{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/tjwei/GAN_Tutorial/blob/master/CGAN_intro_for_MNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for tf 2.0\n",
    "#!pip install -U tensorflow-gpu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ckOHCuiArv5c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.0.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import PIL\n",
    "import time\n",
    "from skimage.io import imshow\n",
    "\n",
    "from IPython.display import display\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 70
    },
    "colab_type": "code",
    "id": "1V2PJHrDrxf2",
    "outputId": "9ea36b90-cb48-432a-9148-178734c9d61d"
   },
   "outputs": [],
   "source": [
    "(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "L0iNo21Arykj",
    "outputId": "d39284cb-6aa5-47e8-d93f-0bfb1a3f7d99"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(dtype('uint8'), (60000, 28, 28))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.dtype, train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 314
    },
    "colab_type": "code",
    "id": "uAcCsSinus1d",
    "outputId": "55d4702d-93f2-4136-80be-fe756cada4ca"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f53f5067160>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARsAAAEYCAYAAABsuVKPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAADwFJREFUeJzt3X+o1XWex/HXa63+yCyV2TVxap0iDIv2tpgtrWxF6/SDom5FjNDgUmR/JDgwyIb/TLEYsZWzSDFoZGPROA1Um8WyFWk5S4N0NSvTbYuwxstNKbPMfqH3vX/cr3BzvOdzuuec9zn33OcD5J7z/bzv97z5oi+/38/5nO9xRAgAWu2v2t0AgPGBsAGQgrABkIKwAZCCsAGQgrABkIKwAZCCsAGQgrABkOK4zBezzXJloPt8EhF/XSpq6MzG9hW237X9vu07G9kXgDHrw3qKRh02tidIekjSlZJmS1pge/Zo9weguzVyZjNX0vsR8UFEfCfp95KubU5bALpNI2EzQ9Kfhz3fXW37HtuLbPfZ7mvgtQCMcS2fII6I1ZJWS0wQA+NZI2c2/ZJOG/b8x9U2APgLjYTN65LOsv0T2ydI+pmk9c1pC0C3GfVlVEQcsr1Y0guSJkhaExHvNK0zAF3FmbcFZc4G6EpbImJOqYiPKwBIQdgASEHYAEhB2ABIQdgASEHYAEhB2ABIQdgASEHYAEhB2ABIQdgASEHYAEhB2ABIQdgASEHYAEhB2ABIQdgASEHYAEhB2ABIQdgASEHYAEhB2ABIQdgASEHYAEhB2ABIQdgASEHYAEhB2ABIQdgASEHYAEhB2ABIQdgASEHYAEhB2ABIcVy7G0BnmTBhQrHmlFNOSehkyOLFi2uOn3jiicV9zJo1q1hzxx13FGvuv//+muMLFiwo7uObb74p1tx7773FmrvvvrtY02kaChvbuyQdkHRY0qGImNOMpgB0n2ac2VwaEZ80YT8AuhhzNgBSNBo2IelF21tsLzpWge1Ftvts9zX4WgDGsEYvo+ZFRL/tv5H0ku3/jYhNwwsiYrWk1ZJkOxp8PQBjVENnNhHRX/3cK+kZSXOb0RSA7jPqsLE90fakI48l/VTS9mY1BqC7NHIZNU3SM7aP7Od3EfHfTekKQNcZddhExAeS/q6JvYxbp59+erHmhBNOKNZcdNFFxZp58+bVHJ88eXJxHzfccEOxppPs3r27WLNy5cpiTW9vb83xAwcOFPfx5ptvFmteffXVYs1YxFvfAFIQNgBSEDYAUhA2AFIQNgBSEDYAUhA2AFI4Iu/jSuPxs1E9PT3Fmg0bNhRrMm9YNZYMDg4Wa2655ZZizZdfftlwLwMDA8Wazz77rFjz7rvvNtxLsi313MuKMxsAKQgbACkIGwApCBsAKQgbACkIGwApCBsAKQgbACn4RswW++ijj4o1n376abFmrC3q27x5c7Fm//79xZpLL7205vh3331X3Mfjjz9erEHrcWYDIAVhAyAFYQMgBWEDIAVhAyAFYQMgBWEDIAVhAyAFi/pabN++fcWapUuXFmuuvvrqYs0bb7xRrKnnmx9Ltm3bVqyZP39+sebgwYPFmnPOOafm+JIlS4r7QGfgzAZACsIGQArCBkAKwgZACsIGQArCBkAKwgZACsIGQAq+fneMOPnkk4s1Bw4cKNasWrWq5vitt95a3MfNN99crFm3bl2xBl2jOV+/a3uN7b22tw/bNtX2S7bfq35OabRbAN2tnsuo30q64qhtd0p6OSLOkvRy9RwARlQMm4jYJOnoD/hcK2lt9XitpOua3BeALjPaD2JOi4iB6vHHkqaNVGh7kaRFo3wdAF2i4U99R0TUmviNiNWSVktMEAPj2Wjf+t5je7okVT/3Nq8lAN1otGGzXtLC6vFCSc82px0A3aqet77XSfqTpFm2d9u+VdK9kubbfk/SP1fPAWBExTmbiFgwwtBlTe4FNXzxxRdN2c/nn3/e8D5uu+22Ys2TTz5ZrBkcHGy4F4wdfFwBQArCBkAKwgZACsIGQArCBkAKwgZACsIGQApunjXOTJw4seb4c889V9zHxRdfXKy58sorizUvvvhisQZjQnNungUAzUDYAEhB2ABIQdgASEHYAEhB2ABIQdgASEHYAEjBoj58z5lnnlms2bp1a7Fm//79xZqNGzcWa/r6+mqOP/TQQ8V9ZP4dH6dY1AegcxA2AFIQNgBSEDYAUhA2AFIQNgBSEDYAUhA2AFKwqA8/WG9vb7Hm0UcfLdZMmjSp4V6WLVtWrHnssceKNQMDAw33Mo6xqA9A5yBsAKQgbACkIGwApCBsAKQgbACkIGwApCBsAKRgUR9a4txzzy3WrFixolhz2WWXNdzLqlWrijXLly8v1vT39zfcS5dqzqI+22ts77W9fdi2u2z3295W/bmq0W4BdLd6LqN+K+mKY2z/dUT0VH/+q7ltAeg2xbCJiE2S9iX0AqCLNTJBvNj2W9Vl1pSRimwvst1nu/Zt8gF0tdGGzW8knSmpR9KApAdGKoyI1RExp54JJADda1RhExF7IuJwRAxKeljS3Oa2BaDbjCpsbE8f9rRX0vaRagFAko4rFdheJ+kSST+yvVvSryRdYrtHUkjaJen2FvYIoAuwqA9tM3ny5GLNNddcU3O8njsC2i7WbNiwoVgzf/78Ys04xZ36AHQOwgZACsIGQArCBkAKwgZACsIGQArCBkAK1tlgTPv222+LNccdV1y7qkOHDhVrLr/88prjr7zySnEfXYp1NgA6B2EDIAVhAyAFYQMgBWEDIAVhAyAFYQMgBWEDIEV5tRMwCuedd16x5sYbbyzWXHDBBTXH61mwV48dO3YUazZt2tSU1xqvOLMBkIKwAZCCsAGQgrABkIKwAZCCsAGQgrABkIKwAZCCRX34nlmzZhVrFi9eXKy5/vrrizWnnnpqXT016vDhw8WagYGBYs3g4GAz2hm3OLMBkIKwAZCCsAGQgrABkIKwAZCCsAGQgrABkIKwAZCCRX1dpJ5FcgsWLKg5Xs+CvZkzZ9bbUsv19fUVa5YvX16sWb9+fTPaQQ3FMxvbp9neaHuH7XdsL6m2T7X9ku33qp9TWt8ugLGqnsuoQ5J+GRGzJf2DpDtsz5Z0p6SXI+IsSS9XzwHgmIphExEDEbG1enxA0k5JMyRdK2ltVbZW0nWtahLA2PeD5mxsz5R0vqTNkqZFxJFPr30sadoIv7NI0qLRtwigG9T9bpTtkyQ9JekXEfHF8LGICElxrN+LiNURMSci5jTUKYAxra6wsX28hoLmiYh4utq8x/b0any6pL2taRFAN6jn3ShLekTSzohYMWxovaSF1eOFkp5tfnsAuoWHroBqFNjzJP1R0tuSjtw9aJmG5m3+IOl0SR9Kuiki9hX2VfvFxqlp04453fU9s2fPLtY8+OCDxZqzzz67rp4ybN68uVhz33331Rx/9tny/3Hc9KrlttQzTVKcII6I/5HkEYYv+6FdARif+LgCgBSEDYAUhA2AFIQNgBSEDYAUhA2AFIQNgBTcPKtBU6dOrTm+atWq4j56enqKNWeccUbdPbXaa6+9Vqx54IEHijUvvPBCsebrr7+uqyd0Ps5sAKQgbACkIGwApCBsAKQgbACkIGwApCBsAKQgbACkGLeL+i688MJizdKlS4s1c+fOrTk+Y8aMunvK8NVXX9UcX7lyZXEf99xzT7Hm4MGDdfeE8YEzGwApCBsAKQgbACkIGwApCBsAKQgbACkIGwApCBsAKcbtor7e3t6m1DTDjh07ijXPP/98sebQoUPFmtId9Pbv31/cBzAanNkASEHYAEhB2ABIQdgASEHYAEhB2ABIQdgASEHYAEjhiMh7MTvvxQBk2RIRc0pFxTMb26fZ3mh7h+13bC+ptt9lu9/2turPVc3oGkB3qufjCock/TIittqeJGmL7ZeqsV9HxP2taw9AtyiGTUQMSBqoHh+wvVNSZ93FG0DH+0ETxLZnSjpf0uZq02Lbb9leY3vKCL+zyHaf7b6GOgUwptU9QWz7JEmvSloeEU/bnibpE0kh6d8kTY+IWwr7YIIY6D7NmSCWJNvHS3pK0hMR8bQkRcSeiDgcEYOSHpZU+wuUAIxr9bwbZUmPSNoZESuGbZ8+rKxX0vbmtwegW9TzbtQ/Svq5pLdtb6u2LZO0wHaPhi6jdkm6vSUdAugKLOoD0KjmzdkAQKMIGwApCBsAKQgbACkIGwApCBsAKQgbACkIGwApCBsAKQgbACkIGwApCBsAKQgbACkIGwApCBsAKQgbACnquVNfM30i6cNhz39UbRsr6Le16Le1WtXv39ZTlHqnvr94cbuvnjt8dQr6bS36ba1298tlFIAUhA2AFO0Om9Vtfv0fin5bi35bq639tnXOBsD40e4zGwDjBGEDIEXbwsb2Fbbftf2+7Tvb1Ue9bO+y/bbtbbb72t3P0Wyvsb3X9vZh26bafsn2e9XPKe3scbgR+r3Ldn91jLfZvqqdPR5h+zTbG23vsP2O7SXV9o48vjX6bevxbcucje0Jkv5P0nxJuyW9LmlBROxIb6ZOtndJmhMRHbmIy/Y/SfpS0mMRcW617d8l7YuIe6tAnxIR/9rOPo8Yod+7JH0ZEfe3s7ejVd9rPz0ittqeJGmLpOsk/Ys68PjW6PcmtfH4tuvMZq6k9yPig4j4TtLvJV3bpl66QkRskrTvqM3XSlpbPV6rob9wHWGEfjtSRAxExNbq8QFJOyXNUIce3xr9tlW7wmaGpD8Pe75bHXAwCkLSi7a32F7U7mbqNC0iBqrHH0ua1s5m6rTY9lvVZVZHXJYMZ3umpPMlbdYYOL5H9Su18fgyQVy/eRHx95KulHRHdRkwZsTQ9XKnr3P4jaQzJfVIGpD0QHvb+T7bJ0l6StIvIuKL4WOdeHyP0W9bj2+7wqZf0mnDnv+42taxIqK/+rlX0jMauhTsdHuq6/cj1/F729xPTRGxJyIOR8SgpIfVQcfY9vEa+of7REQ8XW3u2ON7rH7bfXzbFTavSzrL9k9snyDpZ5LWt6mXItsTq4k22Z4o6aeSttf+rY6wXtLC6vFCSc+2sZeiI/9wK73qkGNs25IekbQzIlYMG+rI4ztSv+0+vm1bQVy97fYfkiZIWhMRy9vSSB1sn6Ghsxlp6LYcv+u0fm2vk3SJhm4jsEfSryT9p6Q/SDpdQ7f2uCkiOmJSdoR+L9HQKX5I2iXp9mFzIm1je56kP0p6W9JgtXmZhuZBOu741uh3gdp4fPm4AoAUTBADSEHYAEhB2ABIQdgASEHYAEhB2ABIQdgASPH/3kUcm3ELNOYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(train_images[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uSDHJmuwtxT4"
   },
   "outputs": [],
   "source": [
    "def img_to_float(img):\n",
    "    return (np.float32(img)[..., None]-127.5)/127.5\n",
    "def img_to_uint8(img):\n",
    "    return np.uint8(img*127.5+128).clip(0, 255)[...,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 314
    },
    "colab_type": "code",
    "id": "9yGVvMP9vUn0",
    "outputId": "62352be3-6919-4bd7-ceef-8c8cb9bceef5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f53e175e0b8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_img_f32 = img_to_float(train_images)\n",
    "imshow(img_to_uint8(train_img_f32[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cZ5RgpP64zKj"
   },
   "source": [
    "**Add labels to the dataset**\n",
    "```python\n",
    "train_dataset_y = tf.data.Dataset.from_tensor_slices(train_labels).map(lambda y: tf.one_hot(y, 10))\n",
    "train_dataset_x = tf.data.Dataset.from_tensor_slices(train_img_f32)\n",
    "train_dataset = tf.data.Dataset.zip((train_dataset_x, train_dataset_y)).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)\n",
    "```\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oSrY5b6ev0kO"
   },
   "outputs": [],
   "source": [
    "BUFFER_SIZE = train_img_f32.shape[0]\n",
    "BATCH_SIZE = 32\n",
    "train_dataset_y = tf.data.Dataset.from_tensor_slices(train_labels).map(lambda y: tf.one_hot(y, 10))\n",
    "train_dataset_x = tf.data.Dataset.from_tensor_slices(train_img_f32)\n",
    "train_dataset = tf.data.Dataset.zip((train_dataset_x, train_dataset_y)).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YOGpQzuR5LUb"
   },
   "source": [
    "**The generator network is modified to take two inputs, labels and noises.\n",
    "Two inputs are concat into one before sending to the original generator.**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZaJFsMs2v1R1"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Dense, BatchNormalization, LeakyReLU, Reshape, Conv2DTranspose, Input, concatenate\n",
    "from tensorflow.keras import Model\n",
    "latent_dim = 100\n",
    "\n",
    "_0 = Input((latent_dim,))\n",
    "_1 = Input((10,))\n",
    "_ = concatenate([_0, _1])\n",
    "_ = Dense(7*7*256, use_bias=False, input_shape=(latent_dim,))(_)\n",
    "_ = BatchNormalization()(_)\n",
    "_ = LeakyReLU()(_)\n",
    "_ = Reshape((7, 7, 256))(_)\n",
    "_ = Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False)(_)\n",
    "_ = BatchNormalization()(_)\n",
    "_ = LeakyReLU()(_)\n",
    "_ = Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False)(_)\n",
    "_ = BatchNormalization()(_)\n",
    "_ = LeakyReLU()(_)\n",
    "_ = Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', activation='tanh')(_)\n",
    "generator = Model(inputs=[_0,_1], outputs=_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "T52zvuDP5rO8"
   },
   "source": [
    "**The discriminator also take two inputs, labels and images. The labels are concat to the flatten layer in later stage.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kqdl08rxvWiS"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Conv2D, Dropout, Flatten\n",
    "_0 = Input((28,28, 1))\n",
    "_1 = Input((10,))\n",
    "_ = Conv2D(64, (5, 5), strides=(2, 2), padding='same')(_0)\n",
    "_ = LeakyReLU()(_)\n",
    "_ = Dropout(0.3)(_)\n",
    "_ = Conv2D(128, (5, 5), strides=(2, 2), padding='same')(_)\n",
    "_ = LeakyReLU()(_)\n",
    "_ = Dropout(0.3)(_)\n",
    "_ = Flatten()(_)\n",
    "_ = concatenate([_, _1])\n",
    "_ = Dense(50)(_)\n",
    "_ = BatchNormalization()(_)\n",
    "_ = LeakyReLU()(_)\n",
    "_ = Dense(1)(_)\n",
    "discriminator = Model(inputs=[_0,_1], outputs=_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nd8zcnhjzKWq"
   },
   "outputs": [],
   "source": [
    "loss_fn = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
    "def generator_loss(generated_output):\n",
    "    return loss_fn(tf.ones_like(generated_output), generated_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fgWHoC6FzbUA"
   },
   "outputs": [],
   "source": [
    "def discriminator_loss(real_output, generated_output):\n",
    "    # [1,1,...,1] with real output since it is true and we want our generated examples to look like it\n",
    "    real_loss = loss_fn(tf.ones_like(real_output), real_output)\n",
    "\n",
    "    # [0,0,...,0] with generated images since they are fake\n",
    "    generated_loss = loss_fn(tf.zeros_like(generated_output), generated_output)\n",
    "\n",
    "    total_loss = real_loss + generated_loss\n",
    "\n",
    "    return total_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KCYwQqxgz8o_"
   },
   "outputs": [],
   "source": [
    "generator_optimizer = tf.keras.optimizers.Adam(1e-4)\n",
    "discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Rb2-KU6K1J0I"
   },
   "outputs": [],
   "source": [
    "EPOCHS = 50\n",
    "num_examples_to_generate = 20\n",
    "\n",
    "# We'll re-use this random vector used to seed the generator so\n",
    "# it will be easier to see the improvement over time.\n",
    "random_vector_for_generation = tf.random.normal([num_examples_to_generate,\n",
    "                                                 latent_dim])\n",
    "condition_vector_generation = tf.one_hot(list(range(10))+list(range(10)), 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Xnu88uGR1etc"
   },
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def train_step(images, labels):\n",
    "    # generating noise from a normal distribution\n",
    "    noise = tf.random.normal([BATCH_SIZE, latent_dim])  \n",
    "    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n",
    "        generated_images = generator([noise, labels], training=True)\n",
    "      \n",
    "        real_output = discriminator([images, labels], training=True)\n",
    "        generated_output = discriminator([generated_images, labels], training=True)\n",
    "         \n",
    "        gen_loss = generator_loss(generated_output)\n",
    "        disc_loss = discriminator_loss(real_output, generated_output)\n",
    "        \n",
    "    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)\n",
    "    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)\n",
    "      \n",
    "    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))\n",
    "    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 805
    },
    "colab_type": "code",
    "id": "1DPQkOVx2yon",
    "outputId": "f9520ccd-80f9-4ab8-bb92-c7899b8a82f8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 16.848364114761353\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=560x28 at 0x7F53681A58D0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 12.175549507141113\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=560x28 at 0x7F53E1714BA8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 11.367757797241211\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=560x28 at 0x7F53E1714B38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 11.38961124420166\n"
     ]
    },
    {
     "data": {
      "image/png": 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zcfiip68bdtPNeYS8Gt0JoBuaLpP7nV+tiTS8MSAGNz3eNp2SrHrpkm7QAABJx8K/JlE7l/Zpk39LikWlaY0BS7R0Sd5uwKllrSguNRuZFkcP8suyFK15ocfMreeqj696yoUJ5vmE7NPatJcddp6nAJAjyRWuuPF3+S+9Pan0tvwOr7WK7YIN4osIADTp7UWW4FUvWNo1DggA2OGdHjnT5BMleac+Uv3vuWv9qUNPTJ51cXdbRn8ZoZ5M1kXnagYzSgiqsQHDpvEIgMTd5Ug4dLF8/F0rFuopVAAAOv58IFq9omvH+2ujkhxayCFNzXKzxLhzIi2KHSS5UlgWY6BYfCwqRioWVYuyLIujlGnVJ4Jr7bJlTbiXJSRjYroFLvNIxf5Pb2Mxb/PZEgD0iXMBgCh3+vpiTRm8ptgqfJ+IFD2yoBcBIHulk+Y+0i0sR/7mSL3/oCBWa8GNWlpWe03RCbwu1dktCgUA7qIUfTQ+0CwMy7IULRvSaW6jJEnRqCgFihMYzHtytRUOSOltPACkSXKwry3miQxAl82XfIJwefYzv6waFIOKiwgcANDiSd4zWtLBAoytHzUn+QzAZx+4Y+KGk6Ise2PZg/aNonT56YGL72EQkPCmiP/W4/VPa3fSe/UlSJBQBLTlDv/6dEP1lmULjgX8T2ppUgAAe6Ms+f+eQ11fRyTJP5JBkv5qfwIMY1wGIiIdW9eUFcuL7SNfw6El28NipCkcWDn+9uktjZcnWoY57Zoeb7yUZAGjX4aFOhuAY8UmFgAC8nlQtgHQQ2TiLnxkxY06eQAAnByMnho6JB0AcIc4x8QbsgPCkn/TzGlnIpK/rUm9AACOCw0pRtPtYtRj6lRq/ULC0cw4RWNAlt53MYz7nUYhWrfTK8qyOErdkszj9kkLLAQByOgJDEBGVKqwI1OYmepIcRl5ZZrtHDKn8syJj3oO2bKvt9WWOkqVAMDf9cUfYmPCQ2bOJxMN/kz3jTkna/dN5ch0SU74ioLNbfHAHUp8ZTcezSVvq9g9Mkv55LziPhNBROrpfO+KHXteaFdQ+HEwMNMGgFS97SuISoFVH4xs0fnjEzWvZSMAm+ZGANbWwuy1PRd9UxOwwuX1/7yx/vuRN97/6tELNYcmGilEXRo6v2JtTATn9MnSrQCY7gEAGop8g5ZLNgRgil7Yfaq7VZ0tth0frmRdsEJqp7USguj409rp7Tztpp6IhO6OvXQmZ8QZeotdln6JV+fiqG9afGTglsRuALbnTtZdrOjnShrjE30vM3HPIqBlVCqwEFQr/Ib6uwgASR9QxDtSjERUsguUZYFpL52qudGC7CWpFgBo5rshcWocTgCAsXVGepyaLjfonuoldgB4IbHBAPx5fgsNn75/zr7w3WOjn0gFAORfCPm1DRIBAAnlnNc8Ob6jneVvWyKEn7UhUlYRHSdHQjt3ByXRv2fFLTaeSR54iwMAgHPdmWsoIGlL7XSDvFkI7r3GY2Pa3fnOkiNVf8y7WZt2W+d0fea2e++NvVJuIcliKyCUEmRvryn3gNVgEO1Dfjq5M8c6NeTa11sq4rLnfUZuCZHYnBSRSRn9W3C9OX2sgD8o7NTI9/IJZfHPMGy/R5YliDAdwSoPHSZK4isFTj594NDZ75VSALA7La9X6B/CoUThqf2tZakAQJxujw2VfJdSOuTp0cXcnefusLAzSKhLQsC2hwLvxt/UAwD036/HY8TyojV99xEGAFhZTvwicJs3Tu7U3b4Dc3+uPz2KoPO1i+EKa4CKjH3QmHSKyD53WQx/X+Bik9RLzJRnfMGzPkmWZUlo+qVCkGV/OgCAPWX6vDYEAFmGu+bxY755JvYs+/19NdUnvJFI4z+HuFyMZr+2tZtfz1QmFc80FcQJ0FWW5cb1c9fNXfr22HnjPAhoelBDGK7kxYoLnyXHLPs25S87CADQfl/r2QWgiEgQkUnvvSvU+KR58tRNsTra9F7pk/mZRfXhHVOsmyMAANgvC+3iWwG3hwVRluUyR8p1f/u55qNiTxoFAHd6IY/IODgGANC+Knz6xnjvBHCP9zYEYDj1OZJxnZT37cLeLAAA83ZN7Rc3WOaXHo7s/Mtbc7ZWP2+4ZAvyd/apCwIZxvqg58G6zgAAYSktERxcEOKmz7X67P+sPlsX3t5/1EpfoCHWpVFPzyKKAOycy2eX33ddx3yb0kdS+7xVNvXBN4OyLEmSFBFkyZsOAEApn3FtO7crLbPT5EMXjr8YHxSqJf+N972SJJ3Op8gYqcSpgfClgQAAcENwSaKT4K+RaEQIbLjfY3PYGAQwDIYkJ3smL9u8tmfsG3XcEfG/5GJz//Xz/aanSuqprtuMQ+Hwm4zJ52qlsyDLkiSEfOFyF0nACh8O8xah1FruZUkUjtkBMKvXde09DFICABzHc5RJvqVHdnJRn3kNobN9Er6M9tYhAGQmKdcQpu2DHJak4DiGyXll35n3b2ptdQjM/SdESagYwsYxo9S93lS90ZoQdVZ/gQDkgp9JAAcgmbcqpYv2mnhNwdj5u2r8oiw1jUBrL2B6S+X1UNK00UV95r1aRAEAAbWsGrqG3d5j0L55rfNmv0VUOMya+nbHJOL59PRTlky2lRlqY0oq/WuTCFoeL+fuvzQRAcjicG0zb82Ry0wzKdKoOfMHrd327VMZcZPLBGTp1AdVAXM6Uws0M6acC5x1AgAWxIK1C0hS5NzOdT/cmZAT6hOvt8ikVbnbE2TPKOUpoe78HiOWN0WjocppCfeA64MPAQB48hWENhNhm1eWpcvn6k8t7dU1i4sFZP5eU5bf3ANuKknFiHGP6xTALkkAtwf/mlBETBjbICDhu+8UJKm+a5yFsp3UeBid+S07/eWbwQzEWiEAAEHgrqEatpJ/nNnSg7cVx5xUEzzBvyETQf09gy5dnxcfebO8Kbjl6p78mp5hMkWv7lr9VmEC0dv5Aicqz+8xJzA0MNtNa767GwGAzo0jiLZcN2UcyVwiTQMs8nZJ3JGo8M4sGyH2kge31wfPbx6WGb8HAgC8tJIAAJYot57UerS2feiPXvykDRsbP2tYrCdpU90lil05LsUGiQrJzOl3oGpAQpSMHG1OHpL5/JI/mTWmmglrUCGU7zj3niRIZDAAgMqDAQRAR/fKcOhYaUHsuMTq1X+go5a0M4Ikhd5txr/EQZuqRTvrN87ukMjQiL3o7a0jzGS0urv7rLsyAABwcYI4BazH0P+icJyNAPK9Jy5b9mUhRxJPLl6v+B33NQhAuHhJtJ9M/ftiHtb9WLnLWdy3GY1mjt+zf3+fVDZBN5VPXx01g6BVMNeQ/Gb51V4rAQBg9ocVr7njh1wdZU9xl/hEZ7OjTfWSqtqewx6Iv7cDAAB7v0QrjKRc+0Cu4uA7Jgakce7/PypKOIKUMoTEugjDYIwsCrrz/ivCZgojlhWQ1MlTm12C7myeyW2XHa9zPD3qqgkm3tP4q53IK/484v+ymD1MvyXjWYZrLvSxJYwazC/sEvUT+7+Zt6uU1BJT0GYMxtxmT3wphKZfO8akD5rbkshDL7OAfHxsp49FAGzbo7nuqytX2hH+U2P4/4Az98WeZawabO73U1fCiUQ74TYLl/B5fXwx/7aBiWE08eJMTM/8E0YzGmSsr83MOKl2HLGijmGQNt+nlv8F7XSrY5FoxYUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=560x28 at 0x7F53682AB470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 11.278191566467285\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=560x28 at 0x7F53E17144E0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 11.328220844268799\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=560x28 at 0x7F536824DDD8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 11.35372543334961\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=560x28 at 0x7F53E1714B38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 11.041415691375732\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=560x28 at 0x7F53682AB470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 11.560566186904907\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=560x28 at 0x7F53E17146A0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9 11.870312929153442\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=560x28 at 0x7F536824DE80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 11.144068956375122\n"
     ]
    },
    {
     "data": {
      "image/png": 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02vt/ev2wwKQCiCGNorkNIPrp9GQegbrr7rAr2OO7hd97AUABigpAZTaw0Qzx3rYiznoyIzBXVWl0i+V2OmilzdJ2+/+Gk23qg+yVjuOO6zKfWxd2D+Y+TwMMvO5j2auEdiB754kzUvBuizVphqtExj6uagDQ3ekhz3jzZv20/4lsi91jILjyD3nnrCy4feKAI8GbQxpWb+EbNVRjzNbiQBkYKCFYDgCtI0/L8gwdMQQAjKuTfWdccsOkOd+MnFrQsHlyRuayHYZczeJgOcKMai8J34Vdlv58fu8Ue7ZAT5osPkN/THekqZcmCwAAprZI596dPuHd95ZuqW/6IUAXbYMmRSWkDnJYWHS+0/SUnkDwYZ3w1I0MwAyh1HBQT2yJM8/629tOL0yxrah7xCjJCEoPaqNJ2tmWE1PC6vkh0vEIlezH7S7BX/LJkk2fN/na1p0UqHRtgKOOObEtlKiwxISNHM9fUyJKjbeGku8wBr2r2lPD+prWAy68EcmGOm0JyFWvZyAigzY7n5zzY+E1LDpiGROeNWPMK/cBwAy630gTr3u1SVbkw/WVatKOjCr/g9Jb6ijuzrKdVh0e2gnarnksXxb0kQwBAHpRhbb45NLx9y0b/l6NryZ/wm3NQnrYBJm1vvfMFRsAxGfec100cmWS15jW2qfrTuqsLSVRRjzymp/S89m97/lRkptzA2cmyLAE2axPejMAqcVFeptVEa0fnX4IAF4X/DG6PiSIfN5HCwfG8NZh5btNYSBKopWqLKTXYU/FHIt5EvA7+etwg8EW8fz9Dr7b5L+sOHXku3/UiIoiMQAAjEMXEKNXirK4McJhvm1xXf3saIazImM3J9kB1K/pFlUurbGuyWFzIKTvX6qOIlxWDwTkbSwDxDKloahXpC/ufNaUuQQAQKDmj6sk2sai/UShllBo+K+L7wYPfXu+tL/sFdTjcXGOiUt+PCd7B4QEDuyUPKookiB7XVUekUqCTBs+bG4kYRO8tqUxV20K++Jk99NTJm3sWBJ6ecJ1t+mgF/lPFEUuKm8R5JY+qqAhRZATrl6G4cFHrtdlA+CPiu1amoIYyMMYFgHI/KaGkWAEtlgqS0UAQMstn/hk33uMgSYAACmQnjVPCeBqSfzKYXm4ubm5yXf+pLt97d+adweGJ3ZDACBRdsTobz2iu2IBayYJkHzcdSAZAK1xD++81fzNCwAA35KOqgJojQ/JnusQo/dtVGtuJMQeyh3xedeeiN/7yKLKMQAAcFiJlDcBllSFO7uB1Z9fiQDWqz/M/2H5c10DwSgoC4nKfOFoUZXk/4QLrg/DBhYoXqAKVRQt6adSg+92I2EA6FHj3Rj6GGEWxy5T88ndU765mny/Nt4Zpi72e0GWZaFqZZw6SncaHF+zOtJXS7zBX0aALG52HdfOMAKzDBpbSsn55WZEXO73ff6XWyd/dK68zSPUr7TqEQOQ0CQND+f2E/VvLvRRKvsaG7/aum+WkwkKyF7/yKSVG0+7feVr55ZKslS7rVeEq35xbb6PA7EvaWAfGxoM1MIiAOYIu8OvWrRS38MDlm1K1d0o1HuUpOJpEfwLANbJjwAAwN3K+kj9cLI2OTQ4+Ez+V/Xu69OX//LRn0ekJgbsQruGASTlwe2nqyV5RZrNYSeAvFr743d+STUYWfT8Mnnkgp/twXlxrIXj0+3AzHL5TqWaq0eduBKdbmokR11rAueV5JrmopQIs2RXHNp6Z3roO4XOwbzTONBIP/hM8jfbbe8LvkPDWA1LN6x7cdvL4TcDRgiyT5AkmYqeX6d21RZCd/YxSpQiHIO2Up+XKtS9YChPCItadEcupe+Nu4rbqSJLMqWy7+Bg9SxUjz6guSJY+3PxMcSwy5ieK7sRxAGtNXGBbaJP187K1FfaLzTauAEs390c0WDgKF0GAADL6Z0R+0tbosPayO3lVav+tS09RNHChBRq6TN3+wGf+G0vwnAEMfQ1EONHvPHXTxfs/Hhxj37xNgTAtGBKFR2fMH9L/pkvFx4T2otHa0sR4f5nJa0ytzl+8rau6MpxXXI3nZzAEAYjGJoedAZjqWmJ05qtjM7qP2usbpbE6pGqmzB852W+ay+IgzDAl12SLHur3xyVpBOccKHfN8h0ZbhHe7LN62tZmGQWGoGwhDhvKvRTqigKbcsfyWtdISBbvHvVLyKcOYe54plV/ViMLy518lf3NHPmnn+1S3jCH4SU4lHhMwQAGO/fiACAXiHiZRqs9PYMb7U/0Yc35J6EhK7cEIt92qJlf54Q+EwV9jUwEhC77a9+WRJE0f1Fnu66SLhyp0vusNBqm9cmeFo9TQeeS0MIJKcXZgkAgJ8IB7RpME5OZxWWORWi+OMg4+dTFW7xlw2KOKs+q3KdnPmsWSdMQtvSCKKR2KSuEYQLcmBuXdcqyf6GI4uiI91HB1vx6ewOdYx8bB87YYYMMbnIC8MDLQ920NO/D8exzoN0Z2SOomw+RetIttBPktqbvzTxABPfahH9DZunx1zgyv30f4THKcc7jaLkfjvpUv+rwFrjmqenY6TcoYaZ8+KYy/kHhssE5Pund6JO55q034Jr0oHcDnnyMz9f/sH+yHfgwC1n/xbyRILAdZpLR2MddvZi/FgYcCs/jbC1LwizZV+HV9V+E+h0bsxFbuhLBC4jmomJvwytrvSF3Yv4L4NL9LU64NMi/A/CheERf6TTt98QLu3/nC5XNiOejQAwF+WzzWeJ/1nd/EchwhnaReH9Zv+oFhkQ8VJWobNF7oxMp/+Rcpk0/w+/DyKsB7o4MQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=560x28 at 0x7F53E1714B38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11 11.821977615356445\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=560x28 at 0x7F53682AB470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12 11.558624505996704\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=560x28 at 0x7F53E1714A20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13 11.609253644943237\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=560x28 at 0x7F536824DDD8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14 11.420289993286133\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=560x28 at 0x7F53E1714B38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for epoch in range(15):\n",
    "    start_time = time.time()\n",
    "    for images, labels in train_dataset:\n",
    "        train_step(images, labels)\n",
    "    fake = generator([random_vector_for_generation, condition_vector_generation], training=False)\n",
    "    fake_concat = np.transpose(img_to_uint8(fake), [1,0,2]).reshape((28,-1))\n",
    "    print(epoch, time.time()-start_time)\n",
    "    display(PIL.Image.fromarray(fake_concat))\n",
    "  \n",
    "  "
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "CGAN-intro for MNIST",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}