GAN_CFAR_2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "GAN-CFAR.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/puraminy/mini_proj3/blob/master/GAN_CFAR_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "n_0j_uVUcqiG",
        "colab_type": "code",
        "outputId": "99840795-21f6-4267-90e9-b61cde3af5e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2870
        }
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import tensorflow.contrib.layers as lays\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import os\n",
        "import tarfile\n",
        "import urllib\n",
        "import pickle\n",
        "import _pickle as cPickle\n",
        "\n",
        "lr = 0.0002\n",
        "num_epochs = 600\n",
        "batch_size = 256\n",
        "data_path = 'data/cifar-10-batches-py/{}'\n",
        "\n",
        "# Download weights\n",
        "if not os.path.isdir('data'):\n",
        "    os.makedirs('data')\n",
        "if not os.path.isfile('data/cifar-10-batches-py/data_batch_1'):\n",
        "    print('Extracting the data ...')\n",
        " #   urllib.urlretrieve(\"https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\", \"data/cifar-10-python.tar.gz\")\n",
        "    with tarfile.open('data/cifar-10-python.tar.gz', \"r:gz\") as tar:\n",
        "        tar.extractall('data/')\n",
        "    os.remove('data/cifar-10-python.tar.gz')\n",
        "    print('Download is complete !')\n",
        "\n",
        "# unpickle data files\n",
        "def unpickle(file):\n",
        "    with open(file, 'rb') as fo:\n",
        "        dict = cPickle.load(fo, encoding='latin1')\n",
        "    return dict\n",
        "\n",
        "# Load all the data files and stack them together\n",
        "dataset_x = unpickle(data_path.format('data_batch_1'))['data']\n",
        "for i in range(2,6):\n",
        "    dataset_x = np.vstack((dataset_x, unpickle(data_path.format('data_batch_{}'.format(i)))['data']))\n",
        "# calculate the number of batches per epoch\n",
        "batch_per_ep = dataset_x.shape[0]//batch_size\n",
        "\n",
        "\n",
        "def next_batch(batch_number):\n",
        "    # a function to load a bacth of images\n",
        "    batch_x = dataset_x[(batch_number) * batch_size:min([((batch_number) + 1) * batch_size, dataset_x.shape[0]]), :]\n",
        "\n",
        "    # reshape the sample to a batch of images\n",
        "    batch_img = batch_x.reshape((-1, 3, 32, 32))/255.0\n",
        "    batch_img = batch_img.transpose([0, 2, 3, 1])\n",
        "    return batch_img\n",
        "\n",
        "\n",
        "def leaky_relu(x, alpha=0.1):\n",
        "    # Leaky Relu activation function\n",
        "    m_x = tf.nn.relu(-x)\n",
        "    x = tf.nn.relu(x)\n",
        "    x -= alpha * m_x\n",
        "    return x\n",
        "\n",
        "# Define the Generator, a simple CNN with 1 fully connected and 4 convolution layers\n",
        "def generator(inputs, reuse=False):\n",
        "    with tf.variable_scope('generator'):\n",
        "        if reuse:\n",
        "            tf.get_variable_scope().reuse_variables()\n",
        "        net = lays.fully_connected(inputs, 4*4*256, scope='fc1')\n",
        "        net = tf.reshape(net, (batch_size, 4, 4, 256))\n",
        "        net = lays.conv2d_transpose(net, 128, 3, stride=2, scope='conv1', padding='SAME', activation_fn=leaky_relu)\n",
        "        net = lays.conv2d_transpose(net, 64, 3, stride=2, scope='conv2', padding='SAME', activation_fn=leaky_relu)\n",
        "        net = lays.conv2d_transpose(net, 64, 3, stride=2, scope='conv3', padding='SAME', activation_fn=leaky_relu)\n",
        "        net = lays.conv2d(net, 3, 3, scope='conv4', padding='SAME', activation_fn=tf.nn.tanh)\n",
        "        return net\n",
        "\n",
        "\n",
        "# Define the Discriminator, a simple CNN with 3 convolution and 2 fully connected layers\n",
        "def discriminator(inputs, reuse=False):\n",
        "    with tf.variable_scope('discriminator'):\n",
        "        if reuse:\n",
        "            tf.get_variable_scope().reuse_variables()\n",
        "        net = lays.conv2d_transpose(inputs, 64, 3, stride=1, scope='conv1', padding='SAME', activation_fn=leaky_relu)\n",
        "        net = lays.max_pool2d(net, 2, 2, 'SAME', scope='max1')\n",
        "        net = lays.conv2d_transpose(net, 128, 3, stride=1, scope='conv2', padding='SAME', activation_fn=leaky_relu)\n",
        "        net = lays.max_pool2d(net, 2, 2, 'SAME', scope='max2')\n",
        "        net = lays.conv2d_transpose(net, 256, 3, stride=1, scope='conv3', padding='SAME', activation_fn=leaky_relu)\n",
        "        net = lays.max_pool2d(net, 2, 2, 'SAME', scope='max3')\n",
        "        net = tf.reshape(net, (batch_size, 4 * 4 * 256))\n",
        "        net = lays.fully_connected(net, 128, scope='fc1', activation_fn=leaky_relu)\n",
        "        net = lays.dropout(net, 0.5)\n",
        "        net = lays.fully_connected(net, 1, scope='fc2', activation_fn=None)\n",
        "        return net\n",
        "\n",
        "\n",
        "images = tf.placeholder(tf.float32, (batch_size, 32, 32, 3))    # input images\n",
        "z_in = tf.placeholder(tf.float32, (batch_size, 100))            # input noises\n",
        "\n",
        "# Train the discriminator, it tries to discriminate between real and fake (generated) samples\n",
        "outputs_real = discriminator(images)\n",
        "loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(outputs_real), logits=outputs_real))\n",
        "\n",
        "images_fake = generator(z_in)\n",
        "outputs_fake = discriminator(images_fake, reuse=True)\n",
        "loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(outputs_fake), logits=outputs_fake))\n",
        "\n",
        "loss_d = loss_real + loss_fake  # Calculate the total loss\n",
        "discrim_tvars = [var for var in tf.trainable_variables() if var.name.startswith(\"discriminator\")]\n",
        "discrim_train = tf.train.AdamOptimizer(lr).minimize(loss_d, var_list=discrim_tvars)\n",
        "\n",
        "# Train the generator, it tries to fool the discriminator\n",
        "with tf.control_dependencies([discrim_train]):\n",
        "    outputs = discriminator(images_fake, reuse=True)\n",
        "    loss_g = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(outputs), logits=outputs))\n",
        "    gen_tvars = [var for var in tf.trainable_variables() if var.name.startswith(\"generator\")]\n",
        "    gen_train = tf.train.AdamOptimizer(lr).minimize(loss_g, var_list=gen_tvars)\n",
        "\n",
        "# Draw samples from the input distribution as a fixed test set\n",
        "# Can follow how the generator output evolves\n",
        "test_z = np.random.normal(size=(batch_size, 100))\n",
        "\n",
        "# initialize the network\n",
        "init = tf.global_variables_initializer()\n",
        "saver = tf.train.Saver()\n",
        "\n",
        "with tf.Session() as sess:\n",
        "    sess.run(init)\n",
        "    for ep in range(num_epochs):\n",
        "        for batch_n in range(batch_per_ep):  # batches loop\n",
        "            batch_img = next_batch(batch_n)\n",
        "            train_z = np.random.normal(size=(batch_size, 100))\n",
        "\n",
        "            _, gl, dl = sess.run([gen_train, loss_g, loss_d], feed_dict={images: batch_img, z_in: train_z})\n",
        "\n",
        "            if not batch_n%10:\n",
        "               # print('epoch: {} - loss_d: {} - loss_g: {}'.format(ep, dl, gl))\n",
        "               with open('results/dloss.txt', 'a+') as f:\n",
        "                   print(dl,file=f)\n",
        "               with open('results/gloss.txt', 'a+') as f:\n",
        "                   print(gl,file=f)\n",
        "        # Save the test results after each Epoch\n",
        "#        print('Testing ...')\n",
        "        images_test = sess.run([images_fake], feed_dict={z_in: test_z})[0]\n",
        "\n",
        "        if not os.path.isdir('results'):\n",
        "            os.makedirs('results')\n",
        "        for i in range(9):\n",
        "            plt.subplot(3, 3, i+1)\n",
        "            plt.imshow(images_test[i])\n",
        "            plt.savefig('results/cfar10-gan-e{}.png'.format(ep))\n",
        "        print('A new test image saved !')\n",
        "\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-af9f459c742f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m \u001b[0;31m# Train the discriminator, it tries to discriminate between real and fake (generated) samples\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m \u001b[0moutputs_real\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdiscriminator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\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    101\u001b[0m \u001b[0mloss_real\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduce_mean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msigmoid_cross_entropy_with_logits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs_real\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutputs_real\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[1;32m    102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-3-af9f459c742f>\u001b[0m in \u001b[0;36mdiscriminator\u001b[0;34m(inputs, reuse)\u001b[0m\n\u001b[1;32m     81\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mreuse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m             \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_variable_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreuse_variables\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---> 83\u001b[0;31m         \u001b[0mnet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlays\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv2d_transpose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstride\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscope\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'conv1'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'SAME'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mleaky_relu\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     84\u001b[0m         \u001b[0mnet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlays\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_pool2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'SAME'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscope\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'max1'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m         \u001b[0mnet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlays\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv2d_transpose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstride\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscope\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'conv2'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'SAME'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mleaky_relu\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/python3.6/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.py\u001b[0m in \u001b[0;36mfunc_with_args\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    180\u001b[0m       \u001b[0mcurrent_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcurrent_scope\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey_func\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\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[1;32m    181\u001b[0m       \u001b[0mcurrent_args\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\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[0mcurrent_args\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    183\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m   \u001b[0m_add_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\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/python3.6/dist-packages/tensorflow/contrib/layers/python/layers/layers.py\u001b[0m in \u001b[0;36mconvolution2d_transpose\u001b[0;34m(inputs, num_outputs, kernel_size, stride, padding, data_format, activation_fn, normalizer_fn, normalizer_params, weights_initializer, weights_regularizer, biases_initializer, biases_regularizer, reuse, variables_collections, outputs_collections, trainable, scope)\u001b[0m\n\u001b[1;32m   1405\u001b[0m         \u001b[0m_scope\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1406\u001b[0m         _reuse=reuse)\n\u001b[0;32m-> 1407\u001b[0;31m     \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\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   1408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1409\u001b[0m     \u001b[0;31m# Add variables to collections.\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/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, inputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    815\u001b[0m       \u001b[0mOutput\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\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[1;32m    816\u001b[0m     \"\"\"\n\u001b[0;32m--> 817\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\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[0mkwargs\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    818\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    819\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_set_learning_phase_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\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[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/layers/base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    372\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    373\u001b[0m       \u001b[0;31m# Actually call layer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 374\u001b[0;31m       \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLayer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\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[0mkwargs\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    375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    376\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecuting_eagerly\u001b[0m\u001b[0;34m(\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/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    744\u001b[0m           \u001b[0;31m# the user has manually overwritten the build method do we need to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    745\u001b[0m           \u001b[0;31m# build it.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 746\u001b[0;31m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_shapes\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    747\u001b[0m         \u001b[0;31m# We must set self.built since user defined build functions are not\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    748\u001b[0m         \u001b[0;31m# constrained to set self.built.\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/python3.6/dist-packages/tensorflow/python/keras/layers/convolutional.py\u001b[0m in \u001b[0;36mbuild\u001b[0;34m(self, input_shape)\u001b[0m\n\u001b[1;32m    752\u001b[0m         \u001b[0mconstraint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel_constraint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    753\u001b[0m         \u001b[0mtrainable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 754\u001b[0;31m         dtype=self.dtype)\n\u001b[0m\u001b[1;32m    755\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_bias\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    756\u001b[0m       self.bias = self.add_weight(\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/layers/base.py\u001b[0m in \u001b[0;36madd_weight\u001b[0;34m(self, name, shape, dtype, initializer, regularizer, trainable, constraint, use_resource, synchronization, aggregation, partitioner)\u001b[0m\n\u001b[1;32m    286\u001b[0m             \u001b[0msynchronization\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msynchronization\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    287\u001b[0m             \u001b[0maggregation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maggregation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 288\u001b[0;31m             getter=vs.get_variable)\n\u001b[0m\u001b[1;32m    289\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    290\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mregularizer\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/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36madd_weight\u001b[0;34m(self, name, shape, dtype, initializer, regularizer, trainable, constraint, partitioner, use_resource, synchronization, aggregation, **kwargs)\u001b[0m\n\u001b[1;32m    607\u001b[0m         \u001b[0mcollections\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcollections\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    608\u001b[0m         \u001b[0msynchronization\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msynchronization\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 609\u001b[0;31m         aggregation=aggregation)\n\u001b[0m\u001b[1;32m    610\u001b[0m     \u001b[0mbackend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrack_variable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvariable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    611\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/checkpointable/base.py\u001b[0m in \u001b[0;36m_add_variable_with_custom_getter\u001b[0;34m(self, name, shape, dtype, initializer, getter, overwrite, **kwargs_for_getter)\u001b[0m\n\u001b[1;32m    637\u001b[0m     new_variable = getter(\n\u001b[1;32m    638\u001b[0m         \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitializer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitializer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 639\u001b[0;31m         **kwargs_for_getter)\n\u001b[0m\u001b[1;32m    640\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    641\u001b[0m     \u001b[0;31m# If we set an initializer and the variable processed it, tracking will not\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/python3.6/dist-packages/tensorflow/python/ops/variable_scope.py\u001b[0m in \u001b[0;36mget_variable\u001b[0;34m(name, shape, dtype, initializer, regularizer, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter, constraint, synchronization, aggregation)\u001b[0m\n\u001b[1;32m   1485\u001b[0m       \u001b[0mconstraint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconstraint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1486\u001b[0m       \u001b[0msynchronization\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msynchronization\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1487\u001b[0;31m       aggregation=aggregation)\n\u001b[0m\u001b[1;32m   1488\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1489\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variable_scope.py\u001b[0m in \u001b[0;36mget_variable\u001b[0;34m(self, var_store, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter, constraint, synchronization, aggregation)\u001b[0m\n\u001b[1;32m   1235\u001b[0m           \u001b[0mconstraint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconstraint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1236\u001b[0m           \u001b[0msynchronization\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msynchronization\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1237\u001b[0;31m           aggregation=aggregation)\n\u001b[0m\u001b[1;32m   1238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1239\u001b[0m   def _get_partitioned_variable(self,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variable_scope.py\u001b[0m in \u001b[0;36mget_variable\u001b[0;34m(self, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter, constraint, synchronization, aggregation)\u001b[0m\n\u001b[1;32m    521\u001b[0m           function_utils.has_kwargs(custom_getter)):\n\u001b[1;32m    522\u001b[0m         \u001b[0mcustom_getter_kwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"constraint\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconstraint\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 523\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mcustom_getter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mcustom_getter_kwargs\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    524\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    525\u001b[0m       return _true_getter(\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/contrib/layers/python/layers/layers.py\u001b[0m in \u001b[0;36mlayer_variable_getter\u001b[0;34m(getter, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1747\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mlayer_variable_getter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgetter\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[0mkwargs\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[1;32m   1748\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'rename'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrename\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1749\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_model_variable_getter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgetter\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[0mkwargs\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   1750\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1751\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mlayer_variable_getter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/contrib/layers/python/layers/layers.py\u001b[0m in \u001b[0;36m_model_variable_getter\u001b[0;34m(getter, name, shape, dtype, initializer, regularizer, trainable, collections, caching_device, partitioner, rename, use_resource, synchronization, aggregation, **_)\u001b[0m\n\u001b[1;32m   1738\u001b[0m       \u001b[0muse_resource\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_resource\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1739\u001b[0m       \u001b[0msynchronization\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msynchronization\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1740\u001b[0;31m       aggregation=aggregation)\n\u001b[0m\u001b[1;32m   1741\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.py\u001b[0m in \u001b[0;36mfunc_with_args\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    180\u001b[0m       \u001b[0mcurrent_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcurrent_scope\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey_func\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\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[1;32m    181\u001b[0m       \u001b[0mcurrent_args\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\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[0mcurrent_args\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    183\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m   \u001b[0m_add_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\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/python3.6/dist-packages/tensorflow/contrib/framework/python/ops/variables.py\u001b[0m in \u001b[0;36mmodel_variable\u001b[0;34m(name, shape, dtype, initializer, regularizer, trainable, collections, caching_device, device, partitioner, custom_getter, use_resource, synchronization, aggregation)\u001b[0m\n\u001b[1;32m    348\u001b[0m       \u001b[0muse_resource\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_resource\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    349\u001b[0m       \u001b[0msynchronization\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msynchronization\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 350\u001b[0;31m       aggregation=aggregation)\n\u001b[0m\u001b[1;32m    351\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    352\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.py\u001b[0m in \u001b[0;36mfunc_with_args\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    180\u001b[0m       \u001b[0mcurrent_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcurrent_scope\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey_func\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\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[1;32m    181\u001b[0m       \u001b[0mcurrent_args\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\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[0mcurrent_args\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    183\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m   \u001b[0m_add_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\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/python3.6/dist-packages/tensorflow/contrib/framework/python/ops/variables.py\u001b[0m in \u001b[0;36mvariable\u001b[0;34m(name, shape, dtype, initializer, regularizer, trainable, collections, caching_device, device, partitioner, custom_getter, use_resource, synchronization, aggregation)\u001b[0m\n\u001b[1;32m    275\u001b[0m         \u001b[0muse_resource\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_resource\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    276\u001b[0m         \u001b[0msynchronization\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msynchronization\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 277\u001b[0;31m         aggregation=aggregation)\n\u001b[0m\u001b[1;32m    278\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variable_scope.py\u001b[0m in \u001b[0;36m_true_getter\u001b[0;34m(name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, constraint, synchronization, aggregation)\u001b[0m\n\u001b[1;32m    490\u001b[0m           \u001b[0mconstraint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconstraint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m           \u001b[0msynchronization\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msynchronization\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 492\u001b[0;31m           aggregation=aggregation)\n\u001b[0m\u001b[1;32m    493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    494\u001b[0m     \u001b[0;31m# Set trainable value based on synchronization value.\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/python3.6/dist-packages/tensorflow/python/ops/variable_scope.py\u001b[0m in \u001b[0;36m_get_single_variable\u001b[0;34m(self, name, shape, dtype, initializer, regularizer, partition_info, reuse, trainable, collections, caching_device, validate_shape, use_resource, constraint, synchronization, aggregation)\u001b[0m\n\u001b[1;32m    859\u001b[0m                          \u001b[0;34m\"reuse=tf.AUTO_REUSE in VarScope? \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    860\u001b[0m                          \"Originally defined at:\\n\\n%s\" % (\n\u001b[0;32m--> 861\u001b[0;31m                              name, \"\".join(traceback.format_list(tb))))\n\u001b[0m\u001b[1;32m    862\u001b[0m       \u001b[0mfound_var\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_vars\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    863\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_compatible_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfound_var\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\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;31mValueError\u001b[0m: Variable discriminator/conv1/weights already exists, disallowed. Did you mean to set reuse=True or reuse=tf.AUTO_REUSE in VarScope? Originally defined at:\n\n  File \"/usr/local/lib/python3.6/dist-packages/tensorflow/contrib/framework/python/ops/variables.py\", line 277, in variable\n    aggregation=aggregation)\n  File \"/usr/local/lib/python3.6/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.py\", line 182, in func_with_args\n    return func(*args, **current_args)\n  File \"/usr/local/lib/python3.6/dist-packages/tensorflow/contrib/framework/python/ops/variables.py\", line 350, in model_variable\n    aggregation=aggregation)\n"
          ]
        }
      ]
    }
  ]
}