Face Completion.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "V3.3_dcgan.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [
        "Hbw-GRN-lbpO",
        "irM5oz84wmvE",
        "zhLIHTlkwvBw"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hbw-GRN-lbpO",
        "colab_type": "text"
      },
      "source": [
        "### Steps to load Models, make predictions and view predictions\n",
        "\n",
        "Notice: You may encounter an error while loading the model due to multi GPU compatibility: https://github.com/keras-team/keras/issues/9562\n",
        "\n",
        "If you see such an error, you can always use Colab and train a new model, for which the code is provided in the notebook.\n",
        "\n",
        "Step 1- Run the first 12 code cells of the notebook.\n",
        "\n",
        "Step 2- Go to the last code cell of the notebook. This cell is to load model weights. \n",
        "\n",
        "Step 3- Go to second last code cell of the notebook. This cell is to make predictions and load them."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gQZ-wrpwqkpw",
        "colab_type": "text"
      },
      "source": [
        "Step 1 note: Don't forget to change pickle paths. This is the path your dataset is loaded from.\n",
        "\n",
        "Step 2 note: Don't forget to modify \"model_path\" variable so you can use the shared model weight files. This is the path where your model weights reside in.\n",
        "\n",
        "Step 3 Note: Don't forget to modify \"plot_path\" variable. This variable determines where to save the plot."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "irM5oz84wmvE",
        "colab_type": "text"
      },
      "source": [
        "### Links of the shared data and weight files"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WyCtnIYcq7b1",
        "colab_type": "text"
      },
      "source": [
        "__________________________"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "huEojNoSq0Y_",
        "colab_type": "text"
      },
      "source": [
        "Links of the shared Weight Files:\n",
        "\n",
        "250 epochs:\n",
        "\n",
        "\thttps://drive.google.com/drive/folders/1xUVIkAGRHxXUhNj-V2-q6M7pnXSn_V4e?usp=sharing\n",
        "\n",
        "\n",
        "500 epochs:\n",
        "\n",
        "\thttps://drive.google.com/drive/folders/1nPecL3pfFkPDTQTnz3PofWWtdk-u3IZF?usp=sharing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VyKlsoCarAE3",
        "colab_type": "text"
      },
      "source": [
        "______________________"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4EcADONlrCif",
        "colab_type": "text"
      },
      "source": [
        "Links of the shared Pickle Files:\n",
        "\n",
        "\tHalf Faces:\n",
        "  \n",
        "\t\tGround Truths:\n",
        "\n",
        "\t\t\thttps://drive.google.com/file/d/1-JVnG_wVJR3VgAwi6-Hhu2C-ZAyQ2-_9/view?usp=sharing\n",
        "\n",
        "\t\tOccluded Images:\n",
        "\t\t\thttps://drive.google.com/file/d/1-7E0x-UGFjotUH8UJAWruM9Y0rwEzYzV/view?usp=sharing\n",
        "\n",
        "\n",
        "\n",
        "\tUnrestricted Occlusions:\n",
        "\n",
        "\t\tGround Truths:\n",
        "\n",
        "\t\t\thttps://drive.google.com/file/d/19li26wV60jhrf8UtUhGH6xuocDqiHqPG/view?usp=sharing\n",
        "\n",
        "\t\tOccluded Images:\n",
        "\n",
        "\t\t\thttps://drive.google.com/file/d/179YgtbT7A0YFJsQyFULbQPgPZzdmnySA/view?usp=sharing\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zhLIHTlkwvBw",
        "colab_type": "text"
      },
      "source": [
        "### The Code"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PoWfF1Z226T-",
        "colab_type": "text"
      },
      "source": [
        "Step 1- Run the first 12 code cells\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3bLWZ7uNEgwk",
        "colab_type": "code",
        "outputId": "672346b5-8b97-49af-c3b2-37a6b91027ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pickle\n",
        "import os\n",
        "\n",
        "import keras\n",
        "import tensorflow as tf\n",
        "from keras.backend.tensorflow_backend import set_session\n",
        "\n",
        "\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Activation, Flatten, Dense, Dropout, BatchNormalization\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, UpSampling2D, Dropout, LeakyReLU, Conv2DTranspose, ReLU\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.layers import Reshape\n",
        "from tensorflow.keras import layers\n",
        "import datetime\n",
        "\n",
        "from keras import initializers\n",
        "\n",
        "\n",
        "config = tf.ConfigProto()\n",
        "config.gpu_options.per_process_gpu_memory_fraction = 0.9 # fraction of memory\n",
        "config.gpu_options.visible_device_list = \"0\"\n",
        "\n",
        "set_session(tf.Session(config=config))"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hQWow1vnIbGe",
        "colab_type": "code",
        "outputId": "3dbcad05-e745-445e-ac6f-ed38ddd4a224",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\", force_remount=False)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "St-UfS4sk5sC",
        "colab_type": "text"
      },
      "source": [
        "Step 1 note: Don't forget to change pickle paths."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2bEPxtcJISlx",
        "colab_type": "code",
        "outputId": "707a727f-6d1c-41fa-9398-2184fdd13ee4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "\n",
        "path = '/content/drive/My Drive/biometrics_data/'\n",
        "xname = '1_half_face_occluded.pickle'\n",
        "yname = '1_half_face_labels.pickle'\n",
        "pickle_in = open(os.path.join(path,xname),\"rb\")\n",
        "x = pickle.load(pickle_in)\n",
        "\n",
        "pickle_in = open(os.path.join(path,yname),\"rb\")\n",
        "y = pickle.load(pickle_in)\n",
        "\n",
        "print(x.shape)\n",
        "print(y.shape)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4471, 200, 200, 1)\n",
            "(4471, 200, 200, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cTpaol_6KMRG",
        "colab_type": "code",
        "outputId": "9b8100b2-f236-4022-d18b-ea2fc19f2021",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        }
      },
      "source": [
        "#I use this because with 200,200 images, I exceed GPU memory.\n",
        "\n",
        "from skimage.transform import resize\n",
        "x = resize(x, (len(x),64,64,1), anti_aliasing=False)\n",
        "y = resize(y, (len(y),64,64,1), anti_aliasing=False)\n",
        "\n",
        "# Print the shape after resize\n",
        "print(x.shape)\n",
        "print(y.shape)\n",
        "\n",
        "#Draw the image to be sure occluded image is the same as the ground truth one\n",
        "fig=plt.figure(figsize=(6, 6))\n",
        "fig.add_subplot(1, 2, 1)\n",
        "plt.imshow(x[1,:,:,0],cmap=\"gray\")\n",
        "fig.add_subplot(1, 2, 2)\n",
        "plt.imshow(y[1,:,:,0],cmap=\"gray\")"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4471, 64, 64, 1)\n",
            "(4471, 64, 64, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fe50663bac8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
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gaTaQecT2VY5Jxbl790ImFy/HjFeyznO0yuFpzUPK5aJzy/QC8LnPfQ5ojp1W0sqcdOON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\nWMIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vWxHj7P1djZv",
        "colab_type": "code",
        "outputId": "8c9fd2f8-59b4-42cc-cd4b-cec11ee3480c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "#I print pixel values to be sure occluded and ground truth images are aligned\n",
        "print(x[0,0:5,0:5,0])\n",
        "print(y[0,0:5,0:5,0])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.25130208 0.304856   0.30303309 0.34253983 0.3629136 ]\n",
            " [0.27388174 0.32608762 0.34776348 0.36331189 0.38823529]\n",
            " [0.23821998 0.30718444 0.32617953 0.38941483 0.38988971]\n",
            " [0.27643995 0.32598039 0.35706189 0.36937806 0.41199449]\n",
            " [0.2950674  0.32501532 0.3527114  0.38802083 0.40833333]]\n",
            "[[0.25130208 0.304856   0.30303309 0.34253983 0.3629136 ]\n",
            " [0.27388174 0.32608762 0.34776348 0.36331189 0.38823529]\n",
            " [0.23821998 0.30718444 0.32617953 0.38941483 0.38988971]\n",
            " [0.27643995 0.32598039 0.35706189 0.36937806 0.41199449]\n",
            " [0.2950674  0.32501532 0.3527114  0.38802083 0.40833333]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PFe3-3yPEldc",
        "colab_type": "code",
        "outputId": "9b28297a-be5e-475a-8dd7-f1a5cd075768",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "#Here is the function to create a generator. I also use the function and create a generator in the same cell.\n",
        "def creategen():\n",
        "  generator = Sequential()\n",
        "\n",
        "\n",
        "  generator.add(Conv2D(64, (5,5) , strides = (2,2), input_shape = x.shape[1:] , padding = \"SAME\",kernel_initializer = 'random_normal'))\n",
        "  generator.add(BatchNormalization())\n",
        "  generator.add(ReLU())  \n",
        "  generator.add(Dropout(0.3))\n",
        "\n",
        "\n",
        "  generator.add(Conv2D(128, (5,5) ,  strides = (2,2),padding = \"SAME\",kernel_initializer = 'random_normal'))\n",
        "  generator.add(BatchNormalization())\n",
        "  generator.add(ReLU())  \n",
        "  generator.add(Dropout(0.3))\n",
        "\n",
        "  \n",
        "  generator.add(Conv2D(256, (5,5) ,  strides = (2,2), padding = \"SAME\",kernel_initializer = 'random_normal'))\n",
        "  generator.add(BatchNormalization())\n",
        "  generator.add(ReLU())  \n",
        "  generator.add(Dropout(0.3))\n",
        "\n",
        "  # I would use these if there was a bottleneck in the network.\n",
        "  #generator.add(Flatten())\n",
        "  \n",
        "  #generator.add(Dense(64)) \n",
        "  #generator.add(BatchNormalization())\n",
        "  #generator.add(ReLU(alpha=0.2))\n",
        "  \n",
        "  #generator.add(Dense(8*8*128)) \n",
        "  #generator.add(BatchNormalization())\n",
        "  #generator.add(ReLU(alpha=0.2))\n",
        "  \n",
        "  #generator.add(layers.Reshape((8,8,128)))\n",
        "\n",
        "\n",
        "  generator.add(Conv2DTranspose(128, (5, 5), strides=(2, 2), padding='same', use_bias=False))\n",
        "  generator.add(BatchNormalization())\n",
        "  generator.add(ReLU())\n",
        "  \n",
        "  generator.add(Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))\n",
        "  generator.add(BatchNormalization())\n",
        "  generator.add(ReLU())\n",
        "  \n",
        "  generator.add(Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation = \"tanh\"))\n",
        "  \n",
        "\n",
        "  return generator\n",
        "\n",
        "generator = creategen()\n",
        "\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0615 12:59:44.105421 140624213170048 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/initializers.py:143: calling RandomNormal.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "W0615 12:59:44.566210 140624213170048 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1W9TidqzEubv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Here is the function to create a discriminator. \n",
        "#I also use the function and create a discriminator in the same cell.\n",
        "def createdisc():\n",
        "  discriminator = Sequential()\n",
        "\n",
        "  discriminator.add(Conv2D(64, (5,5) , strides = (2,2), input_shape = x.shape[1:] , padding = \"SAME\",kernel_initializer = 'random_normal'))\n",
        "  discriminator.add(BatchNormalization())\n",
        "  discriminator.add(LeakyReLU(alpha=0.2))  \n",
        "  discriminator.add(Dropout(0.3))\n",
        "\n",
        "\n",
        "  discriminator.add(Conv2D(128, (5,5) ,  strides = (2,2),padding = \"SAME\",kernel_initializer = 'random_normal'))\n",
        "  discriminator.add(BatchNormalization())\n",
        "  discriminator.add(LeakyReLU(alpha=0.2))  \n",
        "  discriminator.add(Dropout(0.3))\n",
        "\n",
        "  \n",
        "  discriminator.add(Conv2D(256, (5,5) ,  strides = (2,2), padding = \"SAME\",kernel_initializer = 'random_normal'))\n",
        "  discriminator.add(BatchNormalization())\n",
        "  discriminator.add(LeakyReLU(alpha=0.2))  \n",
        "  discriminator.add(Dropout(0.3))\n",
        "\n",
        "  \n",
        "  discriminator.add(Flatten())\n",
        "  discriminator.add(Dense(1))\n",
        "  \n",
        "  return discriminator\n",
        "\n",
        "\n",
        "discriminator = createdisc()\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QmBb2AVRUYek",
        "colab_type": "code",
        "outputId": "e30044e3-921c-416a-c25c-94fca0188bf3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 877
        }
      },
      "source": [
        "generator.summary()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 32, 32, 64)        1664      \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 32, 32, 64)        256       \n",
            "_________________________________________________________________\n",
            "re_lu (ReLU)                 (None, 32, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 32, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 16, 16, 128)       204928    \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 16, 16, 128)       512       \n",
            "_________________________________________________________________\n",
            "re_lu_1 (ReLU)               (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 8, 8, 256)         819456    \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 8, 8, 256)         1024      \n",
            "_________________________________________________________________\n",
            "re_lu_2 (ReLU)               (None, 8, 8, 256)         0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 8, 8, 256)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_transpose (Conv2DTran (None, 16, 16, 128)       819200    \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 16, 16, 128)       512       \n",
            "_________________________________________________________________\n",
            "re_lu_3 (ReLU)               (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_1 (Conv2DTr (None, 32, 32, 64)        204800    \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 32, 32, 64)        256       \n",
            "_________________________________________________________________\n",
            "re_lu_4 (ReLU)               (None, 32, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_2 (Conv2DTr (None, 64, 64, 1)         1600      \n",
            "=================================================================\n",
            "Total params: 2,054,208\n",
            "Trainable params: 2,052,928\n",
            "Non-trainable params: 1,280\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZadB3ZS4vzoR",
        "colab_type": "code",
        "outputId": "57875b87-91d1-47f0-e0af-06c2af483a6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 690
        }
      },
      "source": [
        "# Learning rate of discriminator is advised to be the double of the generator. \n",
        "# I train the generator two times in each step of training. First is with Gan loss, second is with L2 loss.\n",
        "# That is why I use x4 learning rate in discriminator.\n",
        "\n",
        "opt_disc = Adam(lr=0.00004)\n",
        "discriminator.trainable = True\n",
        "discriminator.compile(loss = \"binary_crossentropy\", optimizer = opt_disc)\n",
        "discriminator.summary()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_3 (Conv2D)            (None, 32, 32, 64)        1664      \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 32, 32, 64)        256       \n",
            "_________________________________________________________________\n",
            "leaky_re_lu (LeakyReLU)      (None, 32, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 32, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 16, 16, 128)       204928    \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 16, 16, 128)       512       \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_1 (LeakyReLU)    (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 8, 8, 256)         819456    \n",
            "_________________________________________________________________\n",
            "batch_normalization_7 (Batch (None, 8, 8, 256)         1024      \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_2 (LeakyReLU)    (None, 8, 8, 256)         0         \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 8, 8, 256)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 16384)             0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1)                 16385     \n",
            "=================================================================\n",
            "Total params: 1,044,225\n",
            "Trainable params: 1,043,329\n",
            "Non-trainable params: 896\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k3wLocRlM2fY",
        "colab_type": "code",
        "outputId": "6d3d7308-3ff3-4660-f0bc-b485acc93287",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 877
        }
      },
      "source": [
        "opt_gen = Adam(lr=0.00001)\n",
        "generator.compile(loss='mean_squared_error', optimizer = opt_gen)\n",
        "generator.summary()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 32, 32, 64)        1664      \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 32, 32, 64)        256       \n",
            "_________________________________________________________________\n",
            "re_lu (ReLU)                 (None, 32, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 32, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 16, 16, 128)       204928    \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 16, 16, 128)       512       \n",
            "_________________________________________________________________\n",
            "re_lu_1 (ReLU)               (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 8, 8, 256)         819456    \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 8, 8, 256)         1024      \n",
            "_________________________________________________________________\n",
            "re_lu_2 (ReLU)               (None, 8, 8, 256)         0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 8, 8, 256)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_transpose (Conv2DTran (None, 16, 16, 128)       819200    \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 16, 16, 128)       512       \n",
            "_________________________________________________________________\n",
            "re_lu_3 (ReLU)               (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_1 (Conv2DTr (None, 32, 32, 64)        204800    \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 32, 32, 64)        256       \n",
            "_________________________________________________________________\n",
            "re_lu_4 (ReLU)               (None, 32, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_2 (Conv2DTr (None, 64, 64, 1)         1600      \n",
            "=================================================================\n",
            "Total params: 2,054,208\n",
            "Trainable params: 2,052,928\n",
            "Non-trainable params: 1,280\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bLMiISsSFQyL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Here is the function to create a GAN model. I also use the function and create GAN model in the same cell.\n",
        "\n",
        "def creategan(generator,discriminator):\n",
        "  gan = Sequential()\n",
        "  gan.add(generator)\n",
        "  discriminator.trainable = False\n",
        "  gan.add(discriminator)\n",
        "  return(gan)\n",
        "\n",
        "gan = creategan(generator,discriminator)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NCaQuj77wZ8W",
        "colab_type": "code",
        "outputId": "2cee3a22-bb8c-49a2-bfc7-e4a5659cf474",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "opt_gan = Adam(lr=0.00001)\n",
        "gan.compile(loss = \"binary_crossentropy\", optimizer = opt_gan)\n",
        "gan.summary()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "sequential (Sequential)      (None, 64, 64, 1)         2054208   \n",
            "_________________________________________________________________\n",
            "sequential_1 (Sequential)    (None, 1)                 1044225   \n",
            "=================================================================\n",
            "Total params: 3,098,433\n",
            "Trainable params: 2,052,928\n",
            "Non-trainable params: 1,045,505\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wi-J96roLL9W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# This is to save models. It saves both the architectures and the weights.\n",
        "# While loading the model, you will only need weights since you declared the architecture in the previous cells.\n",
        "\n",
        "def save_models(gan,discriminator,generator,path,epoch):\n",
        "  datenow = str(datetime.datetime.now().strftime('%m-%d-%H:%M'))\n",
        "  gan.save_weights(os.path.join(path,\"{0}_wgan_{1}.h5\".format(epoch,datenow)))\n",
        "  gan.save(os.path.join(path,\"{0}_mgan_{1}.h5\".format(epoch,datenow)))\n",
        "\n",
        "\n",
        "  discriminator.save_weights(os.path.join(path,\"{0}_wd_{1}.h5\".format(epoch,datenow)))\n",
        "  discriminator.save(os.path.join(path,\"{0}_md_{1}.h5\".format(epoch,datenow)))\n",
        "\n",
        "\n",
        "  generator.save_weights(os.path.join(path,\"{0}_wg_{1}.h5\".format(epoch,datenow)))\n",
        "  generator.save(os.path.join(path,\"{0}_mg_{1}.h5\".format(epoch,datenow)))\n",
        "  \n",
        "\n",
        "  \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tpfaOehsWRKw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# This is to obtain test losses in each training step. I use this function in the \"train\" function.\n",
        "def test(x,y):\n",
        "  gan_inp_t = x  \n",
        "  gan_label_t = np.ones([len(gan_inp_t)])\n",
        "  gan_predict_t = None\n",
        "  \n",
        "  disc_inp_t = None\n",
        "\n",
        "\n",
        "  disc_label_t = np.zeros([len(gan_inp_t)*2])\n",
        "  disc_label_t[len(gan_inp_t):] = 1\n",
        "  \n",
        "  disc_predict_t = None\n",
        "  \n",
        "\n",
        "  gen_predict_t = generator.predict(gan_inp_t)\n",
        "\n",
        "\n",
        "  disc_inp_t = np.concatenate((gen_predict_t,y), axis = 0)\n",
        "  disc_predict_t = discriminator.predict(disc_inp_t)\n",
        "\n",
        "  d_loss_t = discriminator.test_on_batch(disc_inp_t,disc_label_t)\n",
        "\n",
        "  gan_loss_t = gan.test_on_batch(gan_inp_t,gan_label_t)\n",
        "  \n",
        "  return (gan_loss_t, d_loss_t)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W7vPoV2QlqyU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# In the previous versions, I used this function to pretrain the discriminator in each training step.\n",
        "# I do not use this function in this version.\n",
        "def pretrain(x,y,nepoch):\n",
        "    \n",
        "  gen_predict = None\n",
        "  \n",
        "  \n",
        "  gan_inp = x  \n",
        "  gan_label = np.ones(64)\n",
        "  gan_predict = None\n",
        "  \n",
        "  disc_inp = None\n",
        "\n",
        "\n",
        "  disc_label = np.zeros(64*2)\n",
        "  disc_label[64:] = 1\n",
        "  \n",
        "  disc_predict = None\n",
        "  \n",
        "  \n",
        "  for epoch in range(nepoch):\n",
        "    for batch_ctr in range(65):\n",
        "\n",
        "\n",
        "      gen_predict = generator.predict(gan_inp[batch_ctr*64:(batch_ctr+1)*64])\n",
        "\n",
        "\n",
        "      disc_inp = np.concatenate((gen_predict,y[batch_ctr*64:(batch_ctr+1)*64]), axis = 0)\n",
        "      disc_predict = discriminator.predict(disc_inp)\n",
        "\n",
        "\n",
        "\n",
        "      d_loss = discriminator.train_on_batch(disc_inp,disc_label)\n",
        "\n",
        "\n",
        "      gan_loss = gan.test_on_batch(gan_inp[batch_ctr*64:(batch_ctr+1)*64],\n",
        "                                    gan_label)\n",
        "    \n",
        "\n",
        "    (tgan,tdisc)= test(x[4160:],y[4160:])\n",
        "    print(\"Pretrain Epoch Gan Loss: {0}       Disc Loss: {1}\".format(gan_loss,d_loss))\n",
        "    print(\"Pretrain Epoch Test Gan Loss: {0}  Test Disc Loss: {1} \\n\\n\\n\".format(tgan,tdisc))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XiZh2paxOHTd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# I use isolated mini batches to slow down discriminator:\n",
        "# I also use smooth/noisy labels proposed by Salimans et al 2016\n",
        "# Reference: https://github.com/soumith/ganhacks\n",
        "\n",
        "\n",
        "\n",
        "def train(x,y, nepoch, model_save_path = \"/content/drive/My Drive/biometrics_data/models/dummymodel\"):\n",
        "\n",
        "  gen_predict = None\n",
        "  \n",
        "  #Initialize the inputs and the labels.\n",
        "  gan_inp = x  \n",
        "  gan_label = np.ones(64)\n",
        "  gan_predict = None\n",
        "  \n",
        "  disc_inp = None\n",
        "\n",
        "\n",
        "  disc_label = np.zeros(64*2)\n",
        "  disc_label[64:] = 1\n",
        "  \n",
        "  disc_predict = None\n",
        "  \n",
        "\n",
        "  sess = tf.Session()\n",
        "  \n",
        "\n",
        "  for epoch in range(nepoch):\n",
        "\n",
        "    for batch_ctr in range(65):\n",
        "\n",
        "      #Generator makes a prediction.\n",
        "      gen_predict = generator.predict(gan_inp[batch_ctr*64:(batch_ctr+1)*64])\n",
        "\n",
        "\n",
        "      #Minibatch isolation and label smoothing is done here:\n",
        "      \n",
        "      if(epoch%2==0):\n",
        "        disc_inp = gen_predict\n",
        "        #disc_label = np.zeros(64) //// I would use this line if there was no noisy labels.\n",
        "        disc_label = np.random.normal(loc=0, scale=0.10, size=64)\n",
        "      else:\n",
        "        disc_inp = y[batch_ctr*64:(batch_ctr+1)*64]\n",
        "        #disc_label = np.ones(64)  //// I would use this line if there was no noisy labels.\n",
        "        disc_label = np.random.normal(loc=1, scale=0.10, size=64)\n",
        "\n",
        "\n",
        "        \n",
        "      #Initialize a label variable for generator to use it in training.  \n",
        "      gen_label = y[batch_ctr*64:(batch_ctr+1)*64]\n",
        "      \n",
        "      \n",
        "      #Do one training step. Also assign the losses to variables. We will print them.\n",
        "      d_loss = discriminator.train_on_batch(disc_inp,disc_label)\n",
        "\n",
        "\n",
        "      gan_loss = gan.train_on_batch(gan_inp[batch_ctr*64:(batch_ctr+1)*64],\n",
        "                                    gan_label)\n",
        "\n",
        "      gen_loss = generator.train_on_batch(gan_inp[batch_ctr*64:(batch_ctr+1)*64],gen_label)\n",
        "      \n",
        "    \n",
        "    \n",
        "    if epoch+1%500 == 0:\n",
        "      save_models(gan,discriminator,generator, model_save_path,epoch+1)  \n",
        "      print(\"MODEL SAVED\")\n",
        "      \n",
        "    \n",
        "    # Test images are the images after the 4160th image. It makes 311 test images.\n",
        "    (tgan,tdisc)= test(x[4160:],y[4160:])\n",
        "    print(\"Epoch: {2} Gan Loss: {0}       Disc Loss: {1}        Gen Loss: {3}\".format(gan_loss,d_loss,epoch+1,gen_loss))\n",
        "    print(\"Epoch: {2} Test Gan Loss: {0}  Test Disc Loss: {1} \\n\\n\\n\".format(tgan,tdisc,epoch+1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JmhHddBiTVjK",
        "colab_type": "code",
        "outputId": "65d1bd4d-2217-428e-e514-1d92f9ac7e90",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "# Checking lengths of the input and ground truth arrays. Also checking if normalization is done.\n",
        "print(len(x),len(y))\n",
        "print(x.max(),x.min())\n",
        "print(y.max(),y.min())\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4471 4471\n",
            "1.0 0.0\n",
            "1.0 0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tcWGd0HNqQK2",
        "colab_type": "text"
      },
      "source": [
        "If you would like to make trainings, don't forget to modify \"model_save_path\" variable."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WB3UtK6-1MlK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 56
        },
        "outputId": "c04caa22-ac8a-4d3d-a634-51b4576cf87d"
      },
      "source": [
        "# Use this cell if you have loaded and viewed the results, and want to train further.\n",
        "# If you start the model from beginning, using above cells where I declare \n",
        "# generator architecture, discriminator architecture etc. , you don't need to use this cell since\n",
        "# I already compile the models in above cells.\n",
        "\n",
        "\n",
        "# Learning rate of discriminator is advised to be the double of the generator. \n",
        "# I train the generator two times in each step of training. First is with Gan loss, second is with L2 loss.\n",
        "# That is why I use x4 learning rate in discriminator.\n",
        "\n",
        "\"\"\"opt_disc = Adam(lr=0.00004)\n",
        "discriminator.trainable = True\n",
        "discriminator.compile(loss = \"binary_crossentropy\", optimizer = opt_disc)\n",
        "discriminator.summary()\n",
        "\n",
        "opt_gen = Adam(lr=0.00001)\n",
        "generator.compile(loss='mean_squared_error', optimizer = opt_gen)\n",
        "generator.summary()\n",
        "\n",
        "opt_gan = Adam(lr=0.00001)\n",
        "gan.compile(loss = \"binary_crossentropy\", optimizer = opt_gan)\n",
        "gan.summary()\"\"\""
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'opt_disc = Adam(lr=0.00004)\\ndiscriminator.trainable = True\\ndiscriminator.compile(loss = \"binary_crossentropy\", optimizer = opt_disc)\\ndiscriminator.summary()\\n\\nopt_gen = Adam(lr=0.00001)\\ngenerator.compile(loss=\\'mean_squared_error\\', optimizer = opt_gen)\\ngenerator.summary()\\n\\nopt_gan = Adam(lr=0.00001)\\ngan.compile(loss = \"binary_crossentropy\", optimizer = opt_gan)\\ngan.summary()'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J4yECos4QNIe",
        "colab_type": "code",
        "outputId": "71efe16a-5609-41ec-baa6-789ab1853646",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 11494
        }
      },
      "source": [
        "# To train, don't forget to:\n",
        "# -compile the models\n",
        "# - run the necessary cells to declare the necessary functions such as \"train\" and \"save_models\" and \"test\"\n",
        "\n",
        "#If you train models from beginning, just run the cells of the notebook in order.\n",
        "\n",
        "#If you want to further train loaded models, use the previous cell to compile models.\n",
        "\n",
        "model_save_path = \"/content/drive/My Drive/biometrics_data/models/half_face\"\n",
        "batch_size = 64\n",
        "train(x,y,504,model_save_path)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0615 12:59:49.312861 140624213170048 training.py:2197] Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
            "W0615 12:59:49.426358 140624213170048 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 1 Gan Loss: 15.424948692321777       Disc Loss: 0.02699965238571167        Gen Loss: 0.33917707204818726\n",
            "Epoch: 1 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.67417049407959 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 2 Gan Loss: 0.31460461020469666       Disc Loss: -0.003959421068429947        Gen Loss: 0.19448310136795044\n",
            "Epoch: 2 Test Gan Loss: 1.8346877098083496  Test Disc Loss: 0.0897170677781105 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 3 Gan Loss: 7.74964714050293       Disc Loss: 0.46623095870018005        Gen Loss: 0.11904726922512054\n",
            "Epoch: 3 Test Gan Loss: 6.344828128814697  Test Disc Loss: 0.031923647969961166 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 4 Gan Loss: 0.5525617599487305       Disc Loss: 0.1054864227771759        Gen Loss: 0.07899534702301025\n",
            "Epoch: 4 Test Gan Loss: 1.0162090063095093  Test Disc Loss: 0.2520448863506317 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 5 Gan Loss: 13.94118881225586       Disc Loss: 0.21348145604133606        Gen Loss: 0.05697213113307953\n",
            "Epoch: 5 Test Gan Loss: 2.1777305603027344  Test Disc Loss: 0.5405696630477905 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 6 Gan Loss: 1.1652345657348633       Disc Loss: 0.34193843603134155        Gen Loss: 0.04352438822388649\n",
            "Epoch: 6 Test Gan Loss: 1.289751648902893  Test Disc Loss: 0.4380815625190735 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 7 Gan Loss: 12.961828231811523       Disc Loss: 0.1835959553718567        Gen Loss: 0.03524559736251831\n",
            "Epoch: 7 Test Gan Loss: 15.424947738647461  Test Disc Loss: 6.790205478668213 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 8 Gan Loss: 1.99534273147583       Disc Loss: 0.4222114086151123        Gen Loss: 0.029434099793434143\n",
            "Epoch: 8 Test Gan Loss: 4.8378496170043945  Test Disc Loss: 0.8912221193313599 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 9 Gan Loss: 15.201812744140625       Disc Loss: 0.3136347830295563        Gen Loss: 0.0258655846118927\n",
            "Epoch: 9 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.7124738693237305 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 10 Gan Loss: 2.1075057983398438       Disc Loss: 0.21538345515727997        Gen Loss: 0.02320854179561138\n",
            "Epoch: 10 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.7124738693237305 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 11 Gan Loss: 14.046333312988281       Disc Loss: 0.1251121312379837        Gen Loss: 0.021051090210676193\n",
            "Epoch: 11 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.7124738693237305 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 12 Gan Loss: 8.495137214660645       Disc Loss: 1.725151777267456        Gen Loss: 0.01963164657354355\n",
            "Epoch: 12 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.7124738693237305 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 13 Gan Loss: 13.648599624633789       Disc Loss: 0.44448351860046387        Gen Loss: 0.018552610650658607\n",
            "Epoch: 13 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.7124738693237305 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 14 Gan Loss: 2.022308588027954       Disc Loss: 0.5880019664764404        Gen Loss: 0.017709016799926758\n",
            "Epoch: 14 Test Gan Loss: 14.663421630859375  Test Disc Loss: 2.688899278640747 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 15 Gan Loss: 4.689872741699219       Disc Loss: 7.412837982177734        Gen Loss: 0.017097219824790955\n",
            "Epoch: 15 Test Gan Loss: 15.424947738647461  Test Disc Loss: 6.949312210083008 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 16 Gan Loss: 4.610850811004639       Disc Loss: 0.9511827230453491        Gen Loss: 0.016517356038093567\n",
            "Epoch: 16 Test Gan Loss: 15.424947738647461  Test Disc Loss: 6.880469799041748 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 17 Gan Loss: 9.117307662963867       Disc Loss: 2.1202144622802734        Gen Loss: 0.016009092330932617\n",
            "Epoch: 17 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.7124738693237305 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 18 Gan Loss: 5.239142417907715       Disc Loss: 0.7419081330299377        Gen Loss: 0.015534947626292706\n",
            "Epoch: 18 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.557852745056152 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 19 Gan Loss: 9.7306547164917       Disc Loss: 2.606595516204834        Gen Loss: 0.015380153432488441\n",
            "Epoch: 19 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.7124738693237305 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 20 Gan Loss: 5.54709529876709       Disc Loss: 2.307659864425659        Gen Loss: 0.015129057690501213\n",
            "Epoch: 20 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.656790733337402 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 21 Gan Loss: 12.66595458984375       Disc Loss: 1.2289468050003052        Gen Loss: 0.014805477112531662\n",
            "Epoch: 21 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.7124738693237305 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 22 Gan Loss: 6.429646015167236       Disc Loss: 0.9434088468551636        Gen Loss: 0.014401130378246307\n",
            "Epoch: 22 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.4996113777160645 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 23 Gan Loss: 11.98563289642334       Disc Loss: 1.0195587873458862        Gen Loss: 0.014078633859753609\n",
            "Epoch: 23 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.7124738693237305 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 24 Gan Loss: 6.215508937835693       Disc Loss: 1.6141325235366821        Gen Loss: 0.013860593549907207\n",
            "Epoch: 24 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.5933942794799805 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 25 Gan Loss: 14.494616508483887       Disc Loss: 0.0641084611415863        Gen Loss: 0.013749516569077969\n",
            "Epoch: 25 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.7124738693237305 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 26 Gan Loss: 7.710699081420898       Disc Loss: 1.6857231855392456        Gen Loss: 0.01353183202445507\n",
            "Epoch: 26 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.594416618347168 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 27 Gan Loss: 4.543856620788574       Disc Loss: 8.974237442016602        Gen Loss: 0.013335399329662323\n",
            "Epoch: 27 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.0799455642700195 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 28 Gan Loss: 11.279983520507812       Disc Loss: 2.772855758666992        Gen Loss: 0.013258758001029491\n",
            "Epoch: 28 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.7124738693237305 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 29 Gan Loss: 12.171736717224121       Disc Loss: 1.5828759670257568        Gen Loss: 0.012966953217983246\n",
            "Epoch: 29 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.7124738693237305 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 30 Gan Loss: 7.339411735534668       Disc Loss: 2.11545467376709        Gen Loss: 0.012769690714776516\n",
            "Epoch: 30 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.7124738693237305 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 31 Gan Loss: 10.36076545715332       Disc Loss: 2.7505433559417725        Gen Loss: 0.012860476970672607\n",
            "Epoch: 31 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.7124738693237305 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 32 Gan Loss: 7.268618583679199       Disc Loss: 1.0947113037109375        Gen Loss: 0.012427715584635735\n",
            "Epoch: 32 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.670627593994141 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 33 Gan Loss: 10.118368148803711       Disc Loss: 1.8365709781646729        Gen Loss: 0.012465642765164375\n",
            "Epoch: 33 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.7124738693237305 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 34 Gan Loss: 8.876309394836426       Disc Loss: 1.4785875082015991        Gen Loss: 0.012199214659631252\n",
            "Epoch: 34 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.5736894607543945 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 35 Gan Loss: 12.048130989074707       Disc Loss: 1.2446041107177734        Gen Loss: 0.012167439796030521\n",
            "Epoch: 35 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.650873184204102 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 36 Gan Loss: 8.5739164352417       Disc Loss: 1.5324403047561646        Gen Loss: 0.012009061872959137\n",
            "Epoch: 36 Test Gan Loss: 15.309455871582031  Test Disc Loss: 6.175200939178467 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 37 Gan Loss: 12.265339851379395       Disc Loss: 1.2566832304000854        Gen Loss: 0.011833412572741508\n",
            "Epoch: 37 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.490398406982422 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 38 Gan Loss: 9.892889022827148       Disc Loss: 1.6172921657562256        Gen Loss: 0.011721329763531685\n",
            "Epoch: 38 Test Gan Loss: 15.3506441116333  Test Disc Loss: 6.501123905181885 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 39 Gan Loss: 12.944127082824707       Disc Loss: 1.0426735877990723        Gen Loss: 0.011610964313149452\n",
            "Epoch: 39 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.49202823638916 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 40 Gan Loss: 12.467544555664062       Disc Loss: 2.030938148498535        Gen Loss: 0.011423252522945404\n",
            "Epoch: 40 Test Gan Loss: 15.348237037658691  Test Disc Loss: 6.323350429534912 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 41 Gan Loss: 11.470159530639648       Disc Loss: 1.9914510250091553        Gen Loss: 0.01149333268404007\n",
            "Epoch: 41 Test Gan Loss: 15.424947738647461  Test Disc Loss: 6.9698872566223145 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 42 Gan Loss: 8.58975601196289       Disc Loss: 2.0577313899993896        Gen Loss: 0.011358722113072872\n",
            "Epoch: 42 Test Gan Loss: 15.221900939941406  Test Disc Loss: 4.1962080001831055 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 43 Gan Loss: 13.755644798278809       Disc Loss: -0.34225353598594666        Gen Loss: 0.01132967323064804\n",
            "Epoch: 43 Test Gan Loss: 15.424947738647461  Test Disc Loss: 6.941848278045654 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 44 Gan Loss: 1.9184671640396118       Disc Loss: 1.035881519317627        Gen Loss: 0.011152571067214012\n",
            "Epoch: 44 Test Gan Loss: 0.11766380816698074  Test Disc Loss: 4.475856781005859 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 45 Gan Loss: 2.5725290775299072       Disc Loss: 10.436234474182129        Gen Loss: 0.010974965058267117\n",
            "Epoch: 45 Test Gan Loss: 0.0715860053896904  Test Disc Loss: 5.566915035247803 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 46 Gan Loss: 2.3342199325561523       Disc Loss: 1.1367430686950684        Gen Loss: 0.011096653528511524\n",
            "Epoch: 46 Test Gan Loss: 0.07348702847957611  Test Disc Loss: 5.5028886795043945 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 47 Gan Loss: 5.336105823516846       Disc Loss: 6.952095031738281        Gen Loss: 0.010939921252429485\n",
            "Epoch: 47 Test Gan Loss: 0.15315744280815125  Test Disc Loss: 3.9971134662628174 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 48 Gan Loss: 4.773412227630615       Disc Loss: 0.27755454182624817        Gen Loss: 0.010758621618151665\n",
            "Epoch: 48 Test Gan Loss: 0.15134017169475555  Test Disc Loss: 4.000687122344971 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 49 Gan Loss: 8.154706001281738       Disc Loss: 3.950627326965332        Gen Loss: 0.010797902941703796\n",
            "Epoch: 49 Test Gan Loss: 0.3973192274570465  Test Disc Loss: 1.0672260522842407 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 50 Gan Loss: 7.241023063659668       Disc Loss: 1.3129031658172607        Gen Loss: 0.010807029902935028\n",
            "Epoch: 50 Test Gan Loss: 0.40168437361717224  Test Disc Loss: 1.0578116178512573 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 51 Gan Loss: 10.12089729309082       Disc Loss: 2.7835826873779297        Gen Loss: 0.010580813512206078\n",
            "Epoch: 51 Test Gan Loss: 0.9139879941940308  Test Disc Loss: 0.44505220651626587 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 52 Gan Loss: 7.195752143859863       Disc Loss: 1.90794837474823        Gen Loss: 0.010654890909790993\n",
            "Epoch: 52 Test Gan Loss: 0.7105382680892944  Test Disc Loss: 0.5063906908035278 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 53 Gan Loss: 12.92981243133545       Disc Loss: 1.0420938730239868        Gen Loss: 0.010427500121295452\n",
            "Epoch: 53 Test Gan Loss: 1.9222691059112549  Test Disc Loss: 0.4062845706939697 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 54 Gan Loss: 10.281720161437988       Disc Loss: 1.576453685760498        Gen Loss: 0.010326996445655823\n",
            "Epoch: 54 Test Gan Loss: 1.421741247177124  Test Disc Loss: 0.4090541899204254 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 55 Gan Loss: 11.966644287109375       Disc Loss: 1.0813965797424316        Gen Loss: 0.01040368527173996\n",
            "Epoch: 55 Test Gan Loss: 3.450883388519287  Test Disc Loss: 0.46330496668815613 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 56 Gan Loss: 10.01235580444336       Disc Loss: 2.165999412536621        Gen Loss: 0.010433197021484375\n",
            "Epoch: 56 Test Gan Loss: 1.5497353076934814  Test Disc Loss: 0.4004093110561371 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 57 Gan Loss: 12.578222274780273       Disc Loss: 0.8225430250167847        Gen Loss: 0.010179243981838226\n",
            "Epoch: 57 Test Gan Loss: 4.608268737792969  Test Disc Loss: 0.5022510290145874 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 58 Gan Loss: 7.279635906219482       Disc Loss: 1.7662967443466187        Gen Loss: 0.010210511274635792\n",
            "Epoch: 58 Test Gan Loss: 1.2499176263809204  Test Disc Loss: 0.3893028497695923 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 59 Gan Loss: 13.591853141784668       Disc Loss: 0.5493407845497131        Gen Loss: 0.01010986976325512\n",
            "Epoch: 59 Test Gan Loss: 11.521322250366211  Test Disc Loss: 0.8783789873123169 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 60 Gan Loss: 6.691862106323242       Disc Loss: 0.2773253321647644        Gen Loss: 0.01012047752737999\n",
            "Epoch: 60 Test Gan Loss: 0.8798701763153076  Test Disc Loss: 0.43025335669517517 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 61 Gan Loss: 13.463397979736328       Disc Loss: 0.8609785437583923        Gen Loss: 0.009857180528342724\n",
            "Epoch: 61 Test Gan Loss: 2.4446253776550293  Test Disc Loss: 0.42285582423210144 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 62 Gan Loss: 7.408827781677246       Disc Loss: 0.5304611921310425        Gen Loss: 0.010051104240119457\n",
            "Epoch: 62 Test Gan Loss: 0.8247506022453308  Test Disc Loss: 0.4439197778701782 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 63 Gan Loss: 15.221796035766602       Disc Loss: -0.17076322436332703        Gen Loss: 0.009948119521141052\n",
            "Epoch: 63 Test Gan Loss: 4.3516459465026855  Test Disc Loss: 0.4685138463973999 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 64 Gan Loss: 10.60824203491211       Disc Loss: 2.1060497760772705        Gen Loss: 0.009914498776197433\n",
            "Epoch: 64 Test Gan Loss: 0.8734070062637329  Test Disc Loss: 0.4250190556049347 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 65 Gan Loss: 11.947916030883789       Disc Loss: 0.525726854801178        Gen Loss: 0.009743431583046913\n",
            "Epoch: 65 Test Gan Loss: 1.4234974384307861  Test Disc Loss: 0.3872983753681183 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 66 Gan Loss: 5.43187141418457       Disc Loss: 0.7113568782806396        Gen Loss: 0.009837483987212181\n",
            "Epoch: 66 Test Gan Loss: 0.6778768301010132  Test Disc Loss: 0.5142643451690674 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 67 Gan Loss: 13.18397331237793       Disc Loss: 0.7417030334472656        Gen Loss: 0.009724916890263557\n",
            "Epoch: 67 Test Gan Loss: 2.8620383739471436  Test Disc Loss: 0.4299475848674774 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 68 Gan Loss: 5.983484268188477       Disc Loss: 0.35608911514282227        Gen Loss: 0.009756293147802353\n",
            "Epoch: 68 Test Gan Loss: 1.844718098640442  Test Disc Loss: 0.4196755290031433 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 69 Gan Loss: 14.549972534179688       Disc Loss: 0.7524541616439819        Gen Loss: 0.00958411768078804\n",
            "Epoch: 69 Test Gan Loss: 11.409692764282227  Test Disc Loss: 0.827987551689148 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 70 Gan Loss: 9.443907737731934       Disc Loss: 1.1585190296173096        Gen Loss: 0.009498197585344315\n",
            "Epoch: 70 Test Gan Loss: 6.266384124755859  Test Disc Loss: 0.4923364520072937 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 71 Gan Loss: 14.998414993286133       Disc Loss: -0.053411878645420074        Gen Loss: 0.009494783356785774\n",
            "Epoch: 71 Test Gan Loss: 1.1301236152648926  Test Disc Loss: 0.42089956998825073 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 72 Gan Loss: 14.28735065460205       Disc Loss: 7.051171779632568        Gen Loss: 0.009521901607513428\n",
            "Epoch: 72 Test Gan Loss: 0.33261507749557495  Test Disc Loss: 1.972051978111267 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 73 Gan Loss: 14.714136123657227       Disc Loss: 0.8352154493331909        Gen Loss: 0.00936315767467022\n",
            "Epoch: 73 Test Gan Loss: 0.45458096265792847  Test Disc Loss: 1.1026699542999268 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 74 Gan Loss: 11.319391250610352       Disc Loss: 4.439312934875488        Gen Loss: 0.00932464562356472\n",
            "Epoch: 74 Test Gan Loss: 0.24134697020053864  Test Disc Loss: 2.7136969566345215 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 75 Gan Loss: 12.227479934692383       Disc Loss: 1.2440743446350098        Gen Loss: 0.0092666856944561\n",
            "Epoch: 75 Test Gan Loss: 0.5155307650566101  Test Disc Loss: 0.9667298793792725 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 76 Gan Loss: 12.461395263671875       Disc Loss: 3.3681187629699707        Gen Loss: 0.009390191175043583\n",
            "Epoch: 76 Test Gan Loss: 0.42621660232543945  Test Disc Loss: 1.221388816833496 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 77 Gan Loss: 12.786125183105469       Disc Loss: 0.23107154667377472        Gen Loss: 0.009261627681553364\n",
            "Epoch: 77 Test Gan Loss: 0.8758593201637268  Test Disc Loss: 0.47735390067100525 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 78 Gan Loss: 9.954505920410156       Disc Loss: 1.4570025205612183        Gen Loss: 0.009105190634727478\n",
            "Epoch: 78 Test Gan Loss: 0.38788488507270813  Test Disc Loss: 1.519042730331421 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 79 Gan Loss: 13.114091873168945       Disc Loss: -0.11779730767011642        Gen Loss: 0.009164998307824135\n",
            "Epoch: 79 Test Gan Loss: 3.4559075832366943  Test Disc Loss: 0.4307830035686493 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 80 Gan Loss: 11.50240707397461       Disc Loss: 1.8787660598754883        Gen Loss: 0.009007501415908337\n",
            "Epoch: 80 Test Gan Loss: 0.7241053581237793  Test Disc Loss: 0.5666210651397705 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 81 Gan Loss: 13.18301773071289       Disc Loss: 0.40466412901878357        Gen Loss: 0.009009179659187794\n",
            "Epoch: 81 Test Gan Loss: 1.3548085689544678  Test Disc Loss: 0.4293219745159149 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 82 Gan Loss: 10.814002990722656       Disc Loss: 1.6491093635559082        Gen Loss: 0.009041602723300457\n",
            "Epoch: 82 Test Gan Loss: 0.9024083018302917  Test Disc Loss: 0.4885231852531433 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 83 Gan Loss: 14.3400297164917       Disc Loss: -0.12080760300159454        Gen Loss: 0.00928524136543274\n",
            "Epoch: 83 Test Gan Loss: 4.842245101928711  Test Disc Loss: 0.7041730284690857 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 84 Gan Loss: 10.908679962158203       Disc Loss: 4.17193603515625        Gen Loss: 0.008940465748310089\n",
            "Epoch: 84 Test Gan Loss: 1.1571046113967896  Test Disc Loss: 0.9779236316680908 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 85 Gan Loss: 14.947179794311523       Disc Loss: 0.15776723623275757        Gen Loss: 0.00896700844168663\n",
            "Epoch: 85 Test Gan Loss: 8.202353477478027  Test Disc Loss: 1.0159211158752441 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 86 Gan Loss: 12.223747253417969       Disc Loss: 3.150017261505127        Gen Loss: 0.008936879225075245\n",
            "Epoch: 86 Test Gan Loss: 2.9843921661376953  Test Disc Loss: 0.5576622486114502 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 87 Gan Loss: 14.73227596282959       Disc Loss: 0.6116017699241638        Gen Loss: 0.008862237446010113\n",
            "Epoch: 87 Test Gan Loss: 5.261011600494385  Test Disc Loss: 0.6082808971405029 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 88 Gan Loss: 10.177298545837402       Disc Loss: 2.0402655601501465        Gen Loss: 0.008746253326535225\n",
            "Epoch: 88 Test Gan Loss: 0.9766895771026611  Test Disc Loss: 0.8042863011360168 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 89 Gan Loss: 14.088899612426758       Disc Loss: 1.9467686414718628        Gen Loss: 0.008801212534308434\n",
            "Epoch: 89 Test Gan Loss: 3.910595178604126  Test Disc Loss: 0.4845576286315918 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 90 Gan Loss: 8.604665756225586       Disc Loss: 0.9516146183013916        Gen Loss: 0.008739255368709564\n",
            "Epoch: 90 Test Gan Loss: 0.9106274843215942  Test Disc Loss: 0.712678074836731 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 91 Gan Loss: 12.6972074508667       Disc Loss: 0.3221384286880493        Gen Loss: 0.008743560872972012\n",
            "Epoch: 91 Test Gan Loss: 2.99625301361084  Test Disc Loss: 0.47995656728744507 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 92 Gan Loss: 8.724835395812988       Disc Loss: 1.1356983184814453        Gen Loss: 0.008637277409434319\n",
            "Epoch: 92 Test Gan Loss: 0.38470086455345154  Test Disc Loss: 2.2261979579925537 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 93 Gan Loss: 13.434724807739258       Disc Loss: 0.5781996250152588        Gen Loss: 0.008678138256072998\n",
            "Epoch: 93 Test Gan Loss: 1.195570468902588  Test Disc Loss: 0.5744267106056213 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 94 Gan Loss: 10.143826484680176       Disc Loss: 1.9272043704986572        Gen Loss: 0.008751715533435345\n",
            "Epoch: 94 Test Gan Loss: 0.46700185537338257  Test Disc Loss: 2.0678927898406982 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 95 Gan Loss: 12.295618057250977       Disc Loss: 0.8652524352073669        Gen Loss: 0.008541712537407875\n",
            "Epoch: 95 Test Gan Loss: 1.30878746509552  Test Disc Loss: 0.5597322583198547 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 96 Gan Loss: 2.084299087524414       Disc Loss: 0.11087220907211304        Gen Loss: 0.008561590686440468\n",
            "Epoch: 96 Test Gan Loss: 8.746333122253418  Test Disc Loss: 0.9365964531898499 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 97 Gan Loss: 8.846344947814941       Disc Loss: 4.532351970672607        Gen Loss: 0.008606117218732834\n",
            "Epoch: 97 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.648492336273193 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 98 Gan Loss: 8.86337947845459       Disc Loss: 1.613706350326538        Gen Loss: 0.008701231330633163\n",
            "Epoch: 98 Test Gan Loss: 15.38422679901123  Test Disc Loss: 7.481385707855225 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 99 Gan Loss: 11.02524185180664       Disc Loss: 1.6906695365905762        Gen Loss: 0.008612397126853466\n",
            "Epoch: 99 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.64957332611084 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 100 Gan Loss: 12.501041412353516       Disc Loss: 1.5207716226577759        Gen Loss: 0.008524538017809391\n",
            "Epoch: 100 Test Gan Loss: 15.384994506835938  Test Disc Loss: 7.566999912261963 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 101 Gan Loss: 13.832008361816406       Disc Loss: 0.6564474701881409        Gen Loss: 0.008492780849337578\n",
            "Epoch: 101 Test Gan Loss: 15.424947738647461  Test Disc Loss: 7.66741943359375 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 102 Gan Loss: 3.0372676849365234       Disc Loss: -0.06071348488330841        Gen Loss: 0.008405737578868866\n",
            "Epoch: 102 Test Gan Loss: 5.128119945526123  Test Disc Loss: 0.6614778637886047 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 103 Gan Loss: 10.393542289733887       Disc Loss: 2.9969234466552734        Gen Loss: 0.008525297045707703\n",
            "Epoch: 103 Test Gan Loss: 12.77629566192627  Test Disc Loss: 2.2485146522521973 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 104 Gan Loss: 10.5109224319458       Disc Loss: 2.443807363510132        Gen Loss: 0.008261010982096195\n",
            "Epoch: 104 Test Gan Loss: 12.791940689086914  Test Disc Loss: 2.0509567260742188 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 105 Gan Loss: 13.845603942871094       Disc Loss: 0.5244130492210388        Gen Loss: 0.00838860496878624\n",
            "Epoch: 105 Test Gan Loss: 14.5836181640625  Test Disc Loss: 4.122808456420898 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 106 Gan Loss: 10.269414901733398       Disc Loss: 1.0780490636825562        Gen Loss: 0.008159098215401173\n",
            "Epoch: 106 Test Gan Loss: 12.326384544372559  Test Disc Loss: 1.9660192728042603 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 107 Gan Loss: 14.493005752563477       Disc Loss: 0.33154264092445374        Gen Loss: 0.00830372516065836\n",
            "Epoch: 107 Test Gan Loss: 12.30196475982666  Test Disc Loss: 1.9014794826507568 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 108 Gan Loss: 11.041129112243652       Disc Loss: 2.523414373397827        Gen Loss: 0.00825333409011364\n",
            "Epoch: 108 Test Gan Loss: 7.988635063171387  Test Disc Loss: 0.8704524040222168 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 109 Gan Loss: 14.251640319824219       Disc Loss: 0.4197976589202881        Gen Loss: 0.00816057063639164\n",
            "Epoch: 109 Test Gan Loss: 14.024008750915527  Test Disc Loss: 3.305402994155884 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 110 Gan Loss: 11.2852201461792       Disc Loss: 1.3696004152297974        Gen Loss: 0.008086500689387321\n",
            "Epoch: 110 Test Gan Loss: 3.2642126083374023  Test Disc Loss: 0.6335180997848511 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 111 Gan Loss: 12.935892105102539       Disc Loss: 1.112591028213501        Gen Loss: 0.008135935291647911\n",
            "Epoch: 111 Test Gan Loss: 7.7476301193237305  Test Disc Loss: 0.8780491948127747 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 112 Gan Loss: 9.465572357177734       Disc Loss: 2.826812267303467        Gen Loss: 0.008204592391848564\n",
            "Epoch: 112 Test Gan Loss: 2.5522921085357666  Test Disc Loss: 0.5626369714736938 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 113 Gan Loss: 14.294769287109375       Disc Loss: 0.6129496693611145        Gen Loss: 0.00816748570650816\n",
            "Epoch: 113 Test Gan Loss: 13.514811515808105  Test Disc Loss: 2.6054158210754395 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 114 Gan Loss: 10.48425579071045       Disc Loss: 2.8429906368255615        Gen Loss: 0.00806526280939579\n",
            "Epoch: 114 Test Gan Loss: 7.8692708015441895  Test Disc Loss: 0.8972488641738892 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 115 Gan Loss: 14.94291877746582       Disc Loss: 0.17894838750362396        Gen Loss: 0.008121205493807793\n",
            "Epoch: 115 Test Gan Loss: 14.53101634979248  Test Disc Loss: 4.222768306732178 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 116 Gan Loss: 9.651911735534668       Disc Loss: 1.460837960243225        Gen Loss: 0.008085601031780243\n",
            "Epoch: 116 Test Gan Loss: 11.434136390686035  Test Disc Loss: 1.6251457929611206 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 117 Gan Loss: 12.584900856018066       Disc Loss: 1.2007595300674438        Gen Loss: 0.008072327822446823\n",
            "Epoch: 117 Test Gan Loss: 14.91722583770752  Test Disc Loss: 5.100801467895508 \n",
            "\n",
            "\n",
            "\n",
            "Epoch: 118 Gan Loss: 10.356244087219238       Disc Loss: 1.2863644361495972        Gen Loss: 0.008112860843539238\n",
            "Epoch: 118 Test Gan Loss: 11.55204963684082  Test Disc Loss: 1.4339230060577393 \n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-19-35bfacb2c22c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mmodel_save_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"/content/drive/My Drive/biometrics_data/models/half_face\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m64\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mtrain\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[0;36m504\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmodel_save_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-16-8f26408521e7>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(x, y, nepoch, model_save_path)\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m       \u001b[0;31m#Do one training step. Also assign the losses to variables. We will print them.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m       \u001b[0md_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdiscriminator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_on_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdisc_inp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdisc_label\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     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight, reset_metrics)\u001b[0m\n\u001b[1;32m   1173\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_sample_weight_modes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_weights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1174\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1175\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;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1177\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mreset_metrics\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/backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   3290\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3291\u001b[0m     fetched = self._callable_fn(*array_vals,\n\u001b[0;32m-> 3292\u001b[0;31m                                 run_metadata=self.run_metadata)\n\u001b[0m\u001b[1;32m   3293\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_fetch_callbacks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetched\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[0m_fetches\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[1;32m   3294\u001b[0m     output_structure = nest.pack_sequence_as(\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1456\u001b[0m         ret = tf_session.TF_SessionRunCallable(self._session._session,\n\u001b[1;32m   1457\u001b[0m                                                \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\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[0;32m-> 1458\u001b[0;31m                                                run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1459\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1460\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[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fC6S0LiJqWCW",
        "colab_type": "text"
      },
      "source": [
        "Step 3 - Make predictions and view them.\n",
        "\n",
        "Step 3 Note: Don't forget to modify \"plot_path\" variable. This variable determines where to save the plot."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lzF8GvhT1_6C",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "outputId": "8d91c2cb-3d9c-464a-984b-06d65036dd84"
      },
      "source": [
        "#Making predictions and drawing them.\n",
        "#First row: Occluded images\n",
        "#Second row: Ground Truth images\n",
        "#Third row: Predictions\n",
        "\n",
        "import datetime\n",
        "plot_path = \"/content/drive/My Drive/biometrics_data/plots/half_face\"\n",
        "\n",
        "a = 4160\n",
        "b = 4170\n",
        "pred=generator.predict(x[a:b])\n",
        "\n",
        "fig = plt.figure(figsize = (20,10))\n",
        "for ctr in range(10):\n",
        "  fig.add_subplot(3,10,ctr+1)\n",
        "  plt.imshow(np.reshape(x[a + ctr],(64,64)),  cmap = \"gray\")\n",
        "\n",
        "  \n",
        "\n",
        "for ctr in range(10):\n",
        "  fig.add_subplot(3,10,(10 + ctr + 1))\n",
        "  plt.imshow(np.reshape(y[a + ctr]/255,(64,64)),  cmap = \"gray\")  \n",
        "\n",
        "\n",
        "for ctr in range(10):\n",
        "  fig.add_subplot(3,10,(20 + ctr + 1))\n",
        "  plt.imshow(np.reshape(pred[ctr],(64,64)),  cmap = \"gray\")\n",
        "  \n",
        "#plt.savefig(os.path.join(plot_path,str(datetime.datetime.now().strftime('%m-%d-%H:%M'))))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eYm9kMhn1UrO1Pm2wp068LLlczkYdpAAumOb7vkYfiBc0LYxGI6ysrAAYe6sl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\n1HVU1ks++9nP2o5Eh4CsrVwup/O4vr6u++KO3p8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54YUXLH5PJpNx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M5Nh7qX/LikH6/zKZjIvwExJIeDujPUQFqRBdRC8sI2Gs+MP+sB+EA847Wq2ysLBgVbOazab1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\nG8LhsCFw6PF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1VCo561j7jHQzndPaV0SKEwnJfKLRaFRee+01EZnawdoeVj5k3xLRxb2N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3Fo2eeu2NraWhQmRq+goh0kJp488GZaiAyfffbZPPnbFddfj1dQGvZQdM9094mnw/i7rays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y3JqlN9ZqtRxCMwvRR4T+fD53e3sWIu1v24cfXRNIEy1JEteRhImr805OTsJGyZKbFM8l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\nnNkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x720 with 30 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ka-RX2vblDFf",
        "colab_type": "text"
      },
      "source": [
        "Step 2- Load weights here:\n",
        "(Don't forget to modify the model_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cRUuhUhxXeNW",
        "colab_type": "text"
      },
      "source": [
        "You may encounter an error while loading the model due to multi GPU compatibility: https://github.com/keras-team/keras/issues/9562\n",
        "\n",
        "If you see such an error, you can always use Colab and train a new model. (Don't forget to use GPU.)\n",
        "\n",
        "The code to train the model exists in this notebook."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "3OxJnJn5QMLr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1302
        },
        "outputId": "07af0bb7-e676-4a9e-fc40-453ded49871a"
      },
      "source": [
        "model_path = \"/content/drive/My Drive/biometrics_data/models/last_experiment_500/\"\n",
        "\n",
        "epoch = 500\n",
        "\n",
        "\n",
        "\n",
        "#generator = creategen()\n",
        "generator.load_weights(os.path.join(model_path,\"{0}_wg.h5\".format(epoch)))\n",
        "#discriminator = createdisc()\n",
        "discriminator.load_weights(os.path.join(model_path,\"{0}_wd.h5\".format(epoch)))\n",
        "\n",
        "\n",
        "#gan = creategan(generator,discriminator)\n",
        "gan.load_weights(os.path.join(model_path,\"{0}_wgan.h5\".format(epoch)))"
      ],
      "execution_count": 15,
      "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-15-fef2138e4a6b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;31m#gan = creategan(generator,discriminator)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mgan\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"{0}_wgan.h5\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\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[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mload_weights\u001b[0;34m(self, filepath, by_name)\u001b[0m\n\u001b[1;32m    160\u001b[0m         raise ValueError('Load weights is not yet supported with TPUStrategy '\n\u001b[1;32m    161\u001b[0m                          'with steps_per_run greater than 1.')\n\u001b[0;32m--> 162\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mModel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mby_name\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    163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mtrackable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_automatic_dependency_tracking\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/network.py\u001b[0m in \u001b[0;36mload_weights\u001b[0;34m(self, filepath, by_name)\u001b[0m\n\u001b[1;32m   1422\u001b[0m         \u001b[0msaving\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_weights_from_hdf5_group_by_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1423\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[0;32m-> 1424\u001b[0;31m         \u001b[0msaving\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_weights_from_hdf5_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\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   1425\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1426\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_updated_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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/saving/hdf5_format.py\u001b[0m in \u001b[0;36mload_weights_from_hdf5_group\u001b[0;34m(f, layers)\u001b[0m\n\u001b[1;32m    747\u001b[0m     \u001b[0msymbolic_weights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_legacy_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer\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     weight_values = preprocess_weights_for_loading(\n\u001b[0;32m--> 749\u001b[0;31m         layer, weight_values, original_keras_version, original_backend)\n\u001b[0m\u001b[1;32m    750\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight_values\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msymbolic_weights\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    751\u001b[0m       raise ValueError('Layer #' + str(k) + ' (named \"' + layer.name +\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/saving/hdf5_format.py\u001b[0m in \u001b[0;36mpreprocess_weights_for_loading\u001b[0;34m(layer, weights, original_keras_version, original_backend)\u001b[0m\n\u001b[1;32m    372\u001b[0m     \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_nested_time_distributed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    373\u001b[0m   \u001b[0;32melif\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'Model'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Sequential'\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--> 374\u001b[0;31m     \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_nested_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights\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[0moriginal_keras_version\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'1'\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/saving/hdf5_format.py\u001b[0m in \u001b[0;36mconvert_nested_model\u001b[0;34m(weights)\u001b[0m\n\u001b[1;32m    360\u001b[0m             \u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnum_weights\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    361\u001b[0m             \u001b[0moriginal_keras_version\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moriginal_keras_version\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 362\u001b[0;31m             original_backend=original_backend))\n\u001b[0m\u001b[1;32m    363\u001b[0m         \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnum_weights\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    364\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mnew_weights\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/saving/hdf5_format.py\u001b[0m in \u001b[0;36mpreprocess_weights_for_loading\u001b[0;34m(layer, weights, original_keras_version, original_backend)\u001b[0m\n\u001b[1;32m    454\u001b[0m         \u001b[0mweights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconv_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_kernel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\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    455\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mK\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 456\u001b[0;31m       \u001b[0mweights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\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[0m\u001b[1;32m    457\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'ConvLSTM2D'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    458\u001b[0m         \u001b[0mweights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\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/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36mtranspose\u001b[0;34m(a, axes)\u001b[0m\n\u001b[1;32m    637\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    638\u001b[0m     \"\"\"\n\u001b[0;32m--> 639\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'transpose'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\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    640\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    641\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_wrapfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\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[0mkwds\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     55\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m)\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[0mkwds\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     57\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0;31m# An AttributeError occurs if the object does not have\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: axes don't match array"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BM3E25Ep2zjF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}