DCGAN_MNIST.ipynb

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   "source": [
    "import os, time, itertools, imageio, pickle\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "def lrelu(x, th=0.2):\n",
    "    return tf.maximum(th * x, x)\n",
    "\n",
    "# G(z)\n",
    "def generator(x, isTrain=True, reuse=False):\n",
    "    with tf.variable_scope('generator', reuse=reuse):\n",
    "\n",
    "        # 1st hidden layer\n",
    "        conv1 = tf.layers.conv2d_transpose(x, 1024, [4, 4], strides=(1, 1), padding='valid')\n",
    "        lrelu1 = lrelu(tf.layers.batch_normalization(conv1, training=isTrain), 0.2)\n",
    "\n",
    "        # 2nd hidden layer\n",
    "        conv2 = tf.layers.conv2d_transpose(lrelu1, 512, [4, 4], strides=(2, 2), padding='same')\n",
    "        lrelu2 = lrelu(tf.layers.batch_normalization(conv2, training=isTrain), 0.2)\n",
    "\n",
    "        # 3rd hidden layer\n",
    "        conv3 = tf.layers.conv2d_transpose(lrelu2, 256, [4, 4], strides=(2, 2), padding='same')\n",
    "        lrelu3 = lrelu(tf.layers.batch_normalization(conv3, training=isTrain), 0.2)\n",
    "\n",
    "        # 4th hidden layer\n",
    "        conv4 = tf.layers.conv2d_transpose(lrelu3, 128, [4, 4], strides=(2, 2), padding='same')\n",
    "        lrelu4 = lrelu(tf.layers.batch_normalization(conv4, training=isTrain), 0.2)\n",
    "\n",
    "        # output layer\n",
    "        conv5 = tf.layers.conv2d_transpose(lrelu4, 1, [4, 4], strides=(2, 2), padding='same')\n",
    "        o = tf.nn.tanh(conv5)\n",
    "\n",
    "        return o\n",
    "\n",
    "# D(x)\n",
    "def discriminator(x, isTrain=True, reuse=False):\n",
    "    with tf.variable_scope('discriminator', reuse=reuse):\n",
    "        # 1st hidden layer\n",
    "        conv1 = tf.layers.conv2d(x, 128, [4, 4], strides=(2, 2), padding='same')\n",
    "        lrelu1 = lrelu(conv1, 0.2)\n",
    "\n",
    "        # 2nd hidden layer\n",
    "        conv2 = tf.layers.conv2d(lrelu1, 256, [4, 4], strides=(2, 2), padding='same')\n",
    "        lrelu2 = lrelu(tf.layers.batch_normalization(conv2, training=isTrain), 0.2)\n",
    "\n",
    "        # 3rd hidden layer\n",
    "        conv3 = tf.layers.conv2d(lrelu2, 512, [4, 4], strides=(2, 2), padding='same')\n",
    "        lrelu3 = lrelu(tf.layers.batch_normalization(conv3, training=isTrain), 0.2)\n",
    "\n",
    "        # 4th hidden layer\n",
    "        conv4 = tf.layers.conv2d(lrelu3, 1024, [4, 4], strides=(2, 2), padding='same')\n",
    "        lrelu4 = lrelu(tf.layers.batch_normalization(conv4, training=isTrain), 0.2)\n",
    "\n",
    "        # output layer\n",
    "        conv5 = tf.layers.conv2d(lrelu4, 1, [4, 4], strides=(1, 1), padding='valid')\n",
    "        o = tf.nn.sigmoid(conv5)\n",
    "\n",
    "        return o, conv5\n",
    "\n",
    "fixed_z_ = np.random.normal(0, 1, (25, 1, 1, 100))\n",
    "def show_result(num_epoch, show = False, save = False, path = 'result.png'):\n",
    "    test_images = sess.run(G_z, {z: fixed_z_, isTrain: False})\n",
    "\n",
    "    size_figure_grid = 5\n",
    "    fig, ax = plt.subplots(size_figure_grid, size_figure_grid, figsize=(5, 5))\n",
    "    for i, j in itertools.product(range(size_figure_grid), range(size_figure_grid)):\n",
    "        ax[i, j].get_xaxis().set_visible(False)\n",
    "        ax[i, j].get_yaxis().set_visible(False)\n",
    "\n",
    "    for k in range(size_figure_grid*size_figure_grid):\n",
    "        i = k // size_figure_grid\n",
    "        j = k % size_figure_grid\n",
    "        ax[i, j].cla()\n",
    "        ax[i, j].imshow(np.reshape(test_images[k], (64, 64)), cmap='gray')\n",
    "\n",
    "    label = 'Epoch {0}'.format(num_epoch)\n",
    "    fig.text(0.5, 0.04, label, ha='center')\n",
    "\n",
    "    if save:\n",
    "        plt.savefig(path)\n",
    "\n",
    "    if show:\n",
    "        plt.show()\n",
    "    else:\n",
    "        plt.close()\n",
    "\n",
    "def show_train_hist(hist, show = False, save = False, path = 'Train_hist.png'):\n",
    "    x = range(len(hist['D_losses']))\n",
    "\n",
    "    y1 = hist['D_losses']\n",
    "    y2 = hist['G_losses']\n",
    "\n",
    "    plt.plot(x, y1, label='D_loss')\n",
    "    plt.plot(x, y2, label='G_loss')\n",
    "\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss')\n",
    "\n",
    "    plt.legend(loc=4)\n",
    "    plt.grid(True)\n",
    "    plt.tight_layout()\n",
    "\n",
    "    if save:\n",
    "        plt.savefig(path)\n",
    "\n",
    "    if show:\n",
    "        plt.show()\n",
    "    else:\n",
    "        plt.close()\n",
    "\n",
    "# training parameters\n",
    "batch_size = 100\n",
    "lr = 0.0002\n",
    "train_epoch = 20\n",
    "\n",
    "# load MNIST\n",
    "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True, reshape=[])\n",
    "\n",
    "# variables : input\n",
    "x = tf.placeholder(tf.float32, shape=(None, 64, 64, 1))\n",
    "z = tf.placeholder(tf.float32, shape=(None, 1, 1, 100))\n",
    "isTrain = tf.placeholder(dtype=tf.bool)\n",
    "\n",
    "# networks : generator\n",
    "G_z = generator(z, isTrain)\n",
    "\n",
    "# networks : discriminator\n",
    "D_real, D_real_logits = discriminator(x, isTrain)\n",
    "D_fake, D_fake_logits = discriminator(G_z, isTrain, reuse=True)\n",
    "\n",
    "# loss for each network\n",
    "D_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_real_logits, labels=tf.ones([batch_size, 1, 1, 1])))\n",
    "D_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_fake_logits, labels=tf.zeros([batch_size, 1, 1, 1])))\n",
    "D_loss = D_loss_real + D_loss_fake\n",
    "G_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_fake_logits, labels=tf.ones([batch_size, 1, 1, 1])))\n",
    "\n",
    "# trainable variables for each network\n",
    "T_vars = tf.trainable_variables()\n",
    "D_vars = [var for var in T_vars if var.name.startswith('discriminator')]\n",
    "G_vars = [var for var in T_vars if var.name.startswith('generator')]\n",
    "\n",
    "# optimizer for each network\n",
    "with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n",
    "    D_optim = tf.train.AdamOptimizer(lr, beta1=0.5).minimize(D_loss, var_list=D_vars)\n",
    "    G_optim = tf.train.AdamOptimizer(lr, beta1=0.5).minimize(G_loss, var_list=G_vars)\n",
    "\n",
    "# open session and initialize all variables\n",
    "sess = tf.InteractiveSession()\n",
    "tf.global_variables_initializer().run()\n",
    "\n",
    "# MNIST resize and normalization\n",
    "train_set = tf.image.resize_images(mnist.train.images, [64, 64]).eval()\n",
    "train_set = (train_set - 0.5) / 0.5  # normalization; range: -1 ~ 1\n",
    "\n",
    "# results save folder\n",
    "root = 'MNIST_DCGAN_results/'\n",
    "model = 'MNIST_DCGAN_'\n",
    "if not os.path.isdir(root):\n",
    "    os.mkdir(root)\n",
    "if not os.path.isdir(root + 'Fixed_results'):\n",
    "    os.mkdir(root + 'Fixed_results')\n",
    "\n",
    "train_hist = {}\n",
    "train_hist['D_losses'] = []\n",
    "train_hist['G_losses'] = []\n",
    "train_hist['per_epoch_ptimes'] = []\n",
    "train_hist['total_ptime'] = []\n",
    "\n",
    "# training-loop\n",
    "np.random.seed(int(time.time()))\n",
    "print('training start!')\n",
    "start_time = time.time()\n",
    "for epoch in range(train_epoch):\n",
    "    G_losses = []\n",
    "    D_losses = []\n",
    "    epoch_start_time = time.time()\n",
    "    for iter in range(mnist.train.num_examples // batch_size):\n",
    "        # update discriminator\n",
    "        x_ = train_set[iter*batch_size:(iter+1)*batch_size]\n",
    "        z_ = np.random.normal(0, 1, (batch_size, 1, 1, 100))\n",
    "\n",
    "        loss_d_, _ = sess.run([D_loss, D_optim], {x: x_, z: z_, isTrain: True})\n",
    "        D_losses.append(loss_d_)\n",
    "\n",
    "        # update generator\n",
    "        z_ = np.random.normal(0, 1, (batch_size, 1, 1, 100))\n",
    "        loss_g_, _ = sess.run([G_loss, G_optim], {z: z_, x: x_, isTrain: True})\n",
    "        G_losses.append(loss_g_)\n",
    "\n",
    "    epoch_end_time = time.time()\n",
    "    per_epoch_ptime = epoch_end_time - epoch_start_time\n",
    "    print('[%d/%d] - ptime: %.2f loss_d: %.3f, loss_g: %.3f' % ((epoch + 1), train_epoch, per_epoch_ptime, np.mean(D_losses), np.mean(G_losses)))\n",
    "    fixed_p = root + 'Fixed_results/' + model + str(epoch + 1) + '.png'\n",
    "    show_result((epoch + 1), save=True, path=fixed_p)\n",
    "    train_hist['D_losses'].append(np.mean(D_losses))\n",
    "    train_hist['G_losses'].append(np.mean(G_losses))\n",
    "    train_hist['per_epoch_ptimes'].append(per_epoch_ptime)\n",
    "\n",
    "end_time = time.time()\n",
    "total_ptime = end_time - start_time\n",
    "train_hist['total_ptime'].append(total_ptime)\n",
    "\n",
    "print('Avg per epoch ptime: %.2f, total %d epochs ptime: %.2f' % (np.mean(train_hist['per_epoch_ptimes']), train_epoch, total_ptime))\n",
    "print(\"Training finish!... save training results\")\n",
    "with open(root + model + 'train_hist.pkl', 'wb') as f:\n",
    "    pickle.dump(train_hist, f)\n",
    "\n",
    "show_train_hist(train_hist, save=True, path=root + model + 'train_hist.png')\n",
    "\n",
    "images = []\n",
    "for e in range(train_epoch):\n",
    "    img_name = root + 'Fixed_results/' + model + str(e + 1) + '.png'\n",
    "    images.append(imageio.imread(img_name))\n",
    "imageio.mimsave(root + model + 'generation_animation.gif', images, fps=5)\n",
    "\n",
    "sess.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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