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@myurasov
Created August 22, 2017 23:53
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Keras WACGAN
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{
"cells": [
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "import os\n\nimport matplotlib.pyplot as plt\n%matplotlib inline\n%config InlineBackend.figure_format = 'retina'\n\nimport numpy as np\nimport tensorflow as tf\n\nimport keras.backend as K\nfrom keras.datasets import mnist\nfrom keras.layers import *\nfrom keras.models import *\nfrom keras.optimizers import *\nfrom keras.initializers import *\nfrom keras.callbacks import *\nfrom keras.utils.generic_utils import Progbar",
"execution_count": 3,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "RND = 777\n\nRUN = 'B'\nOUT_DIR = 'out/' + RUN\nTENSORBOARD_DIR = '/tensorboard/wgans/' + RUN\n\n# GPU # \nGPU = \"0\"\n\n# latent vector size\nZ_SIZE = 100\n\n# number of iterations D is trained for per each G iteration\nD_ITERS = 5\n\nBATCH_SIZE = 100\nITERATIONS = 20000\n\nSAVE_SAMPLE_IMAGES = False",
"execution_count": 5,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "np.random.seed(RND)",
"execution_count": 3,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "if not os.path.isdir(OUT_DIR): os.makedirs(OUT_DIR)",
"execution_count": 4,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "# use specific GPU\nos.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = GPU",
"execution_count": 5,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "K.set_image_dim_ordering('tf')",
"execution_count": 6,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "# basically return mean(y_pred),\n# but with ability to inverse it for minimization (when y_true == -1)\ndef wasserstein(y_true, y_pred):\n return K.mean(y_true * y_pred)",
"execution_count": 7,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "def create_D():\n\n # weights are initlaized from normal distribution with below params\n weight_init = RandomNormal(mean=0., stddev=0.02)\n\n input_image = Input(shape=(28, 28, 1), name='input_image')\n\n x = Conv2D(\n 32, (3, 3),\n padding='same',\n name='conv_1',\n kernel_initializer=weight_init)(input_image)\n x = LeakyReLU()(x)\n x = MaxPool2D(pool_size=2)(x)\n x = Dropout(0.3)(x)\n\n x = Conv2D(\n 64, (3, 3),\n padding='same',\n name='conv_2',\n kernel_initializer=weight_init)(x)\n x = MaxPool2D(pool_size=1)(x)\n x = LeakyReLU()(x)\n x = Dropout(0.3)(x)\n\n x = Conv2D(\n 128, (3, 3),\n padding='same',\n name='conv_3',\n kernel_initializer=weight_init)(x)\n x = MaxPool2D(pool_size=2)(x)\n x = LeakyReLU()(x)\n x = Dropout(0.3)(x)\n\n x = Conv2D(\n 256, (3, 3),\n padding='same',\n name='coonv_4',\n kernel_initializer=weight_init)(x)\n x = MaxPool2D(pool_size=1)(x)\n x = LeakyReLU()(x)\n x = Dropout(0.3)(x)\n\n features = Flatten()(x)\n\n output_is_fake = Dense(\n 1, activation='linear', name='output_is_fake')(features)\n\n output_class = Dense(\n 10, activation='softmax', name='output_class')(features)\n\n return Model(\n inputs=[input_image], outputs=[output_is_fake, output_class], name='D')",
"execution_count": 8,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "def create_G(Z_SIZE=Z_SIZE):\n DICT_LEN = 10\n EMBEDDING_LEN = Z_SIZE\n\n # weights are initlaized from normal distribution with below params\n weight_init = RandomNormal(mean=0., stddev=0.02)\n\n # class#\n input_class = Input(shape=(1, ), dtype='int32', name='input_class')\n # encode class# to the same size as Z to use hadamard multiplication later on\n e = Embedding(\n DICT_LEN, EMBEDDING_LEN,\n embeddings_initializer='glorot_uniform')(input_class)\n embedded_class = Flatten(name='embedded_class')(e)\n\n # latent var\n input_z = Input(shape=(Z_SIZE, ), name='input_z')\n\n # hadamard product\n h = multiply([input_z, embedded_class], name='h')\n\n # cnn part\n x = Dense(1024)(h)\n x = LeakyReLU()(x)\n\n x = Dense(128 * 7 * 7)(x)\n x = LeakyReLU()(x)\n x = Reshape((7, 7, 128))(x)\n\n x = UpSampling2D(size=(2, 2))(x)\n x = Conv2D(256, (5, 5), padding='same', kernel_initializer=weight_init)(x)\n x = LeakyReLU()(x)\n\n x = UpSampling2D(size=(2, 2))(x)\n x = Conv2D(128, (5, 5), padding='same', kernel_initializer=weight_init)(x)\n x = LeakyReLU()(x)\n\n x = Conv2D(\n 1, (2, 2),\n padding='same',\n activation='tanh',\n name='output_generated_image',\n kernel_initializer=weight_init)(x)\n\n return Model(inputs=[input_z, input_class], outputs=x, name='G')",
"execution_count": 9,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "# with tf.device('/gpu:0'):\n\nD = create_D()\n\nD.compile(\n optimizer=RMSprop(lr=0.00005),\n loss=[wasserstein, 'sparse_categorical_crossentropy'])",
"execution_count": 10,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "input_z = Input(shape=(Z_SIZE, ), name='input_z_')\ninput_class = Input(shape=(1, ),name='input_class_', dtype='int32')",
"execution_count": 11,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "# with tf.device('/gpu:0'):\n\nG = create_G()\n\n# create combined D(G) model\n# D.trainable = False\noutput_is_fake, output_class = D(G(inputs=[input_z, input_class]))\nDG = Model(inputs=[input_z, input_class], outputs=[output_is_fake, output_class])\nDG.get_layer('D').trainable = False # freeze D in generator training faze\n\nDG.compile(\n optimizer=RMSprop(lr=0.00005),\n loss=[wasserstein, 'sparse_categorical_crossentropy']\n)",
"execution_count": 12,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "# load mnist data\n(X_train, y_train), (X_test, y_test) = mnist.load_data()\n\n# use all available 70k samples\nX_train = np.concatenate((X_train, X_test))\ny_train = np.concatenate((y_train, y_test))\n\n# convert to -1..1 range, reshape to (sample_i, 28, 28, 1)\nX_train = (X_train.astype(np.float32) - 127.5) / 127.5\nX_train = np.expand_dims(X_train, axis=3)",
"execution_count": 13,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "# save 10x10 sample of generated images\ndef generate_samples(n=0, save=True):\n \n zz = np.random.normal(0., 1., (100, Z_SIZE))\n generated_classes = np.array(list(range(0, 10)) * 10)\n generated_images = G.predict([zz, generated_classes.reshape(-1, 1)])\n\n rr = []\n for c in range(10):\n rr.append(\n np.concatenate(generated_images[c * 10:(1 + c) * 10]).reshape(\n 280, 28))\n img = np.hstack(rr)\n\n if save:\n plt.imsave(OUT_DIR + '/samples_%07d.png' % n, img, cmap=plt.cm.gray)\n \n return img",
"execution_count": 14,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "# write tensorboard summaries\n\nsw = tf.summary.FileWriter(TENSORBOARD_DIR)\n\n\ndef update_tb_summary(step, write_sample_images=True):\n\n s = tf.Summary()\n\n # losses as is\n for names, vals in zip((('D_true_is_fake', 'D_true_class'),\n ('D_fake_is_fake', 'D_fake_class'), ('DG_is_fake',\n 'DG_class')),\n (D_true_losses, D_fake_losses, DG_losses)):\n\n v = s.value.add()\n v.simple_value = vals[-1][1]\n v.tag = names[0]\n\n v = s.value.add()\n v.simple_value = vals[-1][2]\n v.tag = names[1]\n\n # D loss: -1*D_true_is_fake - D_fake_is_fake\n v = s.value.add()\n v.simple_value = -D_true_losses[-1][1] - D_fake_losses[-1][1]\n v.tag = 'D loss (-1*D_true_is_fake - D_fake_is_fake)'\n\n # generated image\n if write_sample_images:\n img = generate_samples(step, save=True)\n s.MergeFromString(tf.Session().run(\n tf.summary.image('samples_%07d' % step,\n img.reshape([1, *img.shape, 1]))))\n\n sw.add_summary(s, step)\n sw.flush()",
"execution_count": 15,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# try generating images\nplt.figure(figsize=(5,5), dpi=100)\nplt.imshow(generate_samples(save=False), cmap=plt.cm.gray)",
"execution_count": 16,
"outputs": [
{
"execution_count": 16,
"metadata": {},
"data": {
"text/plain": "<matplotlib.image.AxesImage at 0x7f5c1d47da90>"
},
"output_type": "execute_result"
},
{
"metadata": {
"image/png": {
"width": 435,
"height": 426
}
},
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x7f5c1fd19198>",
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YP84Gg8FgMBgMBoPB4AQwtMZzwre+9a16++23DyHYriZF1VaTwZCqIV4TW+Tj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\nRYc+L3wOxI+k2UkT93zkUa8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iJ8rX1QvStzsK8yoTmceR6xvf+MbhnHM141Kv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\nu/pxjksde39k7er62JZUCmVV36GeOC6zh2VeruiaaVfKjvO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Z6FB6j7Jk3F32wKottC3t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uf6TleTTN90znX1+qYkOJfbrDpfw7o6Zvpsrk+U9vzG0B+7ZFKu9173uawLrou+O7r1WFlj764+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\ntizih64fti99J7aVF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tGJjhk/c6xdZriJBpdjxzdRtKIb9aIs6cs51+nbOev60GW/dd1U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YpEA6tH1lXvpgbadLbCNSo0pZdVThqSajFKw8p710709loT7yrpRqONX+7Mbtc+lDe3Qd5Njr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\nXPxK6kBdHUv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uajLZrz4o6LI1ag/KuKMQaw9dHUB9QUep9Rnn6rp33XaI7JSrvj/y9Jzj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\nMBgMBoPBYDAYDE4AhtZ4TNhsNrXZbLahZ1NQDELfph+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KtrraZdAZL1y4cMs9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vdjeycHYXfCO2yuP0O0ymNNO63eaWInqmp3p/Ohhx6qqn6Hi92Vq1evbs9ZR+zgdB+4emed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+et9L3L55/56c2ouw89nDPpQxsxNk5iyEEEIIIYQQRkBmzpbEgQMHdODAgdkv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\n+GzbI3oc7eC27dVbj6ia5eqN3FUrx1d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},
"output_type": "display_data"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# fake = 1\n# real = -1\n\nprogress_bar = Progbar(target=ITERATIONS)\n\nDG_losses = []\nD_true_losses = []\nD_fake_losses = []\n\nfor it in range(ITERATIONS):\n\n if len(D_true_losses) > 0:\n progress_bar.update(\n it,\n values=[\n ('D_real_is_fake', np.mean(D_true_losses[-5:], axis=0)[1]),\n ('D_real_class', np.mean(D_true_losses[-5:], axis=0)[2]),\n ('D_fake_is_fake', np.mean(D_fake_losses[-5:], axis=0)[1]),\n ('D_fake_class', np.mean(D_fake_losses[-5:], axis=0)[2]),\n ('D(G)_is_fake', np.mean(DG_losses[-5:],axis=0)[1]),\n ('D(G)_class', np.mean(DG_losses[-5:],axis=0)[2])\n ]\n )\n \n else:\n progress_bar.update(it)\n\n # 1: train D on real+generated images\n\n if (it % 1000) < 25 or it % 500 == 0: # 25 times in 1000, every 500th\n d_iters = 100\n else:\n d_iters = D_ITERS\n\n for d_it in range(d_iters):\n\n # unfreeze D\n D.trainable = True\n for l in D.layers: l.trainable = True\n\n # clip D weights\n\n for l in D.layers:\n weights = l.get_weights()\n weights = [np.clip(w, -0.01, 0.01) for w in weights]\n l.set_weights(weights)\n\n # 1.1: maximize D output on reals === minimize -1*(D(real))\n\n # draw random samples from real images\n index = np.random.choice(len(X_train), BATCH_SIZE, replace=False)\n real_images = X_train[index]\n real_images_classes = y_train[index]\n\n D_loss = D.train_on_batch(real_images, [-np.ones(BATCH_SIZE), real_images_classes])\n D_true_losses.append(D_loss)\n\n # 1.2: minimize D output on fakes \n\n zz = np.random.normal(0., 1., (BATCH_SIZE, Z_SIZE))\n generated_classes = np.random.randint(0, 10, BATCH_SIZE)\n generated_images = G.predict([zz, generated_classes.reshape(-1, 1)])\n\n D_loss = D.train_on_batch(generated_images, [np.ones(BATCH_SIZE), generated_classes])\n D_fake_losses.append(D_loss)\n\n # 2: train D(G) (D is frozen)\n # minimize D output while supplying it with fakes, telling it that they are reals (-1)\n\n # freeze D\n D.trainable = False\n for l in D.layers: l.trainable = False\n\n zz = np.random.normal(0., 1., (BATCH_SIZE, Z_SIZE)) \n generated_classes = np.random.randint(0, 10, BATCH_SIZE)\n\n DG_loss = DG.train_on_batch(\n [zz, generated_classes.reshape((-1, 1))],\n [-np.ones(BATCH_SIZE), generated_classes])\n\n DG_losses.append(DG_loss)\n\n if it % 10 == 0:\n update_tb_summary(it, write_sample_images=(it % 250 == 0))",
"execution_count": 17,
"outputs": [
{
"text": " 32007/1000000 [..............................] - ETA: 497348s - D_real_is_fake: -1.6877 - D_real_class: 0.5539 - D_fake_is_fake: 1.3971 - D_fake_class: 0.3369 - D(G)_is_fake: -1.1529 - D(G)_class: 0.3303",
"name": "stdout",
"output_type": "stream"
},
{
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-17-06f79bce05a0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0mreal_images_classes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m \u001b[0mD_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mD\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_on_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreal_images\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBATCH_SIZE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal_images_classes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 56\u001b[0m \u001b[0mD_true_losses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mD_loss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[1;32m 1563\u001b[0m \u001b[0mins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1564\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1565\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1566\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1567\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 2266\u001b[0m updated = session.run(self.outputs + [self.updates_op],\n\u001b[1;32m 2267\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2268\u001b[0;31m **self.session_kwargs)\n\u001b[0m\u001b[1;32m 2269\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 787\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 788\u001b[0m result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 789\u001b[0;31m run_metadata_ptr)\n\u001b[0m\u001b[1;32m 790\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 791\u001b[0m \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 995\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 996\u001b[0m results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 997\u001b[0;31m feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m 998\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 999\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 1130\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1131\u001b[0m return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1132\u001b[0;31m target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m 1133\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1134\u001b[0m return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m 1137\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1138\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1139\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1140\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1141\u001b[0m \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m 1119\u001b[0m return tf_session.TF_Run(session, options,\n\u001b[1;32m 1120\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1121\u001b[0;31m status, run_metadata)\n\u001b[0m\u001b[1;32m 1122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1123\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
],
"evalue": "",
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