Cycle_GAN.ipynb

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  "metadata": {
    "colab": {
      "name": "Cycle_GAN.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
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      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VfZgpKsjYozz",
        "colab_type": "text"
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      "source": [
        "\n",
        "## Cycle GAN\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B28lsc3BxIMt",
        "colab_type": "text"
      },
      "source": [
        "This notebook aims at implementing the Cycle GAN model based on the original paper https://arxiv.org/abs/1703.10593."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qiScUafIxIZJ",
        "colab_type": "text"
      },
      "source": [
        "First let us recall the idea of regular GAN model and then go on the how the CycleGAN differs. \n",
        "Given some training data $x$ and noise $z$. More details are given in our PDF report but the regular GAN optimization program is the following : \n",
        "\n",
        "The generator minimizes $log(1 – D(G(z)))$ and the discriminator maximizes $log D(x) + log(1 – D(G(z)))$. It has been shown that using least squares losses for this problem yields better results. Therefore we can use the following program. The generator will minimize $(D(G(z)) – 1)^2$ and the discriminator will minimize $(D(x) – 1)^2 + (D(G(z)))^2$.\n",
        "\n",
        "\n",
        "The Cycle GAN architecture contains two GAN each one being made of a generator and a discriminator. \n",
        "\n",
        "\n",
        "The goal is to learn mapping functions between two\n",
        "domains $\\mathcal{X}$ and $\\mathcal{Y}$ given training samples $\\{x_i\\}_{i=1, \\dots, N}$ where $x_i$ ∈ $\\mathcal{X}$ and $\\{y_j\\}_{j=1, \\dots, N}$ $y_j$ ∈ $\\mathcal{Y}$. Let us denote $x ∼ p_{data}(x)$ and $y ∼ p_{data}(y)$. \n",
        "\n",
        "The model will include two mappings $G : \\mathcal{X} → \\mathcal{Y}$ and $F : \\mathcal{Y} → \\mathcal{X}$.\n",
        "\n",
        "In addition, the authors introduce two adversarial discriminators $D_X$ and $D_Y$ , where $D_X$ aims to distinguish between images ${x}$ and translated\n",
        "images ${F(y)}$; in the same way, $D_Y$ aims to discriminate between ${y}$ and ${G(x)}$. \n",
        "\n",
        "Now the cycle gan architecture becomes different because of the loss it will consider. The objective contains two types of terms: adversarial losses (same as regular gan) for matching the distribution of generated images to the data distribution in the target domain and **cycle consistency losses **to prevent the learned mappings $G$ and $F$ from contradicting each other.\n",
        "\n",
        "\n",
        "For the mapping function $G : X → Y$ and its discriminator $D_Y$ , we express the objective as:\n",
        "\n",
        "$$\n",
        "L_{GAN}(G, D_Y , X, Y ) = \\mathbb{E}_{y∼p_{data}(y)} [log D_Y (y)]\n",
        "+ \\mathbb{E}_{x∼p_{data}(x)} [log(1 − D_Y (G(x))]\n",
        "$$\n",
        "\n",
        "\n",
        "Similarly for $F : Y → X$, the objective is : \n",
        "\n",
        "$$\n",
        "L_{GAN}(F, D_X , Y, X ) = \\mathbb{E}_{x∼p_{data}(x)} [log D_X (x)]\n",
        "+ \\mathbb{E}_{y∼p_{data}(y)} [log(1 − D_X (G(y))]\n",
        "$$\n",
        "\n",
        "\n",
        "And the novel part of the loss is :\n",
        "\n",
        "$$\n",
        "L_{cyc}(G, F) = \\mathbb{E}_{x∼p_{data}(x)} [||F(G(x)) − x||_1]\n",
        "+ \\mathbb{E}_{y∼p_{data}(y)} [||G(F(y)) − y||_1]\n",
        "$$\n",
        "\n",
        "\n",
        "Finally, the total loss the Cycle GAN will optimize is the following one : \n",
        "\n",
        "$$\n",
        "L(G, F, D_X, D_Y ) =L_{GAN}(G, D_Y , X, Y )\n",
        "+ L_{GAN}(F, D_X, Y, X)\n",
        "+ λL_{cyc}(G, F)\n",
        "$$\n",
        "\n",
        "\n",
        "Where $\\lambda$ is parameter to control how much ty cyclic loss term will count.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "However, as for the standard GAN, we will use least square losses for the GAN losses part. This is detailed later on.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WGOe2b4pGYXl",
        "colab_type": "code",
        "outputId": "338c4d36-7c4e-4eb5-8bc4-7f5b42ec4c1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 62
        }
      },
      "source": [
        "#import some basic packages\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "import random\n",
        "from sklearn.datasets import load_digits\n",
        "import os \n",
        "tf.enable_eager_execution()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will switch to TensorFlow 2.x on the 27th of March, 2020.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now\n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uARLhyjlbANW",
        "colab_type": "text"
      },
      "source": [
        "# Upload dataset\n",
        "\n",
        "Here we load all the data needed to train our cycle GAN. We use USPS and MNIST datasets. USPS dataset are pictures of black digits whereas MNIST dataset contains white digits. The goal of the cycle GAN is to convert white digit into black digit. The datasets contain images of size 28*28 which represent handwritten digits from 0 to 9.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rodoJuxk7H54",
        "colab_type": "code",
        "outputId": "f6a4c401-f0e3-43d4-fc69-ae3b23ec0e72",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nxhxPLzi7Kkc",
        "colab_type": "code",
        "outputId": "eecf1b96-6b88-49c2-eb3d-b2f0d8ab9436",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "raIaRWZELBfn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train=X_train/255"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GETZUoYdSTcS",
        "colab_type": "text"
      },
      "source": [
        "Since we only have 20 000 samples for the USPS dataset, we will keep only 20 000 samples from the MNIST dataset. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xO8lD-u6G99A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mnist = X_train[np.random.choice(range(60000), 20000), :, :]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M3s4HO0eBd5g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bb12W7JP40kY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "usps = np.load(\"../usps_array.npy\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iY1IaaMv7Ova",
        "colab_type": "code",
        "outputId": "0cfdaa78-e2b4-48f2-a865-2df1160a83ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "usps.shape "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(19999, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B9jCyKnhDYLF",
        "colab_type": "code",
        "outputId": "8efc2de7-e1c1-43b9-808f-fd6ff3b28d8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "plt.gray() #just to display every following images in gray scale"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EK_DXy2zOs05",
        "colab_type": "text"
      },
      "source": [
        "Sample from the USPS Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wqzHqhVCFf_7",
        "colab_type": "code",
        "outputId": "e2fe976f-b6ae-4822-db76-86e06c7ff9f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "source": [
        "f, axarr = plt.subplots(2,2)\n",
        "axarr[0,0].imshow(usps[15000,:,:])\n",
        "axarr[0,1].imshow(usps[4500,:,:])\n",
        "axarr[1,0].imshow(usps[2000,:,:])\n",
        "axarr[1,1].imshow(usps[30,:,:])\n",
        "f.suptitle('Sample image from USPS dataset')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lwaexuRNPPLHS7uu09dvf/nZV36etNlCzZs2q6p9wwgmV9oEHHlg19sgjj5QRUl0f+tCH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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UGR-9xpHOvsc",
        "colab_type": "text"
      },
      "source": [
        "Sample from the MNIST dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0qaCMfvL5RRN",
        "colab_type": "code",
        "outputId": "a7b027cf-3258-4b2e-cfe9-fac2029a8783",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "source": [
        "f, axarr = plt.subplots(2,2)\n",
        "axarr[0,0].imshow(mnist[15000,:,:])\n",
        "axarr[0,1].imshow(mnist[450,:,:])\n",
        "axarr[1,0].imshow(mnist[2000,:,:])\n",
        "axarr[1,1].imshow(mnist[31,:,:])\n",
        "f.suptitle('Sample image from MNIST dataset')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wEkC9J5eSHARgUO5DFCm+pua28rryZP0FBck2AB4DMMTMNsT7LLhQltX3PDMb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\nExEvqNiJiBdU7ETEC/8fWDI1/e9qlp4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YceiqKN7OyPm",
        "colab_type": "text"
      },
      "source": [
        "Looking at the picture we are going to use to perform image translation, one can see that this is in reality a simple task. This is why we will not use a very complex architecture for the generators and discriminators of the GANs, this will also allow for faster algorithm and less computationnal time. However, it would be quite straightforward to implement any kind of architecture by changing the Cycle_GAN object which is presented below. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_fk0c9RUYsYw",
        "colab_type": "text"
      },
      "source": [
        "# Creation of the model\n",
        "\n",
        "In this section we build the model by implementing the class Cycle_GAN. This class gathers the structure of the generator and the discriminators."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jo4Lokt0XuKx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Cycle_GAN(object):\n",
        "\n",
        "  def __init__(self, color=False, std=0.02):\n",
        "    self.n_channel = 1\n",
        "    if color:\n",
        "      self.n_channel = 3\n",
        "    # Generator G weights\n",
        "    self.W1_genG = tf.Variable(std * np.random.randn(3,3,self.n_channel,8), shape=[3,3,self.n_channel,8], trainable=True, name=\"G_filter_1\")\n",
        "    self.b1_genG = tf.Variable(np.zeros((8)), shape=(8), trainable=True, name=\"G_bias_1\")\n",
        "    self.W2_genG = tf.Variable(std * np.random.randn(3,3,8,4), shape=[3,3,8,4])\n",
        "    self.b2_genG = tf.Variable(np.zeros((4)), shape=(4))\n",
        "    self.W3_genG = tf.Variable(std * np.random.randn(3,3,4,self.n_channel), shape=[3,3,4,self.n_channel])\n",
        "    self.b3_genG = tf.Variable(np.zeros((self.n_channel)), shape=(self.n_channel))\n",
        "    # Generator F weights\n",
        "    self.W1_genF = tf.Variable(std * np.random.randn(3,3,self.n_channel,8), shape=[3,3,self.n_channel,8])\n",
        "    self.b1_genF = tf.Variable(np.zeros((8)), shape=(8))\n",
        "    self.W2_genF = tf.Variable(std * np.random.randn(3,3,8,4), shape=[3,3,8,4])\n",
        "    self.b2_genF = tf.Variable(np.zeros((4)), shape=(4))\n",
        "    self.W3_genF = tf.Variable(std * np.random.randn(3,3,4,self.n_channel), shape=[3,3,4,self.n_channel])\n",
        "    self.b3_genF = tf.Variable(np.zeros((self.n_channel)), shape=(self.n_channel))\n",
        "    # Discriminator DX weights\n",
        "    self.W1_disDX = tf.Variable(std * np.random.randn(3,3,self.n_channel,8), shape=[3,3,self.n_channel,8])\n",
        "    self.b1_disDX = tf.Variable(np.zeros((8)), shape=(8))\n",
        "    self.W2_disDX = tf.Variable(std * np.random.randn(3,3,8,4), shape=[3,3,8,4])\n",
        "    self.b2_disDX = tf.Variable(np.zeros((4)), shape=(4))\n",
        "    self.W3_disDX = tf.Variable(std * np.random.randn(3,3,4,1), shape=[3,3,4,1])\n",
        "    self.b3_disDX = tf.Variable(np.zeros((1)), shape=(1))\n",
        "    self.W4_disDX = tf.Variable(std * np.random.randn(1, 784), shape=[1, 784])\n",
        "    self.b4_disDX = tf.Variable(np.zeros((1)), shape=(1))\n",
        "    # Discriminator DY weights\n",
        "    self.W1_disDY = tf.Variable(std * np.random.randn(3,3,self.n_channel,8), shape=[3,3,self.n_channel,8])\n",
        "    self.b1_disDY = tf.Variable(np.zeros((8)), shape=(8))\n",
        "    self.W2_disDY = tf.Variable(std * np.random.randn(3,3,8,4), shape=[3,3,8,4])\n",
        "    self.b2_disDY = tf.Variable(np.zeros((4)), shape=(4))\n",
        "    self.W3_disDY = tf.Variable(std * np.random.randn(3,3,4,1), shape=[3,3,4,1])\n",
        "    self.b3_disDY = tf.Variable(np.zeros((1)), shape=(1))\n",
        "    self.W4_disDY = tf.Variable(std * np.random.randn(1, 784), shape=[1, 784])\n",
        "    self.b4_disDY = tf.Variable(np.zeros((1)), shape=(1))\n",
        "\n",
        "  def generator(self, mode, input_gen):  \n",
        "    \"\"\" Both GANs will have the same architecture and therefore it is only described once.\n",
        "        Generator block for the GAN of architecture : \n",
        "\n",
        "        - Convolution Layer, 8 filters (3, 3, n_channels), stride = 1, padding same\n",
        "        - Add bias\n",
        "        - Leaky Relu non linearity\n",
        "        - Convolution Layer, 4 filters (3, 3, 8), stride = 1, padding same  \n",
        "        - Add bias\n",
        "        - Leaky Relu non linearity\n",
        "        - Convolution Layer, n_channels filters (3, 3, 4), stride = 1, padding same \n",
        "        - Add bias\n",
        "        - tanh non linearity\n",
        "        - Linear transform to fall within image range\n",
        "\n",
        "        ------------\n",
        "        Params : \n",
        "        input_gen : tensor of shape (n, 28, 28, n_channels)\n",
        "        mode : \"XtoY\" for the generator translating X to Y, \"YtoX\" for the generator translating Y to X\n",
        "        ------------\n",
        "        Returns :\n",
        "        tensor of shape (n, 28, 28, n_channels)\n",
        "    \"\"\" \n",
        "    n = input_gen.shape[0]\n",
        "    if mode == \"XtoY\":\n",
        "      G1 = tf.nn.conv2d(input_gen, self.W1_genG, strides=[1,1,1,1], padding='SAME')\n",
        "      G1 = tf.nn.bias_add(G1, self.b1_genG)\n",
        "      A1 = tf.nn.leaky_relu(G1)\n",
        "      G2 = tf.nn.conv2d(A1, self.W2_genG, strides=[1,1,1,1], padding='SAME')\n",
        "      G3 = tf.nn.bias_add(G2, self.b2_genG)\n",
        "      A2 = tf.nn.leaky_relu(G3)\n",
        "      G4 = tf.nn.conv2d(A2, self.W3_genG, strides=[1,1,1,1], padding='SAME')\n",
        "      G5 = tf.nn.bias_add(G4, self.b3_genG)\n",
        "      gen = 0.5*tf.nn.tanh(G5) + 0.5 #tanh activitation + simple linear transform to fall back within image domain\n",
        "\n",
        "    elif mode ==\"YtoX\":\n",
        "      F1 = tf.nn.conv2d(input_gen, self.W1_genF, strides=[1,1,1,1], padding='SAME')\n",
        "      F1 = tf.nn.bias_add(F1, self.b1_genF)\n",
        "      A1 = tf.nn.leaky_relu(F1)\n",
        "      F2 = tf.nn.conv2d(F1, self.W2_genF, strides=[1,1,1,1], padding='SAME')\n",
        "      F3 = tf.nn.bias_add(F2, self.b2_genF)\n",
        "      A2 = tf.nn.leaky_relu(F3)\n",
        "      F4 = tf.nn.conv2d(A2, self.W3_genF, strides=[1,1,1,1], padding='SAME')\n",
        "      F5 = tf.nn.bias_add(F4, self.b3_genF)\n",
        "      gen = 0.5*tf.nn.tanh(F5) + 0.5 #tanh activitation + simple linear transform to fall back within image domain\n",
        "\n",
        "    return gen\n",
        "\n",
        "  def discriminator(self, mode, input_dis):\n",
        "    \"\"\" Both GANs will have the same architecture and therefore it is only described once.\n",
        "        Discriminator block for the GAN of architecture : \n",
        "\n",
        "        - Convolution Layer, 8 filters (3, 3, n_channels), stride = 1, padding = same\n",
        "        - Add bias\n",
        "        - Relu non linearity\n",
        "        - Convolution Layer, 4 filters (3, 3, 8), stride = 1, padding = same\n",
        "        - Add bias\n",
        "        - Relu non linearity\n",
        "        - Convolution Layer, 1 filters (3, 3, 4), stride = 1, padding = same  \n",
        "        - Add bias\n",
        "        - Relu non linearity\n",
        "        - Reshaping \n",
        "        - L2 normalize\n",
        "        - Fully Connected\n",
        "        - Sigmoid non linearity\n",
        "\n",
        "        ------------\n",
        "        Params : \n",
        "        input_dis : tensor of shape (n, 28, 28, n_channels)\n",
        "        mode : \"DX\" for the X discriminator, \"DY\" for the Y discriminator\n",
        "        ------------\n",
        "        Returns :\n",
        "        tensor of shape (n, 1)\n",
        "    \"\"\"\n",
        "    if mode == \"DX\":\n",
        "      DX1 = tf.nn.conv2d(input_dis, self.W1_disDX, strides=[1,1,1,1], padding='SAME')\n",
        "      DX1 = tf.nn.bias_add(DX1, self.b1_disDX)\n",
        "      A1 = tf.nn.relu(DX1)\n",
        "      DX2 = tf.nn.conv2d(A1, self.W2_disDX, strides=[1,1,1,1], padding='SAME')\n",
        "      DX3 = tf.nn.bias_add(DX2, self.b2_disDX)\n",
        "      A2 = tf.nn.relu(DX3)\n",
        "      DX4 = tf.nn.conv2d(A2, self.W3_disDX, strides=[1,1,1,1], padding='SAME')\n",
        "      DX5 = tf.nn.bias_add(DX4, self.b3_disDX)\n",
        "      A3 = tf.nn.relu(DX5)\n",
        "      sh = A3.get_shape().as_list()\n",
        "      A4 = tf.reshape(A3, shape=(sh[0], np.product(sh[1:]))) #flattening\n",
        "      A5 = tf.math.l2_normalize(A4)\n",
        "      DX6 = tf.linalg.matvec(self.W4_disDX, A5) + self.b4_disDX\n",
        "      dis = tf.nn.sigmoid(DX6)\n",
        "      \n",
        "    elif mode == \"DY\":\n",
        "      DY1 = tf.nn.conv2d(input_dis, self.W1_disDY, strides=[1,1,1,1], padding='SAME')\n",
        "      DY1 = tf.nn.bias_add(DY1, self.b1_disDY)\n",
        "      A1 = tf.nn.relu(DY1)\n",
        "      DY2 = tf.nn.conv2d(A1, self.W2_disDY, strides=[1,1,1,1], padding='SAME')\n",
        "      DY3 = tf.nn.bias_add(DY2, self.b2_disDY)\n",
        "      A2 = tf.nn.relu(DY3)\n",
        "      DY4 = tf.nn.conv2d(A2, self.W3_disDY, strides=[1,1,1,1], padding='SAME')\n",
        "      DY5 = tf.nn.bias_add(DY4, self.b3_disDY)\n",
        "      A3 = tf.nn.relu(DY5)\n",
        "      sh = A3.get_shape().as_list()\n",
        "      A4 = tf.reshape(A3, shape=(sh[0], np.product(sh[1:]))) #flattening\n",
        "      A5 = tf.math.l2_normalize(A4)\n",
        "      DY6 = tf.linalg.matvec(self.W4_disDY, A5) + self.b4_disDY\n",
        "      dis = tf.nn.sigmoid(DY6)\n",
        "      \n",
        "    return dis\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6tvGvPUHfbMb",
        "colab_type": "text"
      },
      "source": [
        "# Optimization process\n",
        "\n",
        "After defining the model, we use our data to optimize the weights of the neural network during the training process. To do that, each component of the cycle gan will have a loss function to minimize.  By using the same previous notations we define the loss function for each generator and discriminator:\n",
        "$$L_{G} = \\mathbb{E}_{x∼p_{data}(x)}[(DY(G(x))-1)^2] + \\lambda L_{cycle}$$ \\\\\n",
        "$$L_{F} = \\mathbb{E}_{y∼p_{data}(y)}[(DX(F(y))-1)^2] + \\lambda L_{cycle}$$ \\\\\n",
        "\n",
        "Where $L_{cycle} = \\mathbb{E}_{x∼p_{data}(x)}[\\parallel F(G(x)) - x \\parallel_1] + \\mathbb{E}_{y∼p_{data}(y)}[\\parallel G(F(y)) - y \\parallel_1]$ \\\\\n",
        "\\\\\n",
        "$$L_{DX} = \\mathbb{E}_{x∼p_{data}(x)}[(DX(x) - 1)^2] + \\mathbb{E}_{y∼p_{data}(y)}[DX(F(y))^2]$$ \\\\\n",
        "$$L_{DY} = \\mathbb{E}_{y∼p_{data}(y)}[(DY(y) - 1)^2] + \\mathbb{E}_{x∼p_{data}(x)}[DY(G(x))^2]$$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J6Qc6fsvsVRF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def sample(data, n):\n",
        "  return data[np.random.choice(range(len(data)), n), :, :, np.newaxis]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HDBJ-Em949cG",
        "colab_type": "text"
      },
      "source": [
        "We now write a function that will perfom cycle GAN training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WkdQBly3a_Av",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_cyclegan(model, X_data, Y_data, opt, n_iter, plot_every, batch_size, lbda):\n",
        "  \"\"\"Trains a GAN object containing two generators and two critics using the cycle gan procedure. \n",
        "     Learns image to image translation between X_data and Y_data\n",
        "     -------------\n",
        "     Params : \n",
        "        model : Cycle_GAN object\n",
        "        X_data : array of shape (n_sample, height, width, channels)\n",
        "        Y_data : array of shape (n_sample, height, width, channels)\n",
        "        opt : tensorflow optimizer like object \n",
        "        n_iter : int, number of iteration\n",
        "        plot_every : int, plot of sample translation every plot_every iterations\n",
        "        batch_size : int\n",
        "        lbda : float positive, controls the importance of the cycle loss\n",
        "     -------------\n",
        "     Returns :\n",
        "      None\n",
        "      ------------\n",
        "      model object is modified in place\n",
        "  \"\"\"\n",
        "  for t in range(n_iter):\n",
        "    #sampling data\n",
        "    x = sample(X_data, batch_size)\n",
        "    y = sample(Y_data, batch_size)\n",
        "    with tf.device('/device:GPU:0'): \n",
        "        with tf.GradientTape(persistent=True) as tape:\n",
        "          genY = model.generator(\"XtoY\", x)\n",
        "          genX = model.generator(\"YtoX\", y)\n",
        "          disX_target = model.discriminator(\"DX\", x) #critic on real X\n",
        "          disY_target = model.discriminator(\"DY\", y) #critic on real Y\n",
        "          disX_gen = model.discriminator(\"DX\", genX) #critic on fake X\n",
        "          disY_gen = model.discriminator(\"DY\", genY) #critic on fake Y\n",
        "          f_cycle = model.generator(\"YtoX\", genY) #forward cycle\n",
        "          b_cycle = model.generator(\"XtoY\", genX) #backward cycle\n",
        "          #------------------------------------------------------\n",
        "          # Computing losses for the cycle gan loss - note that we are using the trick of the least square losses instead of the negative log likelihood\n",
        "          #------------------------------------------------------\n",
        "          loss_cycle = lbda * tf.reduce_mean(tf.math.add(tf.norm(tf.math.subtract(f_cycle, x),ord=1, axis=[-3,-2]), tf.norm(tf.math.subtract(b_cycle, y), ord=1, axis=[-3,-2])), axis=0)\n",
        "          # Generator losses\n",
        "          loss_genXtoY = tf.reduce_mean(tf.math.add(tf.math.square(tf.math.subtract(disY_gen, 1)), loss_cycle), axis=0)\n",
        "          loss_genYtoX = tf.reduce_mean(tf.math.add(tf.math.square(tf.math.subtract(disX_gen, 1)), loss_cycle), axis=0)\n",
        "          # Discriminator losses\n",
        "          loss_disDX = tf.reduce_mean(tf.math.add(tf.math.square(tf.math.subtract(disX_target, 1)), tf.math.square(disX_gen)), axis=0)\n",
        "          loss_disDY = tf.reduce_mean(tf.math.add(tf.math.square(tf.math.subtract(disY_target, 1)), tf.math.square(disY_gen)), axis=0)\n",
        "\n",
        "        #variable for each block to optimize\n",
        "        var_genX = [model.W1_genF, model.b1_genF, model.W2_genF, model.b2_genF, model.W3_genF, model.b3_genF]\n",
        "        var_genY = [model.W1_genG, model.b1_genG, model.W2_genG, model.b2_genG, model.W3_genG, model.b3_genG]\n",
        "        var_disX = [model.W1_disDX, model.b1_disDX, model.W2_disDX, model.b2_disDX, model.W3_disDX, model.b3_disDX, model.W4_disDX, model.b4_disDX]\n",
        "        var_disY = [model.W1_disDY, model.b1_disDY, model.W2_disDY, model.b2_disDY, model.W3_disDY, model.b3_disDY, model.W4_disDY, model.b4_disDY]\n",
        "\n",
        "        #computing grandients\n",
        "        gradients_genXtoY = tape.gradient(loss_genXtoY, var_genY)\n",
        "        gradients_genYtoX = tape.gradient(loss_genYtoX, var_genX)\n",
        "        gradients_disX = tape.gradient(loss_disDX, var_disX)\n",
        "        gradients_disY = tape.gradient(loss_disDY, var_disY)\n",
        "\n",
        "\n",
        "        #applying gradients\n",
        "        opt.apply_gradients(zip(gradients_genXtoY, var_genY))\n",
        "        opt.apply_gradients(zip(gradients_genYtoX, var_genX))\n",
        "        opt.apply_gradients(zip(gradients_disX, var_disX))\n",
        "        opt.apply_gradients(zip(gradients_disY, var_disY))\n",
        "\n",
        "    if (t+1)%plot_every == 500:\n",
        "      print(\"Iteration\", t)\n",
        "      print(\"_____________________\")\n",
        "      print(\"Sample created image from mnist to usps : \")\n",
        "      xp = sample(X_data, 1)\n",
        "      genY_sample = model.generator(\"XtoY\", xp)\n",
        "      xu = sample(Y_data, 1)\n",
        "      genX_sample = model.generator(\"YtoX\", xu)\n",
        "      f, axarr = plt.subplots(1,2)\n",
        "      axarr[0].imshow(xp[0,:,:,0])\n",
        "      axarr[1].imshow(genY_sample[0,:,:,0])\n",
        "      axarr[0].set_title(\"MNIST sample\")\n",
        "      axarr[1].set_title(\"Transalated towards USPS\")\n",
        "      plt.show()\n",
        "      f2, axarr2 = plt.subplots(1,2)\n",
        "      axarr2[0].imshow(xu[0,:,:,0])\n",
        "      axarr2[1].imshow(genX_sample[0,:,:,0])\n",
        "      axarr2[0].set_title(\"USPS sample\")\n",
        "      axarr2[1].set_title(\"Transalated towards MNIST\")\n",
        "      plt.show()\n",
        "      print(\"=====================\")\n",
        "      print(\"=====================\")\n",
        "\n",
        "  \n",
        "  return None"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PA0yH90-reRb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cgan = Cycle_GAN(std=1)\n",
        "n_samples = 20000\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002)\n",
        "n_iter = 15000\n",
        "batch_size = 200\n",
        "epochs = (n_iter * batch_size )/(n_samples)\n",
        "lbda = 10 #the factor lambda for which multiplies the cycle loss, we choose 10 as is done in the paper\n",
        "plot_every = 500 #plot sample of generated image every plot_every iteration\n",
        "#with n_iter = 10000 and batch_size of 200, we will go over 150 epochs"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s7MhaRmBSAlv",
        "colab_type": "text"
      },
      "source": [
        "Each 500 iterations we sample images and their translation and display the results to that we can monitor and visualize the training and its evolution."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XWrFK_R7reWZ",
        "colab_type": "code",
        "outputId": "be5adf96-9964-4e61-e50a-34d419be76f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "train_cyclegan(cgan, mnist, usps, optimizer, n_iter, plot_every, batch_size, lbda)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iteration 499\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wTtOntj1+/PiBqKPZRxrqmUfEG+n/XOA2YMeSMpMjossDSNZJ+tq2R48e3e4qmnXT7565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\nA/8fCCZXbfuMolkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jYn5E9EdEf19f3wRXZ9YxE8rtjkVnVoeJ3lLgauBY4PT8+9sti8haat26dd0Oodc4t3vE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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 999\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TJ02a1Nb9umVuZlYAB3MzswI4mJuZFcDB3MysAA7mZmYF8GiWFvnoRz9al/aBD3ygMm/V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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 1499\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 1999\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 2499\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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27lN2DHBjRGwL3JiHbQjWW2+9fq/x48dXfY0ZM6bfa/To0VVftlrTcWxbIQZM5hFxC/Bs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\nczOzAjiZm5kVwMnczKwA/w9yU/lNWZ9BSAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vG8d2m61atWqDMp8k7axGLne8DLgDeI2kJZI+TAr6d0l6EDgwT5v1FMe2lWrEHntEHD3E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\nzcwK48RuZlYYJ3Yzs8L8P0m+N0KUcpQZAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 2999\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Z6t+qDZB0uKQ+SX39/f3Nbs6sY0YS2x2sltmwRpvYF0raFCD/XzTYghExIyJ6I6K3p6dn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\nZmaF+X+UjmIaDFJ9MgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 3499\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 3999\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 4499\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 4999\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 5499\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 5999\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Lbrvt1szqDIqVK1eWxk8//fSqWK2zb8rOUNl3331Ly2677bZVsddee623KlqT9Ofsjlpl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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 6499\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zs8I4sZuZFcaJ3cysME7sZmaF8clTG9TUqVOb3sYtt9zSgpqYjVyzJ0pXrBjwlkEbJPfY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\nfNs6ZN4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 6999\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 7499\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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s5ZzbVpLBjpmPioj5+fECYFST4jEbas5t60gNnwCNiACi1vOSjpI0Q5K/vWIdZSC53dPT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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Qx1WS5kvqk9TX39/f7NOZdcxoYruD1TIb0VgT+2pJ0wDy/zVDLRgRSyKiNyJ6e3p6xvh0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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 7999\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WlZo+Bop0t0vNO14kHSNptqTZzS7LrJMGE9u9vb0drJlZf40m80WStgTI/xfXGjEipkZE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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 8499\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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148ntLoZlNqaJFvbVkmYA5P9rGs0YEQsjYjAiBgcGBib4dGZdM6Hc7lp0Zk2Y6CUFrgbm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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 8999\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Pbn3xRp96l93Xu+y9llpnxNQa14ozUdp1m4AFCxa0ZbkdNg/HthVi0Jp5RFwHPN5v9AHA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\nP2ejjkpQY7CrAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 9499\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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D9TGbbo5t63ttD55GOnZqePwk6SxJw5KGR0ZG2n05s55pJbZ7WC2zSU01se+SdARA/r+7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\njBO7mVlhnNjNzArjxG5mVpj/Bwn9ISgcJOfJAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 9999\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 10499\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xIhZHRH9E9Pf19Y1xdWZdM6bY7lrtzJow1ksKXA4cC5ye/3+7bTWapOpdJqDeQVKAo48+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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 10999\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6PqC1/4Uzmzwc29bzWj55Guk4adhjJUnHShqQNDA4ONjq6sy6Ziyx3cVqmY1qvIl9jaTp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\nglcG3Xm9QAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 11499\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 11999\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 12499\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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poaSFfX19zT6dWccMJ7c7GJbZkEZa2JdJ2gYg/18+0IwRMTMieiOit6enZ4RPZ9YxI8rt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\nu5lZYVzYzcwK48JuZlYYF3Yzs8L8P5H8hA9fOJL4AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 12999\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2c2ZtM5zYbmO1zIY00sS+VNImAPn/soFmjIiZEdEdEd1dXV0j3JxZ24wotttWO7MGjPSW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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 13499\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 13999\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 14499\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AvHnzqsoWL15cVbbbbrvVnH/mzJktb5O9xbFtXamRSxOvAO4E3ivpGUlHkAJ9L0mPAnvm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\nFeD/AThI9WJpSeClAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yk08+uXbe888/v611sraasbE9ERO5b75vP9B9zVzuuBK4AfgdSRslnUQK+j+WdDdwZH5t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\nQk1CXnwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n",
            "Iteration 14999\n",
            "_____________________\n",
            "Sample created image from mnist to usps : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=====================\n",
            "=====================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bqGmpPagTJMf",
        "colab_type": "text"
      },
      "source": [
        "# Results \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5qafd6CUXA4M",
        "colab_type": "text"
      },
      "source": [
        "After having trained the model for 150 epochs, the generators are able to translate the digits as below:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AaumzjDUlU6T",
        "colab_type": "text"
      },
      "source": [
        "We will test the translation on the a sample of the training data. Then to evaluate it on unseen data, we drew ourselves some numbers that match the mnist and usps data format to evaluate the translation on new data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_aKiGG6qiCKk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#This is the data we drew ourselves\n",
        "usps_test = np.load(\"../uspstest.npy\")\n",
        "mnist_test = np.load(\"../mnisttest.npy\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ul7OXo5TlJTa",
        "colab_type": "text"
      },
      "source": [
        "### First vizualize the results on the training set\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rx7aB1PTTVFF",
        "colab_type": "code",
        "outputId": "e010cd33-da1f-4941-908c-18161c8e2291",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "xp = sample(mnist, 5)\n",
        "genY_sample = cgan.generator(\"XtoY\", xp)\n",
        "xu = sample(usps, 5)\n",
        "genX_sample = cgan.generator(\"YtoX\", xu)\n",
        "f, axarr = plt.subplots(5,4, figsize=(18,25))\n",
        "\n",
        "for i in range(5):\n",
        "  axarr[i,0].imshow(xp[i,:,:,0])\n",
        "  axarr[i,1].imshow(genY_sample[i,:,:,0])\n",
        "  axarr[i,0].set_title(\"MNIST sample\")\n",
        "  axarr[i,1].set_title(\"Transalated towards USPS\")\n",
        "\n",
        "for j in range(5):\n",
        "  axarr[j,2].imshow(xu[j,:,:,0])\n",
        "  axarr[j,3].imshow(genX_sample[j,:,:,0])\n",
        "  axarr[j,2].set_title(\"USPS sample\")\n",
        "  axarr[j,3].set_title(\"Transalated towards MNIST\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3VR3cWruutfaxJY/zmtbata21G5Kc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            "text/plain": [
              "<Figure size 1296x1800 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "efkZQBcCltEb",
        "colab_type": "text"
      },
      "source": [
        "#### Now, on our own data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r1DXk3CW4yve",
        "colab_type": "code",
        "outputId": "99ad0455-3329-4647-eccc-265cd13536ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "xp = mnist_test[:,:,:,np.newaxis]\n",
        "genY_sample = cgan.generator(\"XtoY\", xp)\n",
        "xu = usps_test[:,:,:,np.newaxis]\n",
        "genX_sample = cgan.generator(\"YtoX\", xu)\n",
        "f, axarr = plt.subplots(5,4, figsize=(18,25))\n",
        "\n",
        "for i in range(5):\n",
        "  axarr[i,0].imshow(xp[i,:,:,0])\n",
        "  axarr[i,1].imshow(genY_sample[i,:,:,0])\n",
        "  axarr[i,0].set_title(\"MNIST sample\")\n",
        "  axarr[i,1].set_title(\"Transalated towards USPS\")\n",
        "\n",
        "for j in range(5):\n",
        "  axarr[j,2].imshow(xu[j,:,:,0])\n",
        "  axarr[j,3].imshow(genX_sample[j,:,:,0])\n",
        "  axarr[j,2].set_title(\"USPS sample\")\n",
        "  axarr[j,3].set_title(\"Transalated towards MNIST\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RSf6htfbZJGmtXddaO6e1dvP4jK21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\nN/bVr351cNkPfvCDg/EvfvGLg/Glct933317Y/vtt9/gsgcccMBg/Md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4PMfHtm/ndm/jIzP9FFDzWZ+Whm/iQzH8rMpR70c3dm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\nAACAqv8DlcMZWJba05AAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1296x1800 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9LKAP3w5oDE8",
        "colab_type": "text"
      },
      "source": [
        "#### Random shape of data\n",
        "\n",
        "We drew random shapes of data to see if the model is still able to translate images in the same way without having trained on it. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vzsWRl6eoDUS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#This is the data we drew ourselves\n",
        "u_rand= np.load(\"...\")\n",
        "m_rand = np.load(\"...\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QMhTakCipUkB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1kKuQNADciZn",
        "colab_type": "code",
        "outputId": "a3492bcd-18f4-49b9-f27a-9dd7f8454eab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "xp = m_rand[:,:,:,np.newaxis]\n",
        "genY_sample = cgan.generator(\"XtoY\", xp)\n",
        "xu = u_rand[:,:,:,np.newaxis]\n",
        "genX_sample = cgan.generator(\"YtoX\", xu)\n",
        "f, axarr = plt.subplots(5,4, figsize=(18,25))\n",
        "\n",
        "for i in range(5):\n",
        "  axarr[i,0].imshow(xp[i,:,:,0])\n",
        "  axarr[i,1].imshow(genY_sample[i,:,:,0])\n",
        "  axarr[i,0].set_title(\"Random sample\")\n",
        "  axarr[i,1].set_title(\"Transalated towards USPS\")\n",
        "\n",
        "for j in range(5):\n",
        "  axarr[j,2].imshow(xu[j,:,:,0])\n",
        "  axarr[j,3].imshow(genX_sample[j,:,:,0])\n",
        "  axarr[j,2].set_title(\"Random sample\")\n",
        "  axarr[j,3].set_title(\"Transalated towards MNIST\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AAAAABmkcAAAAAIM0DgAAAIBBGgcAAADAII0DAAAAYNC2WSfAfHO+dZhPrbWZ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\nm34Mz0zyt0luTlfHfzvJoX3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AgzQOAAAAgEEaBwAAAMAgjQMAAABg0PBJbQEAFtANN9ww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\nD1X1lKraUVXOOYTNb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/L43iQ/3v94wBjpBWCaXgCm6QVYH4d28TVfSnIqyStVdXZy7OEk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\n",
            "text/plain": [
              "<Figure size 1296x1800 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bEqDDkYyb_XJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O6Ibng7kuF4X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b86SybPdwZhU",
        "colab_type": "text"
      },
      "source": [
        "### We tried it on random sample of faces\n",
        "\n",
        "We used the Faces in the Wild dataset and resized the images to be of size (28,28). We can see that, the translator roughly draw a sketch of a face, but the quality is quite poor as it could be expected. However, it is nice to observe these results, for some faces the sketch is quite good.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3_2j33FyuF7s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "male = np.load(\"../malefaces.npy\")\n",
        "female = np.load(\"../femalefaces.npy\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_CXWeMuVuTSN",
        "colab_type": "code",
        "outputId": "ff81ae09-0061-4ab1-8932-10605a0fa8d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 819
        }
      },
      "source": [
        "xp = male[np.random.choice(range(len(male)), 3),:,:,np.newaxis]\n",
        "genY_sample = cgan.generator(\"XtoY\", xp)\n",
        "f, axarr = plt.subplots(3,4, figsize=(18,14))\n",
        "xu = female[np.random.choice(range(len(female)), 3),:,:,np.newaxis]\n",
        "genX_sample = cgan.generator(\"XtoY\", xu)\n",
        "\n",
        "for i in range(3):\n",
        "  axarr[i,0].imshow(xp[i,:,:,0])\n",
        "  axarr[i,1].imshow(genY_sample[i,:,:,0])\n",
        "  axarr[i,0].set_title(\"Random Face\")\n",
        "  axarr[i,1].set_title(\"Transalated Face\")\n",
        "\n",
        "for j in range(3):\n",
        "  axarr[j,2].imshow(xu[j,:,:,0])\n",
        "  axarr[j,3].imshow(genX_sample[j,:,:,0])\n",
        "  axarr[j,2].set_title(\"Random Face\")\n",
        "  axarr[j,3].set_title(\"Transalated Face\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mCC\n",
            "text/plain": [
              "<Figure size 1296x1008 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HmIPVwNcuZU0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}