ymyQyW6NjTP6jENw6ifv/nN3/DM0DOzeswNLRv4xAWfOOY2\ne/fu5corr+TCCy/k0Ucf5eMf/zg33XQT5XKZNWvW8K1vfYt0On3EPul0emJA33/913/xgx/8gFtv\nvXVW8z7b5kuj99XAL1V16ER3VNWbVXWzqm5ub2+ftQx5nk9+tGx6SBnG/9/e3QdXVZ17HP8+CRHC\nm4AkXAUEvJVBJIIQEAV50Sv1rUAcRAVUdDroaC3OUAsyOhdacVC5eu2LWAUEexEuXl6klFFKa0Et\nvZCkuYhggTqhBREIb014CYY894+zE05CCCfJOZyc8PvMODl77bV31soyedhr7/0siciOHTt44okn\nWLduHXPnzmXt2rXk5uaSmZnJq6++Gu/mRUUsrzD2AB3DtjsEZVW5nzPTUTU9NiaOHS4G1xNSIonk\nfFcCsdSpUyf69+/PqlWr2Lp1KwMGDADg1KlT3HjjjXFrVzTFMmBsAq42sy6E/tjfD4ypXMnMLgUG\nA+NqemwsKa25iNREs2bNgNA9jNtuu41FixZVWz88oenJkydj2rZoidmUlLuXELon8RGwDVji7l+Y\n2eNm9nhY1SxgjbsfO9+xsWprVfTSnojURv/+/fnss8/YuXMnEFptb/v27WfVa9euHdu2baO0tJTl\ny5df6GbWSkyz1br7amB1pbI3K23PB+ZHcuyFVBYwmitTrYjUQFpaGvPnz+eBBx6guDiUXuiFF16g\na9euFerNnDmTu+++m7S0NDIzMytktK2vlN78HAoPniS15SU0SkmOd1NEpJ7r3LkzW7ZsKd++5ZZb\n2LRp01n1wlOeJ2Ka8/rylFS9o7TmIiIVKWCcgwKGiEhFChhV8FKn6FCxHqkVEQmjgFGF44WnOF1S\nqisMEZEwChhVUFpzEZGzKWBUQS/tiYicTQGjCmde2tM7GCJSvSNHjvDGG2/U+vhESnmugFGFooMn\nuaRJMo2bpsS7KSJSz9U1YCQSvbhXhcJDJ3X/QiQBffPiixRvi25688bXdONfpk495/4pU6bwt7/9\njV69ejF06FA2b97M4cOH+fbbb3nhhRcYMWIE+fn53HHHHQwcOJA//elPtG/fng8++IDU1FQA3n//\nfZ544gmOHDnC3Llzufnmm6Pah2hRwKhC4SGlNReRyMycOZMtW7aQl5dHSUkJx48fp2XLlhQUFNC/\nf3+GDx8OhNKfL1q0iLfffpvRo0ezdOlSxo0L5VwtKSlh48aNrF69munTp7N27dp4dumcFDCqUHjo\nJFd859J4N0NEaqi6K4ELwd2ZOnUq69evJykpiT179rBv3z4AunTpQq9evQDo06cP+fn55cfdc889\nVZbXNwoYlRSfKOHUiRKaa0pKRGpo4cKFHDhwgJycHFJSUujcuXN56vLGjc88RJOcnMyJEyfKt8v2\nJScnU1JScmEbXQO66V2JHqkVkZpo0aIFhYWFABw9epT09HRSUlL4+OOP2bVrV5xbF126wqhEL+2J\nSPdR2v8AAA5vSURBVE1cdtllDBgwgB49etC3b1++/PJLMjIyyMzMpFu3bvFuXlQpYFSiKwwRqan3\n3nvvvHXC05//6Ec/Kv8cnvK8bdu29foehqakKik8dJKkRkbTFpfEuykiIvVKTAOGmd1uZn81s51m\nNuUcdYaYWZ6ZfWFm68LK883s82DfBXsNsujQSVq0boIl2fkri4hcRGI2JWVmycAvgduA3cAmM1vp\n7lvD6rQC3gBud/e/m1l6pdMMdfeCWLWxKnppT0SkarG8wugH7HT3r9z9FLAYGFGpzhhgmbv/HcDd\n98ewPREpPKiFk0REqhLLgNEe+EfY9u6gLFxXoLWZ/dHMcszsobB9DqwNyiec65uY2QQzyzaz7AMH\nDtSpwSXfnub4P0/pCkNEpArxfkqqEdAHuBVIBTaY2Z/dfTsw0N33BNNUvzOzL919feUTuPtbwFsA\nmZmZXpfGFB0qBvSElIhIVWJ5hbEH6Bi23SEoC7cb+MjdjwX3KtYDPQHcfU/wdT+wnNAUV0yVvYPR\nXAFDROro+9//Plu3bj1/xTq48847OXLkyFnl06ZNY9asWVH/frEMGJuAq82si5ldAtwPrKxU5wNg\noJk1MrOmwA3ANjNrZmYtAMysGTAM2EKMnVkHQwFDROpmzpw5dO/ePabfY/Xq1bRq1Sqm3yNczKak\n3L3EzH4AfAQkA/Pc/QszezzY/6a7bzOzD4HNQCkwx923mNlVwHIzK2vje+7+YazaWqbw4EkwaN5a\nCyeJJKJPlmyn4B9FUT1n247NuXl012rrHDt2jNGjR7N7925Onz7N888/z+zZs5k1axaZmZnMnTuX\nl156iVatWtGzZ08aN27ML37xC8aPH09qaip/+ctf2L9/P/PmzePdd99lw4YN3HDDDcyfPx+ARYsW\n8eKLL+Lu3HXXXbz00ksAdO7cmezsbNq2bcuMGTNYsGAB6enpdOzYkT59+kT15wAxvofh7quB1ZXK\n3qy0/QrwSqWyrwimpi6kokMnaXZpY5Ib6X1GEYnchx9+yBVXXMFvf/tbIJRTavbs2QB8/fXX/PSn\nPyU3N5cWLVpwyy230LPnmT9vhw8fZsOGDaxcuZLhw4fz2WefMWfOHPr27UteXh7p6elMnjyZnJwc\nWrduzbBhw1ixYgUjR44sP0dOTg6LFy8uT7Heu3fvxAsYiabwkB6pFUlk57sSiJWMjAwmTZrE5MmT\nufvuuyssgLRx40YGDx5MmzZtALj33nvZvn17+f7vfe97mBkZGRm0a9eOjIwMAK699lry8/PZtWsX\nQ4YMIS0tDYCxY8eyfv36CgHjk08+ISsri6ZNmwKUr8ERbQoYYQoPnaRdF62DISI107VrV3Jzc1m9\nejXPPfcct956a8THlqU2T0pKqpACPSkpiZKSElJS6s9S0Zp7CZSWOkVaaU9EauHrr7+madOmjBs3\njmeeeYbc3NzyfX379mXdunUcPnyYkpISli5dWqNz9+vXj3Xr1lFQUMDp06dZtGgRgwcPrlBn0KBB\nrFixghMnTlBYWMhvfvObqPSrMl1hBI4fLaa01PXSnojU2Oeff84zzzxDUlISKSkpzJ49uzwjbfv2\n7Zk6dSr9+vWjTZs2dOvWjUsvjXwm4/LLL2fmzJkMHTq0/Kb3iBEVk2b07t2b++67j549e5Kenk7f\nvn2j2r8y5l6nd93qlczMTM/Orl2ewr07j7BsVi53PXkdnTPaRrllIhIr27Zt45prrol3M6pVVFRE\n8+bNKSkpISsri0cffZSsrKwL3o6qflZmluPumZEcrympgBZOEpFYmTZtGr169aJHjx506dKlwg3r\nRKIpqYBe2hORWInFW9fxoCuMQOGhYho3a8QlTRRDRUSqooARUFpzEZHqKWAE9NKeiEj1FDAAd9dK\neyIi56GAARQfK6Gk+LSuMESkVsyMSZMmlW/PmjWLadOmxa9BMaKAgZ6QEpG6ady4McuWLaOgoCDe\nTYkpPRJEkNYcvYMhkug+nv8W+3d9FdVzpne6iqHjz7lKNACNGjViwoQJvPbaa8yYMaPCvvz8fB59\n9FEKCgpIS0vjnXfe4corr2T8+PG0bNmS7OxsvvnmG15++WVGjRoFwCuvvMKSJUsoLi4mKyuL6dOn\nR7VPtaUrDHSFISJ19+STT7Jw4UKOHj1aofypp57i4YcfZvPmzYwdO5Yf/vCH5fv27t3Lp59+yqpV\nq5gyZQoAa9asYceOHWzcuJG8vDxycnJYv/6s1anjQlcYhAJGo5QkmjSvP1khRaTmznclEEstW7bk\noYce4mc/+xmpqanl5Rs2bGDZsmUAPPjgg/z4xz8u3zdy5EiSkpLo3r07+/btA0IBY82aNVx//fVA\nKK3Ijh07GDRo0AXsTdUUMKD8CalghT8RkVp5+umn6d27N4888khE9cPTmZfl9XN3nn32WR577LGY\ntLEuYjolZWa3m9lfzWynmU05R50hZpZnZl+Y2bqaHBstemlPRKKhTZs2jB49mrlz55aX3XTTTSxe\nvBiAhQsXVlhcqSrf/e53mTdvHkVFoaVm9+zZw/79+2PX6BqIWcAws2Tgl8AdQHfgATPrXqlOK+AN\nYLi7XwvcG+mx0VR46CTNFTBEJAomTZpU4Wmpn//857zzzjtcd911/PrXv+b111+v9vhhw4YxZswY\nbrzxRjIyMhg1ahSFhYWxbnZEYjkl1Q/YGazPjZktBkYAW8PqjAGWufvfAdx9fw2OjYrSUqfTtZdx\nxdWton1qEblIlF0NALRr147jx4+Xb3fq1Ik//OEPZx0zf/78c55j4sSJTJw4MfoNraNYBoz2wD/C\ntncDN1Sq0xVIMbM/Ai2A19393QiPjYqkJOPfHonZxYuISIMR75vejYA+wK1AKrDBzP5ckxOY2QRg\nAsCVV14Z9QaKiEhILG967wE6hm13CMrC7QY+cvdj7l4ArAd6RngsAO7+lrtnuntmWlpa1BovIomj\nIa0cGivR+BnFMmBsAq42sy5mdglwP7CyUp0PgIFm1sjMmhKadtoW4bEiIjRp0oSDBw8qaFTD3Tl4\n8CBNmtTt4Z6YTUm5e4mZ/QD4CEgG5rn7F2b2eLD/TXffZmYfApuBUmCOu28BqOrYWLVVRBJXhw4d\n2L17NwcOHIh3U+q1Jk2a0KFDhzqdwxpSVM7MzPTs7Ox4N0NEJGGYWY67Z0ZSV7mkREQkIgoYIiIS\nEQUMERGJSIO6h2FmB4BdtTy8LdCQVj9paP2BhtenhtYfaHh9amj9gbP71MndI3onoUEFjLows+xI\nb/wkgobWH2h4fWpo/YGG16eG1h+oW580JSUiIhFRwBARkYgoYJzxVrwbEGUNrT/Q8PrU0PoDDa9P\nDa0/UIc+6R6GiIhERFcYIiISEQUMERGJyEUfMC7k2uEXipnlm9nnwVrpCZdcy8zmmdl+M9sSVtbG\nzH5nZjuCr63j2caaOkefppnZnmCc8szszni2sSbMrKOZfWxmW83sCzObGJQn7DhV06eEHCcza2Jm\nG83s/4L+TA/Kaz1GF/U9jGDt8O3AbYTW5tgEPODuUV8K9kIys3wgM1hjJOGY2SCgCHjX3XsEZS8D\nh9x9ZhDYW7v75Hi2sybO0adpQJG7z4pn22rDzC4HLnf3XDNrAeQAI4HxJOg4VdOn0STgOJmZAc3c\nvcjMUoBPgYnAPdRyjC72K4zytcPd/RRQtna4xJG7rwcOVSoeASwIPi8g9IucMM7Rp4Tl7nvdPTf4\nXEhoHZv2JPA4VdOnhOQhZQuFpwT/OXUYo4s9YFS1dnjC/g8SxoG1ZpYTLGHbELRz973B52+AdvFs\nTBQ9ZWabgymrhJm+CWdmnYHrgf+lgYxTpT5Bgo6TmSWbWR6wH/idu9dpjC72gNFQDXT3XsAdwJPB\ndEiD4aF51IYwlzobuAroBewF/iO+zak5M2sOLAWedvd/hu9L1HGqok8JO07ufjr4W9AB6GdmPSrt\nr9EYXewBI+K1wxOJu+8Jvu4HlhOaekt0+4I55rK55v1xbk+dufu+4Be6FHibBBunYF58KbDQ3ZcF\nxQk9TlX1KdHHCcDdjwAfA7dThzG62ANGg1s73MyaBTfsMLNmwDBgS/VHJYSVwMPB54cJrQef0Mp+\naQNZJNA4BTdU5wLb3P3VsF0JO07n6lOijpOZpZlZq+BzKqGHe76kDmN0UT8lBRA8IvefnFk7fEac\nm1QnZnYVoasKCK3Z/l6i9cnMFgFDCKVh3gf8O7ACWAJcSSiF/Wh3T5ibyOfo0xBC0xwO5AOPhc0t\n12tmNhD4BPgcKA2KpxKa80/IcaqmTw+QgONkZtcRuqmdTOjiYIm7/8TMLqOWY3TRBwwREYnMxT4l\nJSIiEVLAEBGRiChgiIhIRBQwREQkIgoYIiISEQUMkXrAzIaY2ap4t0OkOgoYIiISEQUMkRows3HB\nGgN5ZvarILlbkZm9Fqw58HszSwvq9jKzPwdJ65aXJa0zs++Y2dpgnYJcM/vX4PTNzex/zOxLM1sY\nvHksUm8oYIhEyMyuAe4DBgQJ3U4DY4FmQLa7XwusI/QWN8C7wGR3v47Q28Nl5QuBX7p7T+AmQgnt\nIJQd9WmgO6FkdwNi3imRGmgU7waIJJBbgT7ApuAf/6mEEreVAv8d1PkvYJmZXQq0cvd1QfkC4P0g\nz1d7d18O4O4nAYLzbXT33cF2HtCZ0KI3IvWCAoZI5AxY4O7PVig0e75Svdrm2ykO+3wa/X5KPaMp\nKZHI/R4YZWbpUL42cidCv0ejgjpjgE/d/Shw2MxuDsofBNYFK7ntNrORwTkam1nTC9oLkVrSv2BE\nIuTuW83sOWCNmSUB3wJPAscILU7zHKEpqvuCQx4G3gwCwlfAI0H5g8CvzOwnwTnuvYDdEKk1ZasV\nqSMzK3L35vFuh0isaUpKREQioisMERGJiK4wREQkIgoYIiISEQUMERGJiAKGiIhERAFDREQi8v+a\nQAqLdPJOhwAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7f0044963908>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"for k, v in res.items():\n", | |
" plt.plot(x, v.history['val_acc'], label=k)\n", | |
"\n", | |
"plt.xlabel('epoch')\n", | |
"plt.ylabel('val acc')\n", | |
"plt.title('hidden 512-512 acc')\n", | |
"plt.legend()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 9, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.legend.Legend at 0x7f0028065d68>" | |
] | |
}, | |
"execution_count": 9, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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kMMqB+0RknYh8UkQmjXSlxpLu7rXbX9yL5/nFro4xxoyYQp649zVVXUjw/O3J\nwEMi8kAhOxeRd4nIyyLyqoh8vp/yK0TkmXDYKCKeiFSHZfUi8nxY9vQBvq/DyrrXGmPGgwO52mw3\n8CbQBEwcamURcYEfAWcDC4ALRWRB/jqq+h1VXaaqy4AvAA+pan4f1dPD8lHdK8u61xpjxoNCLtz7\nRxF5EPgfoAb4uKouKWDfq4BXVXWLqmaAWwlOoA/kQuCWAvY76lj3WmPMeFBIC2M68C+qulBVr1LV\nTQXueyqwPW9+R7jsLUSkBHgX8Nu8xQo8ICLrReTygV5ERC4XkadF5Ok9e/YUWLXhZ91rjTFHukLO\nYXxBVZ8Z4Xq8D3h0v8NRbwsPVZ0N/JOIvGOA+l2vqitUdUVdXd0IV3Ng1r3WGHOkG8k75jUQtE66\nTQuX9edD7Hc4SlUbwvFu4PcEh7hGLetea4w50o1kYDwFHCsis0UkRhAKd+y/kohUAqcCf8hbVioi\n5d3TwJnAxhGs6yGz7rXGmCPdiAWGquaATwL3Ai8Cv1bVF0TkEyLyibxVzwXuU9X8241MAh4RkWeB\nJ4E7VfWekarrcLHutcaYI1nBz8M4GKp6F3DXfsuu22/+JuCm/ZZtAZaOZN1GQn732inHVhW7OsYY\nM6zsqT/DyLrXGmOOZBYYw8y61xpjjlQWGMPMutcaY45UFhjDzLrXGmOOVBYYw8y61xpjjlQWGCPA\nutcaY45EFhgjYPq8ahxXeGHdzmJXxRhjho0FxgiIJSMsP2smrzy5i5efeLPY1THGmGFhgTFCVr5n\nFpOPqeShX75M8+7OYlfHGGMOmQXGCHFch3d+bCGOK9x3wwt4OTsBbowZ2ywwRlB5dYIzLpnPntfb\nePz214pdHWOMOSQWGCNszrI6Fp86lWcf2E79843Fro4xxhw0C4zD4OTzjqFmahn/818v0tFstwwx\nxoxNFhiHQSTqctbHF5LLeNz/s034vha7SsYYc8AsMA6TqqNKeceH5tLw8j423LOt2NUxxpgDZoFx\nGM07aTLHrpzEk/+9lTdebS52dYwx5oBYYBxGIsJpHz6O8uo49934AqmObLGrZIwxBbPAOMxiyQhn\nXraIzuYMf/7FS6ja+QxjzNhggVEEk2ZVsPrco9nyzB5eeLih2NUxxpiCWGAUybI105mxsJpHbnuV\nxh3txa6OMcYMaUQDQ0TeJSIvi8irIvL5fspPE5EWEXkmHL5S6LZjnTjCmksXEC+JcO9PNtLa2FXs\nKhljzKBGc4VdAAAgAElEQVRGLDBExAV+BJwNLAAuFJEF/ay6TlWXhcPVB7jtmFZSEeOsjy+iszXD\nr7/1FFue2VPsKhljzIBGsoWxCnhVVbeoaga4FXj/Ydh2TJly7AQu+OJKKmqT3H3d8zz6m832pD5j\nzKg0koExFdieN78jXLa/k0XkORG5W0QWHuC2iMjlIvK0iDy9Z8/Y/IVeWZfkg1csZ9GpU3nmge3c\n/v/+lfZ9qWJXyxhj+ij2Se8NwAxVXQL8ALj9QHegqter6gpVXVFXVzfsFTxcIlGXUy88jjP/biFN\nDe386ptP8fqmpmJXyxhjeoxkYDQA0/Pmp4XLeqhqq6q2h9N3AVERqS1k2yPVsSsncf4XVlBSEeOP\nP3iWJ+7YYveeMsaMCiMZGE8Bx4rIbBGJAR8C7shfQUSOEhEJp1eF9WkqZNsjWdVRpZz3+RXMO2ky\nT99Vzx3f+ysdLXaXW2NMcY1YYKhqDvgkcC/wIvBrVX1BRD4hIp8IVzsP2CgizwLfBz6kgX63Ham6\njkbRmMuaS+ZzxiXz2LWllV9/8ykaXtlX7GoZY8YxOZJuTbFixQp9+umni12NYdfU0M4912+kZXcn\nC942hSWnT6d6Smmxq2WMOQKIyHpVXVHIupGRrow5dDVTyzj/Cyt47Hev8dLjb/DCup1Mm1fF4tOm\nMWtJLY4jxa6iMWYcsBbGGNPVnmHTIzvZ+FAD7fvSlNckWHzqNOafMplEabTY1TPGjDEH0sKwwBij\nfM9n67ONPPfnHezc3Ewk6jD3xKNYcvo0aqaWFbt6xpgxwg5JjQOO63D08okcvXwijTvaef7P23n5\niTfZ9MhOps6dwKJTpzFzUQ3RuFvsqhpjjhDWwjiCpNqzbHo0OFzVtjeFG3GYNr+K2UtqmbWkltLK\neLGraIwZZeyQ1Djn+8rOzc3UP9vI1uf20NoY3GZk4qwKZi+pZfbSWqqnlBJeAmOMGccsMEwPVWXv\nzg62PtfI1mcb2V3fCkBFbYLZS+qYtbSWyXMqcaPFvkuMMaYYLDDMgDpa0tQ/18jW5xrZ8eI+vJyP\nExFqp5UzaXYFk2ZVMGl2BZV1SWuBGDMOWGCYgmTTHttf3Mubr7Wwq76V3dtayWWCW6snSqNMDMOj\nO0is264xRx7rJWUKEo27zFlWx5xlwV1+fc9n7xsd7NraGgz1rcEdc8PfFOU1CSrrklTUJCivTVJZ\nm6S8NkFFTZJkedRaJMYc4SwwTA/HdaidVk7ttHIWvj14/EgmlWP3tjZ2bW2haUc7rU0ptj7XSFdb\nts+2kbhLRU0iGGqTlNckgqE6GCdKLVCMGessMMygYokI046rYtpxVX2WZ1I52ppStDalaG3soq0x\nRWtTF62NKRpeaSab9vqsH4m7lFfFKa8Jw6Q6HoyrEpRUximpjBGN2TUjxoxmFhjAo682smRaJeUJ\nO0ZfqFgiQs3Usn6vKldV0p1BoLQ1pWjbmzfem2JXfQvpjlw/+3SD8KiIUVIZC8YVMUrDZfGSKLGk\nSywZIZaMEIk61mox5jAa94GxryPDj79/HemqmXzrE+cwd3Jlsas05okIidIoidIodTPK+10nk8rR\ntjdF+740nS0ZOlu7x8Gw5/U2OlszZFNev9sDiCPEEmGAJCK9YZKIkCiJEC+NEi+JhEOURGkw7p6P\nxCxwjDkQ4z4wypwMK998DG/XX7jjf91G1bxlnHzG6cxadgLJ8opiV++IFUtEqJlSRs2Uwe97lU17\nPWGS7sqR6cqRSXnBOH86FQydLRn2vdlJujNLujPXc8K+P05EgiDJD5TSKImSKPHSvsviJRHiyd7A\ncSN23YoZf8Z9YDjpFO/p6GR7Yzt7KkrYs/FJ7tq0ARFh8tz5zFm+kjnLV1I7fab9Gi2CaNylsq6E\nyrqSA95WfSWTypHuDIZUZ5Z0R64nTNKdWVKdOdIdwXx7c5qmhg5SndlBWzYAkZgTBEhpNAySCLEw\neOLJCNGE29vqSUTeMh1NuriuhY4ZW+w6DOD2V2/nhLYanF/eSvO9D9LqRmmqS7CnppK9fnBeo7y2\njjnHr2TG4qWUV9dSUjmBkspKovHEcL8NMwp4nk+mM0cqDJNUR5ZMV2/4pDuzpPeb7y7PdOUo5L+V\n4wqOI0g4dlxBnP6nIzE3DKII8aRLNBm0ePoEUTJCNO7iRpxg367kTTu4EcHpnnfEfgAZwC7cO6Bt\nWtItnP3bs8lpjk8d/yn+dua5/PmHN1Hyx5upbWwmFXNonZmgccp0GjodsplMn+2jiSSllRPCAJlA\n6YTu6Soc10V9H/V9fN8Lx/5blqkqIg6O6yKOg+P0TnfPi+PiuC6J0jJKJ1RROqGKksoJRGKx4fwI\nzTBQVXIZPzhM1n3YrHu6K5jOpnJk0z6+r6ivwdgLxj3LvN5xNt33UFw65ZFLD94KGoobdYhEHdyI\n0zsdzndPR6IubtQJzw31BlNva6n3vFE04RKJOjgRBzcMPAul0c8C4wDtbN/JN/7yDdY1rGNRzSKu\nOvkq2lrr+PoP/5sPvHInq1/fBKkcTpmHP68Sf+ZsspPnkK6cSmcuRkdrC12tzXQ0N9PZ0kxXexsF\n/cQcBvHSUkorwwAJg6R0QhWJsnIc1w3D5q0B5Dhuz/JYsoRkeTnJ8gqiCbslyIHyPY90VyeZzg7S\nnZ2I4xCJRnGjUSLRGG40ihuN4UYiBX22qorv5fA9r2eIRGNE4vE+2/uen3cOJxhn0x6+5+PlFN/z\n8T3FywXj3umg3Mv5eFmfXPc444fLPHLZYJmX88mmPbJpj0yXh5fzC/9ghCCMXOkJke4WjhsRIlGX\nSMztE1bB2O07n9dK6t62Z9p1cCK9LTL1FdXgcGQwhJ+nr6j2zgev7RBLBHWIxnuHSHzow4WqSqar\ni47mfXS27KOrrRU3GiWeLCVeGgyJkrK3/M2Gm6ri5XL4uSyx5IEftoVRFBgi8i7ge4AL3KCq1+xX\n/hHgSkCANuAfVPXZsKw+XOYBuULe0KHcGkRVuXvr3Xz7qW/Tkm5h7cK1fHDOWv7llhd4fuseri7f\nxkkb/puul3b0bOPGfOJVPolp1cSPO5bEshOJnXgW1Myhs60V3/MGaC2ELYbuaZGe1kdPq8P38b3e\nVkkw7ZFqb6ejeV+fobN5Hx0t3fPNZFNdB/UZADhuhGRFBcmyIEASYZB0h4l6Hp6Xw8/l8Dwv+GLL\n5fByXs+XnJfLhuPgH7KX88J1cmFZuH4ui+d5PeHa8y+x+9+kKtrPWWtBoPs/oQg9/x2l9xet5H22\njuMg0jvfUxYGpxNxcd1IELCRYLz/PECmq5N0ZxgMXV3huJNcOl3w5+tGo7iRKJFYDMdx8Dwv/Ex7\nPz/1B/hSFiGWSBBLJIkmksSSSaL7zUdiMXo+kbwvqr7fWRL+rd3w36YbfAGHrdjeZW743pVsKk0u\nkybd2dUzZLpSZFIpsqkU2XRQ7udy+L6H7+fQ8N+s7/uoBtPqez1/aceN47gJxIkjTjCGGBBHiaN+\nFCVsQatP8FXggfooHqgHhMu1e78SDOL0ThNOS/c0SPcynPDjyF9fENfBjbi4roJ2on4n6nfgex14\nuQ68bDvqv7Vr+Fv/ZA5uNIEbTeJGkrjRJE4kGv57DHoUihC2xuhplUlYXfVzeF42/H+Wwctm8HJZ\nctksXjZNLpsFVUqrqvnEdT8fsj7913EUBIaIuMArwDuBHcBTwIWquilvnZOBF1V1n4icDVylqieG\nZfXAClVtLPQ1h+NeUs2pZr779Hf5w2t/YEb5DL6w6svc/VQZNz/xOm87ppbvvfcYktu3kNrwGOnn\nniK1+TXSO1vQXPg5ihKfoMSnVpGYP4/kKWeSeNv7cMr67146EjKpLlLtbX0OgeWHT995j3RnJ6m2\nVrraWulqb6OrtZVUezjf1kZXW2vP/ro5bqT3izYSwc37cnXcSPDF6Lo4kShuJIIbCdeLRILynmkX\n8r7ger/Ywi9+Ecj/stMg3LtneieV7i+i4FemH/6i9Hvfd/e85i3L/7IOQ8/zgoDzvd55gHiyhFhJ\nCfGSUmLJEuIlJT3jnmXJEhTFy2TC/9RZctlMOM7i5bJ4meA/vZfzcCPB5xX8Yo6EQ++XtRuJII5D\nLpMhm06R6eoi09VFNtVFJtVFNhUsy6aD5bnwkGnf/9e90/mfV/ff3/cKP7Ql4hBNxInGE0TjCSLx\nONFwiMTiuNFo8Pfd/99DxO35uztuBFWfdGcn6Y4O0p3t4bizz7TqIK0ZkZ59OW4Exwk+p56/b/g3\nfss4/HdwQMQhEi3FjZbiREpx3FLELUWkFJESlCTqJ1A81E+jfho0hWommNcU+Gl8P1iGnwlaQQXV\nw0UkAkRAgkFwg2kiiERwIlESpWV8/PufPLD31f32RklgnEQQAGeF818AUNX/M8D6VcBGVZ0aztdT\nhMDo9sQbT/C1x7/G9rbtfOCYD3C0+yG+9cfXqSqNcunJs7hgxXRqy4IHEqnnkdn6Gukn/4fUM0+S\n3ryZ1Pa95NrzQmRSCcl5R5Nc9XYSJ59J/JhjkMjY6aSmvk8um8GNRHt+qZsjg2oQsL4XBGh3iHQP\nQE84FHpYbTjqlE11ke7sDA9tRfv+6HAO7a4AfX9E9J5LzD/HqL6PE4mQLCtHnJHp0aZ+eGgw5/c5\nDNhnPts7nct6PfO5vLJI1OHEc+YcVB1GS2CcB7xLVS8L5y8GTlTVfmNQRD4HzMtbfyvQQtAO/b+q\nev1Qrzncd6tN5VJc9+x13PTCTVTGK7nw6E/x5w1TeGLLPmKuw9mLj+Li1TM5YWZVv/+JcvUv0fXg\nb+l66lFSr9TT9aaHnw2bxFGHxNFTSR6/isSKk0jMnUts1iwkalebG2MOnzEXGCJyOvBj4G2q2hQu\nm6qqDSIyEbgf+JSqPtzPtpcDlwPMmDHjhG3btg37e3l578tc9dhVbGzayEmTT+L0yR/kxS1T+P2G\nN2hL55h3VDkXnzSTDyybSml84FaD7q0n+/gf6PrL/9C16WVSb+ZI7Yuifhg2rkt89kzix80nPncu\n8WOPJT73WKJTpozYLxxjzPg2WgKjoENSIrIE+D1wtqq+MsC+rgLaVfW7g73mSD4Pw/M9fvnSL7nh\n+RvYm9rLxOREzp79XpLp1dy5PsemN1opi0f44PKpXLR6JnMnDXHOQhX2vIRu/jPpJ+8jvelZ0o1Z\n0i1R0m1Jsm29fxenpITYsccQP/ZYEvPmk1iwgMS843BKDq5XhDHGdBstgREhOOm9BmggOOn9YVV9\nIW+dGcCfgEtU9bG85aWAo6pt4fT9wNWqes9gr3k4HqCU9bI8vONhfvfq73ik4RF89Tlh0gksrzqT\n17bO4e7n95HxfFbNruZjp8zmzAWTcJwCjvl6OXjzWdjyEGx9CO/Vv5De65FuiZHOTSbdUUb6zU68\n1vZgfRFic+aQmB8GyIIFJBbMx62w25kYYwo3KgIjrMi7gf8k6FZ7o6p+U0Q+AaCq14nIDcDfAN3H\nkXKqukJE5hC0OiC4fckvVfWbQ73e4X7i3u7O3dzx2h3c/urtbGvdRmm0lNOnnUmiazX3Pxtnx94U\nR9eV8olTj+b9y6YSO5D7D+XSsP1J2PowbH0IGtajXo5cJkFK5pFKH0WqCVJbd5Hbtatns+j06T0t\nkNjsOcTmzCY2cyZOPD4Cn4AxZqwbNYFxuBXrEa2qyobdG/j95t9z37b76Mp1MadyDjMTJ7Hx1cls\n2VHFlMpSLnv7HD60ajolsYPoHZVug22PQ/06qH8E3ngm6JvuxshNOJ6UHEuqrYLUznZSL79C9vXX\ne7cVITp1KrE5s4nPnk1s9mxis2YTmzObSF2d9XgyZhyzwCii9kw799bfyx9e+wPP7nkWX32SbhmR\nzFz27J5FqbeAj564nEtPnsmEkkO4rUeqFV7/S78BwtQV+JNWkmYqmY5SMm/uI1O/lfTWejJbt6Kp\nVM9unNJSojNmEJ06heiUKcSmTiWaN9ghLmOObBYYo0RLuoXH33icxxoe49Gdj7K7czcAXnoi0nUc\np01/G1ecdjazqquG2FMBUq2w/YneANn5THj1K1A+GaYsh6nHo0ctI+dOJf3mPjJb68ls2UK2oYHs\nzgYyOxrQrr5XiTvl5T3hEZs2ldjsOcSPnkNszhzc6mprnRgzxllgjEKqyqvNr/LYzse4b+tDbGx6\nBp8s6keocY9jXvUCTpq2hFNnLWNm5QwcOcRutJlOePN52LkBdv4VGjZA0+be8qpZYYgsh9q5UDkd\nrZyG1+WR3dEQhEj+sLOBzPYdfVonbmUlsTlziB09h/jscHz00UE3YNcet2rMWGCBMQZ05bq4e/Oj\n/PyZe3mt7Tk0tguRoEXgaIK6+GzmV8/n5OlLWH7UIuZMmEPUOcSL+lItQctj54YgQHb+FVq2910n\nWQ0TpsOEGVA5IxiH81oxndy+DtJbtpDZsoX0a+F461a8pqaeXUgsRuSoo4jU1QVDbW3vdF3vtFtV\nZdeXGFNkFhhjjO8rL+9u5v7Nz/JEw3O81vIyrf42nMROxMkC4BBlUmIWC2oWcOLUxSyuW8ixVccS\ndw+x91NHI+zdCi2vQ3P3sD0Yt2yHbGff9csnBy2SunlQNxdqj4O6eeQyETL19WS2vEZ6y1Zyb75J\nrrGR3J495PbswW9vf+truy6RurrwkNcUYtOmhdPTiE6bSnTSJLvy3ZgRZoFxBGhLZXlm+14efG0T\nT73xPPVtm8lGtuMmdiJucJ5BcKmLz2B+9XxWTV3CkrqFHFd9HMlIcngqoQqdTb1Bsm8r7HkF9rwE\nja9AJi8EklVheIRDzTFQfTRUzQQ3it/V1Rsgu4MQyTU2knvzTbINDWR2NpB7cxfk36nVcYgedVTv\nCfjqatyKcpzyctyKimBc3jvvlpcjJSV2XsWYA2CBcQRSVbY2dvDcjmae2P4qz+5+ge0dm8lFtuMk\nGnAiHeGaQk1sOsdOmMtxNXOYXzOHWRNmMbN8JmWxwZ+ffYAVgtYG2PNyEB57XuoNk669veuJGxzS\nqj4aqudATTiuPjo43BXp7Smm2SzZXbvI7tjR5/xJJjyn4jU39zmH0i/XDcKjqgq3qopIdRXuhGDa\nra4iUlUVBE/3ssoKnJISO+dixi0LjHHC95Xt+zp5bkczT22v56+7nqe+/RVy7nacxBs40ZY+68el\nkqroFKaUTmf2hFksrDuaxROPZnLZUVTEKobvl3lHIzS9Bnu3wN5w3D2fbu1dT9zgEFeiAuIVEC8P\np8vD+Yq+8xVT0LKpeF4Uv7UVr60Nr7UVv8+4Da+lGa+5BW/vXrx9+8jt24e3bx8Mchtvp6QEp6ws\nGMrLcEvLeufLSnHLwpZMeRlOWXlPS8cpK+tp5djFkWYsssAYx1SVHfu6eGVXG/V7m3m5qZ5tLdt4\ns2s7zdmdZJzdOLFGnMh+5xTUISYVlLgVVMSqqE5UM7G0hinltUyvmEhtSQ1ViSoqYhWUx8opj5WT\ncBMHFjLdh7jyA6S1ITgZn24LwiTdFnQRTrdBboAHQSUqoWp20NOrOhxXzQ6mK6ZCP7e+VtUgUPbu\nDQOkGW/fXry2Nvz2jqCsoz2Ybm8Pho52vLbu6Y4hn6IosVgQKmVlQYCUl+GWVwTj/QInKCvHKS3t\nO1hrxxxmFhhmQF0Zj4bmLjbv2cMLe7bwWnM9uzv2sDe1l7bsPrq8VtRpRyIdiNuOuAM/TS4iUUqj\nZVTGK6iMV/QJkwnxCVQnqqlKVFGVCAKoKh6Mo26BJ7JzmeA8SaolGFobghP0+7bCvvpguvl18LO9\n2zhRqJgM8cr9Wiv9tF7i5RArgUgiGKLJt46dCHQ/EbEjDJa2dvy21jBs2oNxaxt+e3dZW7CsrQ2v\nvQ2/LVhHOzsHfKv5JJkMA6QEp7QUt6S0z7xTkjcdhkyf6f0Gsee+m0FYYJiDpqo0d2Z5oyXFm61d\nbN/Xyrbm3Wxv3cObbY3s6WyhOdWCL13gdCFuCnG6iMXSxGJpHDeFL51ktB2l/yemlUXLesMkXkVF\nGDYVsYpBp/vtEeZ70LIjCJB9W4MQad0Ztli6Wy15LZf8cCmEOBBJQrwMSmqhtBbKJkJpXTBdWpc3\n1AbruNHgqns/eJxo96DZbBgqbUHgdKTws4qfzuF3dAaBNNDQGZR7nR1oRyd+geEDQDSKk0z2DZJk\nEkkmcOIJJJHAScSReO9YEvGwLI6TSCCxGBKLh+MoTiyGxLvn84ZoUC7hkwLN6HcggTF2HvlmDgsR\noao0RlVpjAVTKoBJwLF91vF8ZXdbih37umjY18WOfZ3s2NcVDE2dNDR3kfU8cLtw3A7E7UAi7eG4\ng4zbQXOkg0i0EyfSCE4nKl2oM/gJbcElKglibpKkm6AkWkJptISKeCkV8VJKoiUkK5IkqxeQjCRJ\nRpIk3ASJSDAkI0kSOCR9n4SXI+FlKVUoU4j5Och2BTd9zHVBNpU3TgWB09EIHXtgx9PBdKbtwD5b\ngrtwvuWAkxPpbfkkKmBS/vmbCohPDgIrVg6xUoiVopESfD8SDFnBzxKET8pDU11BwHR24nfmTXd1\n9kxrRyfevmZyqS78VBpNpfDTwVjDx7weMtdFotG+QyQSjMPAceLxIHgSCZx4LAireCwIq3gciccQ\nNwKugzhuOHbAcRE3GOMI4rhBkHW3tvoZj6UnXI5W9gmaA+Y6wuTKJJMrk6yc9dZyVaUj49GWytKW\nytGWytKayvVM54/b0zlynpLzfTK5HCm/nbTXQdpvJ63tZLWDrHaS1XZSXhcduS46nDT7nCzipEFa\nEKcRcdI4bhZxMiAZVHIH9J4cosScJFEpIe6UEHNKiDlJ4k4pMaeEZKSGiuoZTJhaQk2yjJrScmpi\nUZJehkS2i2Smk2S6g0SqjRgQdSLBIDEiTvhIUXFAJPiSEwf8XN9zNunW3unWHbC7tXeZ9j1hP2D4\niBMES7w8CJl4OdR1z4ehFKsNytx4+IUbyRtHUATNKX5W0ayPeorvC+oJ2j32FN9TNAfq+WhO0ZwX\nDNls3yHXO0049jMZNJ1B02m89ja0qSkIrUwaTaXRdBo/nYbcgf0dByPxeO9hukgkCLTu0OkTSL1j\nibhhq2m/IR4LWln5LauIGwRZ99h1wI30vEaw3AnOUbluMHYcpHudnmVu77zTd4w4vftznWB7x4FI\nBLdsGHtBDsACwww7EaEsHqEsHmFy5fDu2/OV5s4MTR0ZGtvTNLVnaGpP09ieoakjGLd0Zsn4WbJe\nhoyfIuunyGoGT9Nk/TQ5TeOTIadpVNLgBK2blJNCnDTipMBt7wkicVPgpBHp/xBbIQQHhwiORHAl\ngkMEV6JEJE7USRB1kkQlTjRWQywxlZiT6DPExSGBT0J8EuRI4pHAI+7nSJAl7mdIaJa4lybpZSjx\nUiS8FLFMB7HUPqItO4im24lm2omk2xAGPhQt4XBQB5TECXq/SfhF3D2dcCCZXxYJDt25sXDoni4B\nd0LPMiWKigMqKAIa1E5V+iwLQg78nAatrYziZ/y8wcNP5fAzOfxUDjTsw6ASjP3ueUX9HHiKZhXt\n9NFsrjfkslk0k+0dD1dr7BC5tbXMfWTdiL+OBYYZU1xHqCmLU1MWH/qphgfB9xVfFV/BV0XDcVfW\nY3dbJ2+0tPBGext7wqGps529XR00d3XQku6kPd0Z3CMMDxEPFQ8kF0zjIZID8cIhB04GkQzi7Aum\nnWwYThlwsogMwznGWDiUR4AJwARE92+bKNoTIhokRn/CVSRvBckbR9Qh6jtEcYhp9yDEcYirQxwJ\nBg2+fCL4RFEifpqInyKS8Yji46qPi0/U93rq1l3D7il/v/mcKlmUHD5ZgWwCskkhK+EAPdNxVZK+\nT4kqpeG4xFdKwunusoQqouCgOOF7dMN5ARw/GKR70GDsqATT6gKCoy6iQQw7gOMLqIOLgA+iThDU\n6gRlCI5K8HmLA7hhMLoI3UEZ1EJVIDlMF+sOwQLDmDyOIzj9fFuWxiPUlsVZMHkY7iycZ/+Ayp/2\nPJ+Ul6Y900k6l6Erm6ErmyXlZUllM6RzOdK5LGkvS6p7nM2Q6l43lyGVS5POZcLyTNjqypLxwlvO\niARHyURwxMEREHFwkHBZUC4EP+7zWyY9YRaOVRVPM2Q1Q9ZPk9EUnZrG0wweaXzN4Esan3R4yDD4\naa/4QwRjd5unfxq2OlABdVGNgLrBl7U64dgNv4wdBAmC282gTrZn8J3hO/w1/LoPSfZfx3Kvg8f6\nLRleFhjGFNFAAdUrDoyPZ5JkcjlSuSxdGY+ubC4IvGyOrAeep+R8wfMh6yk5j3BQsr6S9Xx8hWTU\nDYaYQ6Jn2u1Znoi5xCMOWU/pTOfozHjhkKM9naUl1Ulzqo3WdAf/f3v3GyNXVcZx/Pvb3bZAV61/\nVkIAQZQXVoJVGkwETROjAd4UDYJFSeVNNSkG3qEGA5KYGOO/mBAKxiYlVhGFYmNIFAip8gLpbrMK\nFNSGlKRNbUtQcEGxzjy+OGfbceluzs7s7N175/dJmr175t7Z8+Tp7LP33JnnvvL6q7x27F+peEeL\nY+027XabVrtFK9q02u3jX9tx4nwnnaFNbwPT7xZUO4+08x8GrfT5INpETD9HO505xXQhhWGJ4aFI\n/1ckhofEkGBoKD02JBhdftqi5MgFw8yWhOUjIywfGeHNp/T/Z60YgdEV/vU3X36jtJmZFXHBMDOz\nIn0tGJIuk/RnSfskfeUkj0vSD/Pjf5L0odJjzcxscfWtYEgaBu4ALgdWAxskrZ6x2+WkjxGfD2wC\n7pzHsWZmtoj6eYZxMbAvIp6PiP8A9wLrZ+yzHrgnkieAVZLOKDzWzMwWUT8LxplA5w2jD+Sxkn1K\njgVA0iZJ45LGjx492vOkzczs5Gp/0Tsi7o6ItRGxdmxsrOrpmJk1Vj/fiHwQOLvj+7PyWMk+ywqO\nNTOzRdTPgrEbOF/Su0m/7D8LXDtjn53ADZLuBT4MvBwRhyQdLTj2DSYmJl6U9EKX830H8GKXxy5F\nTVD1cwUAAAUPSURBVIsHmhdT0+KB5sXUtHjgjTGdU3pg3wpGRPxX0g3Ab0hdmLdGxDOSvpQf3wI8\nBFwB7ANeA66f69iCn9n1mpSk8dKbiNRB0+KB5sXUtHigeTE1LR7oLaa+fjY+Ih4iFYXOsS0d2wFs\nLj3WzMyqU/uL3mZmtjhcME64u+oJLLCmxQPNi6lp8UDzYmpaPNBDTEqrQmZmZnPzGYaZmRVxwTAz\nsyIDXzCa2BVX0n5JT0malDRe9XzmS9JWSUckPd0x9jZJD0v6a/66sPdK7bNZYrpN0sGcp0lJV1Q5\nx/mQdLakxyTtlfSMpBvzeG3zNEdMtcyTpFMkPSnpjzmeb+TxrnM00NcwclfcvwCfIPWr2g1siIi9\nlU6sR5L2A2sjopYfOJL0MWCK1Jjygjz2beCliPhWLuxvjYibq5znfMwS023AVER8p8q5dSM3CT0j\nIvZIehMwAVwJfIGa5mmOmK6mhnmSJGBlRExJWgY8DtwIfJouczToZxjuirsERcTvgJdmDK8HtuXt\nbaQXcm3MElNtRcShiNiTt/8JPEtqEFrbPM0RUy3lLuBT+dtl+V/QQ44GvWAUd8WtmQAekTQhaVPV\nk1kgp0fEobz9N+D0KiezgL6cbx62tU7LN50knQt8EPgDDcnTjJigpnmSNCxpEjgCPBwRPeVo0AtG\nU10aEWtIN6DanJdDGiN3CGjCWuqdwHnAGuAQ8N1qpzN/kkaB+4GbIuKVzsfqmqeTxFTbPEVEK/8u\nOAu4WNIFMx6fV44GvWCUdNStnYg4mL8eAXaQlt7q7nBeY55eaz5S8Xx6FhGH8wu6DfyImuUpr4vf\nD2yPiAfycK3zdLKY6p4ngIj4B/AYcBk95GjQC8bxjrqSlpO64u6seE49kbQyX7BD0krgk8DTcx9V\nCzuBjXl7I/CrCueyIKZftNmnqFGe8gXVHwPPRsT3Oh6qbZ5mi6mueZI0JmlV3j6V9Oae5+ghRwP9\nLimA/Ba5H3CiK+43K55STySdRzqrgNRc8qd1i0nSz4B1pDbMh4FbgQeB+4B3AS8AV0dEbS4izxLT\nOtIyRwD7gS92rC0vaZIuBX4PPAW08/DXSGv+tczTHDFtoIZ5knQh6aL2MOnk4L6IuF3S2+kyRwNf\nMMzMrMygL0mZmVkhFwwzMyvigmFmZkVcMMzMrIgLhpmZFXHBMFsCJK2T9Ouq52E2FxcMMzMr4oJh\nNg+SPp/vMTAp6a7c3G1K0vfzPQcelTSW910j6YnctG7HdNM6Se+V9Ei+T8EeSe/JTz8q6ZeSnpO0\nPX/y2GzJcMEwKyTpfcA1wCW5oVsL+BywEhiPiPcDu0if4ga4B7g5Ii4kfXp4enw7cEdEfAD4CKmh\nHaTuqDcBq0nN7i7pe1Bm8zBS9QTMauTjwEXA7vzH/6mkxm1t4Od5n58AD0h6C7AqInbl8W3AL3Kf\nrzMjYgdARPwbID/fkxFxIH8/CZxLuumN2ZLggmFWTsC2iPjq/w1KX5+xX7f9dl7v2G7h16ctMV6S\nMiv3KHCVpHfC8Xsjn0N6HV2V97kWeDwiXgb+Lumjefw6YFe+k9sBSVfm51gh6bRFjcKsS/4LxqxQ\nROyVdAvwW0lDwDFgM/Aq6eY0t5CWqK7Jh2wEtuSC8DxwfR6/DrhL0u35OT6ziGGYdc3das16JGkq\nIkarnodZv3lJyszMivgMw8zMivgMw8zMirhgmJlZERcMMzMr4oJhZmZFXDDMzKzI/wAva/6k0KCC\nrAAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7f00280e4390>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"for k, v in res.items():\n", | |
" plt.plot(x, v.history['val_loss'], label=k)\n", | |
"\n", | |
"plt.xlabel('epoch')\n", | |
"plt.ylabel('val loss')\n", | |
"plt.title('hidden 512-512 loss')\n", | |
"plt.legend()" | |
] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python 3", | |
"language": "python", | |
"name": "python3" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 3 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython3", | |
"version": "3.6.1" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 2 | |
} |
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