GAN_on_custom_dataset.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "GAN on custom dataset.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "##Mount Google Drive"
      ],
      "metadata": {
        "id": "EMu-MM6QQBer"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kx2coz4sO--h",
        "outputId": "ff29e6f3-6306-4d32-a45e-697dc33351c8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Importing Packages"
      ],
      "metadata": {
        "id": "blqBlC8_QHbT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Reshape, Flatten, Conv2D, Dense, Conv2DTranspose, Dropout\n",
        "from keras.layers.advanced_activations import LeakyReLU\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "import numpy as np\n",
        "!mkdir generated_images resized_images"
      ],
      "metadata": {
        "id": "BSwRW2OsPAur"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Unzip the file in a destination directory"
      ],
      "metadata": {
        "id": "IxaU0ogrQx4k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/drive/MyDrive/DATASETS/bobross2.zip -d bobross"
      ],
      "metadata": {
        "id": "zOQGLjlPQOP3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "images_path = \"/content/bobross/bobross2/\""
      ],
      "metadata": {
        "id": "hKkdjUm3RJtj"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Resizing Data to feed NN"
      ],
      "metadata": {
        "id": "iaLdLuD1Rxos"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import cv2\n",
        "\n",
        "reshape_size = (64,64)\n",
        "\n",
        "for i,image in enumerate(os.listdir(images_path)):\n",
        "  img_path = os.path.join(images_path, image)\n",
        "  img = cv2.imread(img_path)\n",
        "  img = cv2.resize(img, reshape_size)\n",
        "  cv2.imwrite(f\"resized_images/{i}.png\", img)"
      ],
      "metadata": {
        "id": "RLXiAzN_Rb4Q"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##NN Parameters"
      ],
      "metadata": {
        "id": "ARcUqgwrUSS-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "img_width = 64\n",
        "img_height = 64\n",
        "channels = 3\n",
        "img_shape = (img_width, img_height, channels)\n",
        "latent_dim = 100\n",
        "adam = Adam(lr = 0.0002)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "772IAwVvSYAT",
        "outputId": "84155f9e-09e3-4446-9275-d66365d7de06"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def build_generator():\n",
        "  model = Sequential([\n",
        "    Dense(256 * 8 * 8 , input_dim = latent_dim), #reshape the two 8 in case doent match\n",
        "    LeakyReLU(alpha = 0.2),\n",
        "    Reshape((8,8,256)), #reshape the two 8 in case doent match\n",
        "\n",
        "    Conv2DTranspose(128, (4,4), strides = (2,2), padding = \"same\"),\n",
        "    LeakyReLU(alpha = 0.2),\n",
        "    \n",
        "    Conv2DTranspose(128, (4,4), strides = (2,2), padding = \"same\"),\n",
        "    LeakyReLU(alpha = 0.2),\n",
        "\n",
        "    Conv2DTranspose(128, (4,4), strides = (2,2), padding = \"same\"),\n",
        "    LeakyReLU(alpha = 0.2),\n",
        "\n",
        "    Conv2D(3, (3,3), activation = \"tanh\", padding = \"same\") \n",
        "  ])\n",
        "\n",
        "  model.summary()\n",
        "  return model\n",
        "\n",
        "generator = build_generator()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qZSzv56oUh76",
        "outputId": "eea060ab-b7fc-49f0-e71e-4895008bee8b"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_1 (Dense)             (None, 16384)             1654784   \n",
            "                                                                 \n",
            " leaky_re_lu_4 (LeakyReLU)   (None, 16384)             0         \n",
            "                                                                 \n",
            " reshape_1 (Reshape)         (None, 8, 8, 256)         0         \n",
            "                                                                 \n",
            " conv2d_transpose_3 (Conv2DT  (None, 16, 16, 128)      524416    \n",
            " ranspose)                                                       \n",
            "                                                                 \n",
            " leaky_re_lu_5 (LeakyReLU)   (None, 16, 16, 128)       0         \n",
            "                                                                 \n",
            " conv2d_transpose_4 (Conv2DT  (None, 32, 32, 128)      262272    \n",
            " ranspose)                                                       \n",
            "                                                                 \n",
            " leaky_re_lu_6 (LeakyReLU)   (None, 32, 32, 128)       0         \n",
            "                                                                 \n",
            " conv2d_transpose_5 (Conv2DT  (None, 64, 64, 128)      262272    \n",
            " ranspose)                                                       \n",
            "                                                                 \n",
            " leaky_re_lu_7 (LeakyReLU)   (None, 64, 64, 128)       0         \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 64, 64, 3)         3459      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,707,203\n",
            "Trainable params: 2,707,203\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def build_discriminator():\n",
        "  model = Sequential([\n",
        "    Conv2D(64, (3,3), padding = \"same\", input_shape = img_shape),\n",
        "    LeakyReLU(alpha = 0.2),\n",
        "\n",
        "    Conv2D(128, (3,3), padding = \"same\"),\n",
        "    LeakyReLU(alpha = 0.2),\n",
        "\n",
        "    Conv2D(128, (3,3), padding = \"same\"),\n",
        "    LeakyReLU(alpha = 0.2),\n",
        "\n",
        "    Conv2D(256, (3,3), padding = \"same\"),\n",
        "    LeakyReLU(alpha = 0.2),\n",
        "\n",
        "    Flatten(),\n",
        "    Dropout(0.4),\n",
        "    Dense(1, activation = \"sigmoid\")\n",
        "\n",
        "  ])\n",
        "\n",
        "  model.summary()\n",
        "  return model\n",
        "\n",
        "discriminator = build_discriminator()\n",
        "discriminator.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2HrMlgKrVdyj",
        "outputId": "bfcc53fd-b7c3-4e49-a7bf-32ab62729859"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_10 (Conv2D)          (None, 64, 64, 64)        1792      \n",
            "                                                                 \n",
            " leaky_re_lu_16 (LeakyReLU)  (None, 64, 64, 64)        0         \n",
            "                                                                 \n",
            " conv2d_11 (Conv2D)          (None, 64, 64, 128)       73856     \n",
            "                                                                 \n",
            " leaky_re_lu_17 (LeakyReLU)  (None, 64, 64, 128)       0         \n",
            "                                                                 \n",
            " conv2d_12 (Conv2D)          (None, 64, 64, 128)       147584    \n",
            "                                                                 \n",
            " leaky_re_lu_18 (LeakyReLU)  (None, 64, 64, 128)       0         \n",
            "                                                                 \n",
            " conv2d_13 (Conv2D)          (None, 64, 64, 256)       295168    \n",
            "                                                                 \n",
            " leaky_re_lu_19 (LeakyReLU)  (None, 64, 64, 256)       0         \n",
            "                                                                 \n",
            " flatten_2 (Flatten)         (None, 1048576)           0         \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 1048576)           0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 1)                 1048577   \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,566,977\n",
            "Trainable params: 1,566,977\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Creating the GAN"
      ],
      "metadata": {
        "id": "KDcVprXQWiWy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "GAN = Sequential([\n",
        "  generator,\n",
        "  discriminator\n",
        "])\n",
        "\n",
        "GAN.layers[1].trainable = False\n",
        "GAN.summary()\n",
        "GAN.compile(loss = \"binary_crossentropy\", optimizer = adam)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qheb4BxxWU7n",
        "outputId": "f335e91e-a770-4541-ee10-64bb001ae4d1"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " sequential_1 (Sequential)   (None, 64, 64, 3)         2707203   \n",
            "                                                                 \n",
            " sequential_6 (Sequential)   (None, 1)                 1566977   \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,274,180\n",
            "Trainable params: 2,707,203\n",
            "Non-trainable params: 1,566,977\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Output Images"
      ],
      "metadata": {
        "id": "OEHPG3FSXYA1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import glob\n",
        "import imageio\n",
        "import PIL\n",
        "\n",
        "save_name = 0.00000000\n",
        "\n",
        "def save_imgs(epoch):\n",
        "    r, c = 2, 2\n",
        "    noise = np.random.normal(0, 1, (r * c, latent_dim))\n",
        "    gen_imgs = generator.predict(noise)\n",
        "    global save_name\n",
        "    save_name += 0.00000001\n",
        "    # print(\"%.8f\" % save_name)\n",
        "\n",
        "    # Rescale images 0 - 1\n",
        "    # gen_imgs = 0.5 * gen_imgs + 0.5\n",
        "    gen_imgs = (gen_imgs + 1) / 2.0\n",
        "    # gen_imgs = gen_imgs * 255\n",
        "\n",
        "    fig, axs = plt.subplots(r, c)\n",
        "    cnt = 0\n",
        "    for i in range(r):\n",
        "        for j in range(c):\n",
        "            axs[i,j].imshow(gen_imgs[cnt])\n",
        "            axs[i,j].axis('off')\n",
        "            cnt += 1\n",
        "    fig.savefig(\"generated_images/%.8f.png\" % save_name)\n",
        "    plt.close()"
      ],
      "metadata": {
        "id": "-p0XCySWW0Sd"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Training GAN"
      ],
      "metadata": {
        "id": "FrciOgFxXd22"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from PIL import Image\n",
        "\n",
        "array = []\n",
        "path = \"/content/resized_images/\"\n",
        "\n",
        "for image in os.listdir(path):\n",
        "  image = Image.open(path + image)\n",
        "  data = np.asarray(image)\n",
        "  array.append(data)\n",
        "\n",
        "X_train = np.array(array)\n",
        "X_train = X_train /127.5 -1.\n",
        "print(X_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PfjDhi34XfUL",
        "outputId": "09ebaf31-8a47-4992-a1e0-5954742a1fda"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2612, 64, 64, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Remeber the batch size should be proportional to the amoubt of data"
      ],
      "metadata": {
        "id": "yHv8E2LiYxEi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def train(epochs, batch_size = 32):\n",
        "  bat_per_epo = int(X_train.shape[0] / batch_size)\n",
        "\n",
        "  valid = np.ones((batch_size, 1))\n",
        "  fakes = np.zeros((batch_size, 1))\n",
        "\n",
        "  for epoch in range(epochs):\n",
        "    for j in range(bat_per_epo):\n",
        "      #Get random batch\n",
        "      idx = np.random.randint(0,X_train.shape[0], batch_size)\n",
        "      imgs = X_train[idx]\n",
        "\n",
        "      #Generate Fake Images\n",
        "      noise = np.random.normal(0,1, (batch_size, latent_dim))\n",
        "      gen_imgs = generator.predict(noise)\n",
        "\n",
        "      #train discriminator\n",
        "      d_loss_real = discriminator.train_on_batch(imgs, valid)\n",
        "      d_loss_fake = discriminator.train_on_batch(imgs,fakes)\n",
        "      d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)\n",
        "\n",
        "      noise = np.random.normal(0,1, (batch_size, latent_dim))\n",
        "\n",
        "      #inverse y label\n",
        "      g_loss = GAN.train_on_batch(noise,valid)\n",
        "\n",
        "      print(\"******* %d %d [D loss: %f, acc: %.2f%%] [G loss: %f]\" % (epoch,j, d_loss[0], 100* d_loss[1], g_loss))\n",
        "\n",
        "    save_imgs(epoch)\n",
        "\n",
        "train(60000, batch_size = 24)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "9MTnb_LDY0WL",
        "outputId": "35064a9b-d2a9-44df-f248-3165f7d608f7"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "******* 0 50 [D loss: 0.699227, acc: 33.33%] [G loss: 0.599636]\n",
            "******* 0 51 [D loss: 0.698795, acc: 35.42%] [G loss: 0.595855]\n"
          ]
        },
        {
          "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-37-a9fee25a3a48>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0msave_imgs\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[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m60000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m24\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-37-a9fee25a3a48>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(epochs, batch_size)\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m       \u001b[0;31m#inverse y label\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m       \u001b[0mg_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGAN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_on_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnoise\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvalid\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     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m       \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"******* %d %d [D loss: %f, acc: %.2f%%] [G loss: %f]\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md_loss\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m*\u001b[0m \u001b[0md_loss\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg_loss\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/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight, reset_metrics, return_dict)\u001b[0m\n\u001b[1;32m   1898\u001b[0m                                                     class_weight)\n\u001b[1;32m   1899\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_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-> 1900\u001b[0;31m       \u001b[0mlogs\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[0miterator\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   1901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1902\u001b[0m     \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msync_to_numpy_or_python_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogs\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/python3.7/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\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--> 150\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\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/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    908\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    909\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\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--> 910\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\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    911\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    912\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\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/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    940\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    941\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 942\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\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;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    943\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    944\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\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/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3129\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m   3130\u001b[0m     return graph_function._call_flat(\n\u001b[0;32m-> 3131\u001b[0;31m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m   3132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3133\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1958\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1959\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1960\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1961\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1962\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/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    601\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    602\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 603\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    604\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    605\u001b[0m           outputs = execute.execute_with_cancellation(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\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     58\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 59\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     60\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\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",
      "source": [
        "##Create a GIF"
      ],
      "metadata": {
        "id": "5ND4sQyCdNoc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Display a single image using the epoch number\n",
        "# def display_image(epoch_no):\n",
        "#   return PIL.Image.open('generated_images/%.8f.png'.format(epoch_no))\n",
        "\n",
        "anim_file = 'dcgan.gif'\n",
        "\n",
        "with imageio.get_writer(anim_file, mode='I') as writer:\n",
        "  filenames = glob.glob('generated_images/*.png')\n",
        "  filenames = sorted(filenames)\n",
        "  for filename in filenames:\n",
        "    image = imageio.imread(filename)\n",
        "    writer.append_data(image)\n",
        "  image = imageio.imread(filename)\n",
        "  writer.append_data(image)"
      ],
      "metadata": {
        "id": "_doHeuGmap0F"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}