PART2_8.5_Introduction to generative adversarial networks.ipynb

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   "source": [
    "# Chapter8 : Generative deep learning"
   ]
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   "source": [
    "## 8.5 Introduction to generative adversarial networks"
   ]
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    "Generative adversarial networks (GANs) are an alternative to VAEs for learning latent spaces of images. They enable the generation of fairly realistic synthetic images by forcing the generated images to be statistically almost indistinguishable from real ones.\n",
    "\n",
    "In short, a GAN is: a ***generator network*** and a ***discriminator network***, being trained to best the other. A GAN is made of two parts:\n",
    "  + *Generator network*--Takes as input a random vector (a random point in the latent space), and decodes it into a synthetic image\n",
    "  + *Discriminator network (or adversary)*--Takes as input an image (real or synthetic), and predicts whether the image came from the training set or was created by the generator network.\n",
    "  \n",
    "**The generator network is trained to be able to fool the discriminator network, and thus it evolves toward generating increasingly realistic images as training goes on**: artificial images that look indistinguishable from real ones, to the extent that it's impossible for the discriminator network to tell the two apart.\n",
    "\n",
    "Meanwhile, **the discriminator is constantly adapting to the gradually improving capabilities of the generator, setting a high bar of realism for the generated images**. Once training is over, the generator is capable of turning any point in its input space into a believable image. Unlike VAEs, this latent space has fewer explicit guarantees of meaningful structure; in particular, it isn't continuous."
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   "source": [
    "<img src='image/fig815.PNG' width='580'>\n",
    "\n",
    "Remarkably, a GAN is a system where the optimization minimum isn't fixed, unlike in any other training setup you've encountered in this book.\n",
    "\n",
    "With GAN, every small step changes the entire landscape a little. It's a dynamic system where the optimization process is seeking not a minimum, but an equilibrium between two forces. For this reason, *GANs are notoriously difficult to train*--getting a GAN to work requires lots of careful tuning of the model architecture and training parameters."
   ]
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   "source": [
    "### 8.5.1 A schematic GAN implementation"
   ]
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   "source": [
    "In this section, we'll explain how to implement a GAN in Keras, in its barest form. The specific implementation is a *deep convolutional GAN (DCGAN)*: a GAN where the generator and discriminator are deep convnets.\n",
    "\n",
    "You'll train the GAN on images from CIFAR10, a dataset of 50,000 32×32 RGB images belonging to 10 classes (5,000 images per class). To make things easier, you'll only use images belonging to the class \"frog\".\n",
    "\n",
    "Schematically, the GAN looks like this:\n",
    "  1. A `generator` network maps vectors of shape `(latent_dim,)` to images of shape `(32, 32, 3)`.\n",
    "  2. A `discriminator` network maps images of shape `(32, 32, 3)` to binary score estimating the probability that the image is real.\n",
    "  3. A `gan` network chains the generator and the discriminator together: `gan(x) = discriminator(generator(x))`. Thus this `gan` network maps latent space vectors to the discriminator's assessment of the realism of these latent vectors as decoded by the generator.\n",
    "  4. You train the discriminator using examples of real and fake images along with \"real\"/\"fake\" labels, just as you ttrain any regular image-classification model.\n",
    "  5. To train the generator, you use the gradients of the generator's weights with regard to the loss of the `gan` model. This means, at every step, you move the weights of the generator in a direction that makes the discriminator more likely to classify as \"real\" the images decoded by the generator. In other words, you train the generator to fool the discriminator."
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   "source": [
    "### 8.5.2 A bag of tricks"
   ]
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    "The process of training GANs and tuning GAN implementation is notoriously difficult. There are a number of known tricks you should keep in mind.\n",
    "\n",
    "Here are a few of the tricks used in the implementation of the GAN generator and discriminator in this section. It isn't an exhaustive list of GAN-related tips; you'll find many more across the GAN literature:\n",
    "  + We use `tanh` as the last activation in the generator, instead of `sigmoid`, which is more commonly found in other types of models.\n",
    "  + We sample points from the latent space using a *normal distribution* (Gaussian distribution), not a uniform distribution.\n",
    "  + Stochasticity is good to induce robustness. Because GAN training results in a dynamic equilibrium, GANs are likely to get stuck in all sorts of ways. Introducing randomness during training helps prevent this. We introduce randomness in two ways:\n",
    "    - using dropout in the discriminator\n",
    "    - adding random noise to the labels for the discriminator\n",
    "  + Sparse gradients can hinder GAN training: max pooling operations and `ReLU` activations. Instead of max pooling, we recommend using strided convolutions for downsampling, and we recommend using `LeakyReLU` layer instead of a `ReLU` activation (it similar the ReLU, but it relaxes sparsity constraints by allowing small negative activation values).\n",
    "  + In generated images, it's common to see checkerboard artifacts caused by unequal coverage of the pixel space in the generator. To fix this, we use a kernel size that's divisible by the stride size whenever we use a strided `Conv2DTranspose` or `Conv2D` in both the generator and the discriminator.\n",
    "  \n",
    "<img src='image/fig817.PNG' width='500'>"
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   "source": [
    "### 8.5.3 The generator"
   ]
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   "source": [
    "#### GAN generator network"
   ]
  },
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    "One of the many issues that commonly arise with GANs is that the generator gets stuck with generated images that look like noise. A possible solution is to use dropout on both the discriminator and the generator.\n",
    "\n",
    "Let's develop a `generator` model that turns a vector into a candidate image."
   ]
  },
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     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras import layers\n",
    "import numpy as np"
   ]
  },
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   "source": [
    "latent_dim = 32\n",
    "height = 32\n",
    "width = 32\n",
    "channels = 3"
   ]
  },
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      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 32768)             1081344   \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 32768)             0         \n",
      "_________________________________________________________________\n",
      "reshape_1 (Reshape)          (None, 16, 16, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 16, 16, 256)       819456    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 16, 16, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTr (None, 32, 32, 256)       1048832   \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)    (None, 32, 32, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 32, 32, 256)       1638656   \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)    (None, 32, 32, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 32, 32, 256)       1638656   \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_5 (LeakyReLU)    (None, 32, 32, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 32, 32, 3)         37635     \n",
      "=================================================================\n",
      "Total params: 6,264,579\n",
      "Trainable params: 6,264,579\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "generator_input = keras.Input(shape=(latent_dim,))\n",
    "\n",
    "# transforms the input into a 16×16 128-channel feature map\n",
    "x = layers.Dense(128*16*16)(generator_input)\n",
    "x = layers.LeakyReLU()(x)\n",
    "x = layers.Reshape((16, 16, 128))(x)\n",
    "\n",
    "x = layers.Conv2D(256, 5, padding='same')(x)\n",
    "x = layers.LeakyReLU()(x)\n",
    "\n",
    "# upsamples to 32×32\n",
    "x = layers.Conv2DTranspose(256, 4, strides=2, padding='same')(x)\n",
    "x = layers.LeakyReLU()(x)\n",
    "\n",
    "x = layers.Conv2D(256, 5, padding='same')(x)\n",
    "x = layers.LeakyReLU()(x)\n",
    "x = layers.Conv2D(256, 5, padding='same')(x)\n",
    "x = layers.LeakyReLU()(x)\n",
    "\n",
    "# produces a 32×32 1-channel feature map (shape of a CIFAR10 image)\n",
    "x = layers.Conv2D(channels, 7, activation='tanh', padding='same')(x)\n",
    "# instantiates the generator model, \n",
    "# which maps the input of shape (latent_dim,) into an image of shape (32,32,3)\n",
    "generator = keras.models.Model(generator_input, x)\n",
    "generator.summary()"
   ]
  },
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   "source": [
    "### 8.5.4 The discriminator\n",
    "\n",
    "#### The GAN discriminator network"
   ]
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   "source": [
    "Next, you'll develop a `discriminator` model that takes as input a candidate image (real or synthetic) and classifies it into one of two classes: \"generated image\" or \"real image that comes from the training set\"."
   ]
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     "text": [
      "Model: \"model_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         (None, 32, 32, 3)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 30, 30, 128)       3584      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_6 (LeakyReLU)    (None, 30, 30, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 14, 14, 128)       262272    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_7 (LeakyReLU)    (None, 14, 14, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 6, 6, 128)         262272    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_8 (LeakyReLU)    (None, 6, 6, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 2, 2, 128)         262272    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_9 (LeakyReLU)    (None, 2, 2, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 513       \n",
      "=================================================================\n",
      "Total params: 790,913\n",
      "Trainable params: 790,913\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "discriminator_input = layers.Input(shape=(height, width, channels))\n",
    "x = layers.Conv2D(128, 3)(discriminator_input)\n",
    "x = layers.LeakyReLU()(x)\n",
    "x = layers.Conv2D(128, 4, strides=2)(x)\n",
    "x = layers.LeakyReLU()(x)\n",
    "x = layers.Conv2D(128, 4, strides=2)(x)\n",
    "x = layers.LeakyReLU()(x)\n",
    "x = layers.Conv2D(128, 4, strides=2)(x)\n",
    "x = layers.LeakyReLU()(x)\n",
    "x = layers.Flatten()(x)\n",
    "\n",
    "# one dropout layer: an important trick!\n",
    "x = layers.Dropout(0.4)(x)\n",
    "\n",
    "# instantiates the discriminator model,\n",
    "# which turns a (32, 32, 3) input into a binary classification decision (fake/real)\n",
    "# classification layer\n",
    "x = layers.Dense(1, activation='sigmoid')(x)\n",
    "discriminator = keras.models.Model(discriminator_input, x)\n",
    "discriminator.summary()"
   ]
  },
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    "discriminator_optimizer = keras.optimizers.RMSprop(\n",
    "     lr=0.0008,\n",
    "     clipvalue=1.0, # uses gradient clipping (by value) in the optimizer\n",
    "     decay=1e-8)    # to stabilize training, uses learning-rate decay \n",
    "discriminator.compile(optimizer=discriminator_optimizer,\n",
    "                       loss='binary_crossentropy')"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "### 8.5.5 The adversarial network"
   ]
  },
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    "Finally, you'll set up the GAN, which chains the generator and the discriminator. When trained, this model will move the generator in a direction that improves its ability to fool the discriminator.\n",
    "\n",
    "This model turns latent-space points into a classification decision--\"fake\" or \"real\"--and it's meant to be trained with labels that are always \"these are real images\". So training `gan` will update the weights of `generator` in a way that makes `discriminator` more likely to predict \"real\" when looking at fake images.\n",
    "\n",
    "It's very important to note that you **set the discriminator to be frozen during training (non-trainable)**: its weights won't be updated when training `gan`. That is, if the discriminator weights could be updated during this process, then you'd be training the discriminator to always predict \"real\", which isn't what you want!"
   ]
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   "source": [
    "#### Adversarial network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
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   "source": [
    "# sets discriminator weights to non-trainable\n",
    "discriminator.trainable = False\n",
    "\n",
    "gan_input = keras.Input(shape=(latent_dim,))\n",
    "gan_output = discriminator(generator(gan_input))\n",
    "gan = keras.models.Model(gan_input, gan_output)\n",
    "\n",
    "gan_optimizer = keras.optimizers.RMSprop(lr=0.0004,\n",
    "                                         clipvalue=1.0,\n",
    "                                         decay=1e-8)\n",
    "gan.compile(optimizer=gan_optimizer,\n",
    "            loss='binary_crossentropy')"
   ]
  },
  {
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   "source": [
    "### 8.5.6 How to train you DCGAN"
   ]
  },
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   "source": [
    "Now you can begin training. To recapitulate, that is what the training loop looks like schematically. For each epoch, you do the following:\n",
    "  1. Draw random points in the latent space (random noise).\n",
    "  2. Generate image with `generator` using this random noise.\n",
    "  3. Mix the generated images with real ones.\n",
    "  4. Train `discriminator` using these mixed images, with corresponding targets: either \"real\" (for the real images) or \"fake\" (for the generated images).\n",
    "  5. Draw new random points in the latent space.\n",
    "  6. Train `gan` using these random vectors, with targets that all say \"these are real images\". This updates the weights of the generator (only, because the discriminator is frozen inside `gan`) to move them toward getting the discriminator to predict \"these are real images\" for generated images: this trains the generator to fool the discriminator."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "W4RLfoPMVpA4"
   },
   "source": [
    "#### Implementing GAN training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PEpo90EhVpA6"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from keras.preprocessing import image\n",
    "\n",
    "# Load CIFAR10 data\n",
    "(x_train, y_train), (_, _) = keras.datasets.cifar10.load_data()\n",
    "\n",
    "# Select dog images (class 5)\n",
    "x_train = x_train[y_train.flatten() == 5]\n",
    "\n",
    "# Normalize data\n",
    "x_train = x_train.reshape(\n",
    "    (x_train.shape[0],) + (height, width, channels)).astype('float32') / 255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "88QVvwPcWdiA",
    "outputId": "b951005c-3dee-4039-b1a2-9c62154b6cb7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 32, 32, 3)"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Kc_YmC3jWs3n",
    "outputId": "6f163165-75f4-419e-a1e0-27984b23c27e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 591
    },
    "colab_type": "code",
    "id": "YSPzhXcLXLFL",
    "outputId": "19827767-41c7-499e-f975-e030d7bf728e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 32768)             1081344   \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 32768)             0         \n",
      "_________________________________________________________________\n",
      "reshape_1 (Reshape)          (None, 16, 16, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 16, 16, 256)       819456    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 16, 16, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTr (None, 32, 32, 256)       1048832   \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)    (None, 32, 32, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 32, 32, 256)       1638656   \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)    (None, 32, 32, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 32, 32, 256)       1638656   \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_5 (LeakyReLU)    (None, 32, 32, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 32, 32, 3)         37635     \n",
      "=================================================================\n",
      "Total params: 6,264,579\n",
      "Trainable params: 6,264,579\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "generator.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 591
    },
    "colab_type": "code",
    "id": "Nv_vk-mTXK70",
    "outputId": "996c2460-276f-4047-c765-e3bef607f6df"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         (None, 32, 32, 3)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 30, 30, 128)       3584      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_6 (LeakyReLU)    (None, 30, 30, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 14, 14, 128)       262272    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_7 (LeakyReLU)    (None, 14, 14, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 6, 6, 128)         262272    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_8 (LeakyReLU)    (None, 6, 6, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 2, 2, 128)         262272    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_9 (LeakyReLU)    (None, 2, 2, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 513       \n",
      "=================================================================\n",
      "Total params: 1,581,826\n",
      "Trainable params: 790,913\n",
      "Non-trainable params: 790,913\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py:297: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
      "  'Discrepancy between trainable weights and collected trainable'\n"
     ]
    }
   ],
   "source": [
    "discriminator.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 253
    },
    "colab_type": "code",
    "id": "W7cpVODXXKw9",
    "outputId": "1f03649a-9b0b-4eff-d2dd-fd87e9783ce9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_3 (InputLayer)         (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "model_1 (Model)              (None, 32, 32, 3)         6264579   \n",
      "_________________________________________________________________\n",
      "model_2 (Model)              (None, 1)                 790913    \n",
      "=================================================================\n",
      "Total params: 7,055,492\n",
      "Trainable params: 6,264,579\n",
      "Non-trainable params: 790,913\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "gan.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "4C9aOaZDVpBD",
    "outputId": "da6b7ec1-57d4-40ac-c2b7-9684b0c75eed"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py:297: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
      "  'Discrepancy between trainable weights and collected trainable'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "discriminator loss at step 0: 0.6882876\n",
      "adversarial loss at step 0: 0.69443476\n",
      "discriminator loss at step 100: 0.622578\n",
      "adversarial loss at step 100: 1.1532059\n",
      "discriminator loss at step 200: 0.68997896\n",
      "adversarial loss at step 200: 0.73220384\n",
      "discriminator loss at step 300: 0.7003889\n",
      "adversarial loss at step 300: 0.8988147\n",
      "discriminator loss at step 400: 0.68260145\n",
      "adversarial loss at step 400: 0.7336546\n",
      "discriminator loss at step 500: 0.6906723\n",
      "adversarial loss at step 500: 0.7646166\n",
      "discriminator loss at step 600: 0.7027854\n",
      "adversarial loss at step 600: 0.77542514\n",
      "discriminator loss at step 700: 0.6922204\n",
      "adversarial loss at step 700: 0.74403155\n",
      "discriminator loss at step 800: 0.73204744\n",
      "adversarial loss at step 800: 0.7646332\n",
      "discriminator loss at step 900: 0.7138254\n",
      "adversarial loss at step 900: 0.7957219\n",
      "discriminator loss at step 1000: 0.69800305\n",
      "adversarial loss at step 1000: 0.73184127\n",
      "discriminator loss at step 1100: 0.6862015\n",
      "adversarial loss at step 1100: 0.76755255\n",
      "discriminator loss at step 1200: 0.69354147\n",
      "adversarial loss at step 1200: 0.7594651\n",
      "discriminator loss at step 1300: 0.6994674\n",
      "adversarial loss at step 1300: 0.7620866\n",
      "discriminator loss at step 1400: 0.6956771\n",
      "adversarial loss at step 1400: 0.7531488\n",
      "discriminator loss at step 1500: 0.6762949\n",
      "adversarial loss at step 1500: 1.1082332\n",
      "discriminator loss at step 1600: 0.693966\n",
      "adversarial loss at step 1600: 0.79487413\n",
      "discriminator loss at step 1700: 0.71594965\n",
      "adversarial loss at step 1700: 0.97179353\n",
      "discriminator loss at step 1800: 0.71967757\n",
      "adversarial loss at step 1800: 0.7881365\n",
      "discriminator loss at step 1900: 0.6882353\n",
      "adversarial loss at step 1900: 0.78106564\n",
      "discriminator loss at step 2000: 0.73697066\n",
      "adversarial loss at step 2000: 0.8335823\n",
      "discriminator loss at step 2100: 0.6711702\n",
      "adversarial loss at step 2100: 0.79957277\n",
      "discriminator loss at step 2200: 0.66895175\n",
      "adversarial loss at step 2200: 0.76206267\n",
      "discriminator loss at step 2300: 0.6981786\n",
      "adversarial loss at step 2300: 0.7362746\n",
      "discriminator loss at step 2400: 0.7019521\n",
      "adversarial loss at step 2400: 0.7947113\n",
      "discriminator loss at step 2500: 0.67944705\n",
      "adversarial loss at step 2500: 0.79589903\n",
      "discriminator loss at step 2600: 0.6824171\n",
      "adversarial loss at step 2600: 0.7443646\n",
      "discriminator loss at step 2700: 0.6829341\n",
      "adversarial loss at step 2700: 0.9786909\n",
      "discriminator loss at step 2800: 0.68344414\n",
      "adversarial loss at step 2800: 0.7810426\n",
      "discriminator loss at step 2900: 0.731344\n",
      "adversarial loss at step 2900: 0.8597888\n",
      "discriminator loss at step 3000: 0.65708315\n",
      "adversarial loss at step 3000: 0.82416373\n",
      "discriminator loss at step 3100: 0.7487601\n",
      "adversarial loss at step 3100: 0.7675364\n",
      "discriminator loss at step 3200: 0.8024352\n",
      "adversarial loss at step 3200: 0.91891605\n",
      "discriminator loss at step 3300: 0.67130506\n",
      "adversarial loss at step 3300: 0.7946938\n",
      "discriminator loss at step 3400: 0.7452315\n",
      "adversarial loss at step 3400: 0.8545124\n",
      "discriminator loss at step 3500: 0.68737346\n",
      "adversarial loss at step 3500: 0.7730989\n",
      "discriminator loss at step 3600: 0.6978839\n",
      "adversarial loss at step 3600: 0.77839077\n",
      "discriminator loss at step 3700: 0.67816585\n",
      "adversarial loss at step 3700: 0.7692169\n",
      "discriminator loss at step 3800: 0.6780579\n",
      "adversarial loss at step 3800: 0.70032775\n",
      "discriminator loss at step 3900: 0.69609255\n",
      "adversarial loss at step 3900: 0.78059816\n",
      "discriminator loss at step 4000: 0.8281943\n",
      "adversarial loss at step 4000: 0.94384843\n",
      "discriminator loss at step 4100: 0.71003735\n",
      "adversarial loss at step 4100: 0.85741156\n",
      "discriminator loss at step 4200: 0.6844631\n",
      "adversarial loss at step 4200: 0.7766482\n",
      "discriminator loss at step 4300: 0.68381995\n",
      "adversarial loss at step 4300: 0.814098\n",
      "discriminator loss at step 4400: 0.7064435\n",
      "adversarial loss at step 4400: 0.87695944\n",
      "discriminator loss at step 4500: 0.7066448\n",
      "adversarial loss at step 4500: 0.736464\n",
      "discriminator loss at step 4600: 0.6872272\n",
      "adversarial loss at step 4600: 0.9734338\n",
      "discriminator loss at step 4700: 0.6782044\n",
      "adversarial loss at step 4700: 0.7680445\n",
      "discriminator loss at step 4800: 0.6966518\n",
      "adversarial loss at step 4800: 0.7337259\n",
      "discriminator loss at step 4900: 0.7220939\n",
      "adversarial loss at step 4900: 0.7844418\n",
      "discriminator loss at step 5000: 0.68920696\n",
      "adversarial loss at step 5000: 0.7668432\n",
      "discriminator loss at step 5100: 0.6963519\n",
      "adversarial loss at step 5100: 0.7412802\n",
      "discriminator loss at step 5200: 0.71019953\n",
      "adversarial loss at step 5200: 0.76119155\n",
      "discriminator loss at step 5300: 0.69452035\n",
      "adversarial loss at step 5300: 0.76359904\n",
      "discriminator loss at step 5400: 0.6979643\n",
      "adversarial loss at step 5400: 0.79184824\n",
      "discriminator loss at step 5500: 0.70249426\n",
      "adversarial loss at step 5500: 0.772393\n",
      "discriminator loss at step 5600: 0.69455034\n",
      "adversarial loss at step 5600: 0.73068553\n",
      "discriminator loss at step 5700: 0.8907466\n",
      "adversarial loss at step 5700: 1.2516406\n",
      "discriminator loss at step 5800: 0.6983064\n",
      "adversarial loss at step 5800: 0.7634153\n",
      "discriminator loss at step 5900: 0.7080096\n",
      "adversarial loss at step 5900: 0.7508443\n",
      "discriminator loss at step 6000: 0.68890953\n",
      "adversarial loss at step 6000: 0.77562726\n",
      "discriminator loss at step 6100: 0.68097603\n",
      "adversarial loss at step 6100: 0.7124772\n",
      "discriminator loss at step 6200: 0.7353378\n",
      "adversarial loss at step 6200: 1.7868283\n",
      "discriminator loss at step 6300: 0.69193316\n",
      "adversarial loss at step 6300: 0.69880104\n",
      "discriminator loss at step 6400: 0.6975633\n",
      "adversarial loss at step 6400: 0.7309841\n",
      "discriminator loss at step 6500: 0.67699546\n",
      "adversarial loss at step 6500: 0.8049976\n",
      "discriminator loss at step 6600: 0.6860729\n",
      "adversarial loss at step 6600: 0.7918258\n",
      "discriminator loss at step 6700: 0.6969398\n",
      "adversarial loss at step 6700: 0.7439039\n",
      "discriminator loss at step 6800: 0.6772425\n",
      "adversarial loss at step 6800: 0.91203374\n",
      "discriminator loss at step 6900: 0.69971204\n",
      "adversarial loss at step 6900: 0.98381186\n",
      "discriminator loss at step 7000: 0.7011033\n",
      "adversarial loss at step 7000: 0.7870745\n",
      "discriminator loss at step 7100: 0.728882\n",
      "adversarial loss at step 7100: 0.7034898\n",
      "discriminator loss at step 7200: 0.6861555\n",
      "adversarial loss at step 7200: 0.7554115\n",
      "discriminator loss at step 7300: 0.7012042\n",
      "adversarial loss at step 7300: 0.80177367\n",
      "discriminator loss at step 7400: 0.6903853\n",
      "adversarial loss at step 7400: 0.76002705\n",
      "discriminator loss at step 7500: 0.7135509\n",
      "adversarial loss at step 7500: 0.8332917\n",
      "discriminator loss at step 7600: 0.6989972\n",
      "adversarial loss at step 7600: 0.769434\n",
      "discriminator loss at step 7700: 0.69422925\n",
      "adversarial loss at step 7700: 0.73625594\n",
      "discriminator loss at step 7800: 0.69450754\n",
      "adversarial loss at step 7800: 0.78356487\n",
      "discriminator loss at step 7900: 0.6707481\n",
      "adversarial loss at step 7900: 0.75460184\n",
      "discriminator loss at step 8000: 0.6949031\n",
      "adversarial loss at step 8000: 0.81648904\n",
      "discriminator loss at step 8100: 0.68407345\n",
      "adversarial loss at step 8100: 0.743652\n",
      "discriminator loss at step 8200: 0.69685096\n",
      "adversarial loss at step 8200: 0.7134987\n",
      "discriminator loss at step 8300: 0.69513464\n",
      "adversarial loss at step 8300: 0.8305353\n",
      "discriminator loss at step 8400: 0.69544125\n",
      "adversarial loss at step 8400: 0.7185669\n",
      "discriminator loss at step 8500: 0.6940277\n",
      "adversarial loss at step 8500: 0.8755964\n",
      "discriminator loss at step 8600: 0.7044201\n",
      "adversarial loss at step 8600: 0.7343934\n",
      "discriminator loss at step 8700: 0.6744899\n",
      "adversarial loss at step 8700: 0.8173636\n",
      "discriminator loss at step 8800: 0.689687\n",
      "adversarial loss at step 8800: 0.83108985\n",
      "discriminator loss at step 8900: 0.69570225\n",
      "adversarial loss at step 8900: 0.8299713\n",
      "discriminator loss at step 9000: 0.6971501\n",
      "adversarial loss at step 9000: 0.79804957\n",
      "discriminator loss at step 9100: 0.66547406\n",
      "adversarial loss at step 9100: 0.66569704\n",
      "discriminator loss at step 9200: 0.6797432\n",
      "adversarial loss at step 9200: 0.757631\n",
      "discriminator loss at step 9300: 0.655074\n",
      "adversarial loss at step 9300: 0.85331154\n",
      "discriminator loss at step 9400: 0.6977992\n",
      "adversarial loss at step 9400: 0.7705022\n",
      "discriminator loss at step 9500: 0.6935812\n",
      "adversarial loss at step 9500: 0.7751171\n",
      "discriminator loss at step 9600: 0.71254146\n",
      "adversarial loss at step 9600: 0.8395499\n",
      "discriminator loss at step 9700: 0.68884236\n",
      "adversarial loss at step 9700: 0.7667696\n",
      "discriminator loss at step 9800: 0.6830314\n",
      "adversarial loss at step 9800: 0.8061491\n",
      "discriminator loss at step 9900: 0.7334007\n",
      "adversarial loss at step 9900: 0.8118762\n"
     ]
    }
   ],
   "source": [
    "iterations = 10000\n",
    "batch_size = 20\n",
    "save_dir = '/content/drive/My Drive/github/'\n",
    "\n",
    "# Start training loop\n",
    "start = 0\n",
    "for step in range(iterations):\n",
    "    # Sample random points in the latent space\n",
    "    random_latent_vectors = np.random.normal(size=(batch_size, latent_dim))\n",
    "\n",
    "    # Decode them to fake images\n",
    "    generated_images = generator.predict(random_latent_vectors)\n",
    "\n",
    "    # Combine them with real images\n",
    "    stop = start + batch_size\n",
    "    real_images = x_train[start: stop]\n",
    "    combined_images = np.concatenate([generated_images, real_images])\n",
    "\n",
    "    # Assemble labels discriminating real from fake images\n",
    "    labels = np.concatenate([np.ones((batch_size, 1)),\n",
    "                             np.zeros((batch_size, 1))])\n",
    "    # Add random noise to the labels - important trick!\n",
    "    labels = labels + 0.05 * np.random.random(labels.shape)\n",
    "\n",
    "    # Train the discriminator\n",
    "    d_loss = discriminator.train_on_batch(combined_images, labels)\n",
    "\n",
    "    # sample random points in the latent space\n",
    "    random_latent_vectors = np.random.normal(size=(batch_size, latent_dim))\n",
    "\n",
    "    # Assemble labels that say \"all real images\"\n",
    "    misleading_targets = np.zeros((batch_size, 1))\n",
    "\n",
    "    # Train the generator (via the gan model,\n",
    "    # where the discriminator weights are frozen)\n",
    "    a_loss = gan.train_on_batch(random_latent_vectors, misleading_targets)\n",
    "    \n",
    "    start = start + batch_size\n",
    "    if start > len(x_train) - batch_size:\n",
    "      start = 0\n",
    "\n",
    "    # Occasionally save / plot\n",
    "    if step % 100 == 0:\n",
    "        # Save model weights\n",
    "        gan.save_weights('/content/drive/My Drive/github/gan.h5')\n",
    "\n",
    "        # Print metrics\n",
    "        print('discriminator loss at step %s: %s' % (step, d_loss))\n",
    "        print('adversarial loss at step %s: %s' % (step, a_loss))\n",
    "\n",
    "        # Save one generated image\n",
    "        img = image.array_to_img(generated_images[0] * 255., scale=False)\n",
    "        img.save(os.path.join(save_dir, 'generated_dog' + str(step) + '.png'))\n",
    "\n",
    "        # Save one real image, for comparison\n",
    "        img = image.array_to_img(real_images[0] * 255., scale=False)\n",
    "        img.save(os.path.join(save_dir, 'real_dog' + str(step) + '.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3ojRpYpsbRv-"
   },
   "source": [
    "When training, you may see the adversarial loss begin to increase considerably, while the discriminator loss tends to zero--the discriminator may end up dominating the generator."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EIpF37fGbj0o"
   },
   "source": [
    "#### Let's display a few of out fake images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "9lcMX1WcjjAr",
    "outputId": "fc5f8016-c89d-4b5f-bda5-c1e82fa2fc4b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Sample random points in the latent space\n",
    "random_latent_vectors = np.random.normal(size=(25, latent_dim))\n",
    "\n",
    "# Decode them to fake images\n",
    "generated_images = generator.predict(random_latent_vectors)\n",
    "\n",
    "f, ax = plt.subplots(5, 5, figsize=(20,20))\n",
    "\n",
    "for i in range(generated_images.shape[0]):\n",
    "    img = image.array_to_img(generated_images[i] * 255., scale=False)\n",
    "    ax[i//5,i%5].imshow(img)\n",
    "    \n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "PART2_8.5.ipynb",
   "provenance": []
  },
  "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}