01_GAN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "from lib.ops import *\n",
    "from lib.utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GAN():\n",
    "    def __init__(self, sess, epoch, batch_size, z_dim, dataset, model_name, checkpoint_dir, result_dir, logs):\n",
    "        self._sess = sess\n",
    "        self._dataset = dataset\n",
    "        self._model_name = model_name\n",
    "        self._ckpt_dir = checkpoint_dir\n",
    "        self._res_dir = result_dir\n",
    "        self._log_dir = logs\n",
    "        self._epoch = epoch\n",
    "        self._batch_size = batch_size\n",
    "\n",
    "        if self._dataset == 'mnist' or self._dataset == 'fashion-mnist':\n",
    "            # ground truth input size parameters\n",
    "            self._input_height = 28\n",
    "            self._input_width = 28\n",
    "            self._output_height = 28\n",
    "            self._output_width = 28\n",
    "\n",
    "            # dimension of noise-vector\n",
    "            self._z_dim = z_dim\n",
    "            self._c_dim = 1\n",
    "\n",
    "            # for train\n",
    "            self._lr = 0.0002\n",
    "            self._beta1 = 0.5\n",
    "\n",
    "            # save images of generator\n",
    "            self._sample_counts = 64\n",
    "\n",
    "            # load dataset for mnist\n",
    "            self._data_X, self._data_Y = load_mnist(self._dataset)\n",
    "\n",
    "            # cal batches\n",
    "            self._batch_nums = len(self._data_X) // self._batch_size\n",
    "        else:\n",
    "            raise NotImplementedError\n",
    "\n",
    "    def discriminator(self, x, is_training=True, reuse=False):\n",
    "        # build discriminator module\n",
    "        with tf.variable_scope(\"discriminator_module\", reuse=reuse):\n",
    "            net = lrelu(conv2d(x, 64, 4, 4, 2, 2, name='discriminator/conv1'))\n",
    "            net = lrelu(bn(conv2d(net, 128, 4, 4, 2, 2, name='discriminator/conv2'), is_training=is_training, scope='discriminator/bn2'))\n",
    "            net = tf.reshape(net, [self._batch_size, -1])\n",
    "            net = lrelu(bn(linear(net, 1024, scope='discriminator/fc3'), is_training=is_training, scope='discriminator/bn3'))\n",
    "            out_logit = linear(net, 1, scope='discriminator/fc4')\n",
    "            out = tf.nn.sigmoid(out_logit)\n",
    "            return out, out_logit, net\n",
    "\n",
    "    def generator(self, z, is_training=True, reuse=False):\n",
    "        # build generator module\n",
    "        with tf.variable_scope(\"generator_module\", reuse=reuse):\n",
    "            net = tf.nn.relu(bn(linear(z, 1024, scope='generator/fc1'), is_training=is_training, scope='generator/bn1'))\n",
    "            net = tf.nn.relu(bn(linear(net, 128 * 7 * 7, scope='generator/fc2'), is_training=is_training, scope='generator/bn2'))\n",
    "            net = tf.reshape(net, [self._batch_size, 7, 7, 128])\n",
    "            net = tf.nn.relu(\n",
    "                bn(deconv2d(net, [self._batch_size, 14, 14, 64], 4, 4, 2, 2, name='generator/dc3'), is_training=is_training, scope='generator/bn3'))\n",
    "            out = tf.nn.sigmoid(deconv2d(net, [self._batch_size, 28, 28, 1], 4, 4, 2, 2, name='generator/dc4'))\n",
    "            return out\n",
    "\n",
    "    def build_model(self):\n",
    "\n",
    "        \"\"\" Ground Truth Forward \"\"\"\n",
    "        input_dims = [self._input_height, self._input_width, self._c_dim]\n",
    "        \n",
    "        self._inputs = tf.placeholder(tf.float32, [self._batch_size] + input_dims, name='real_images')\n",
    "        D_real, D_real_logits, _ = self.discriminator(self._inputs, is_training=True, reuse=False)\n",
    "        \n",
    "        \n",
    "        \"\"\" Random Noises Forward \"\"\"\n",
    "        self._z = tf.placeholder(tf.float32, [self._batch_size, self._z_dim], name='z')\n",
    "        G = self.generator(self._z, is_training=True, reuse=False)\n",
    "        D_fake, D_fake_logits, _ = self.discriminator(G, is_training=True, reuse=True)\n",
    "        \n",
    "        \n",
    "        \"\"\" cal discriminator loss \"\"\"\n",
    "        d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_real_logits, labels=tf.ones_like(D_real)))\n",
    "        d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_fake_logits, labels=tf.zeros_like(D_fake)))\n",
    "        self.D_loss = d_loss_real + d_loss_fake\n",
    "\n",
    "        \n",
    "        \"\"\" cal generator loss \"\"\"\n",
    "        self.G_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_fake_logits, labels=tf.ones_like(D_fake)))\n",
    "\n",
    "        \n",
    "        \"\"\" Training \"\"\"\n",
    "        trainable_variables = tf.trainable_variables()  # collect D, G weights\n",
    "        d_vars = [var for var in trainable_variables if 'discriminator/' in var.name]\n",
    "        g_vars = [var for var in trainable_variables if 'generator/' in var.name]\n",
    "        with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n",
    "            self.d_optim = tf.train.AdamOptimizer(self._lr, beta1=self._beta1).minimize(self.D_loss, var_list=d_vars)\n",
    "            self.g_optim = tf.train.AdamOptimizer(self._lr * 5, beta1=self._beta1).minimize(self.G_loss, var_list=g_vars)\n",
    "\n",
    "\n",
    "        \"\"\"\" Testing \"\"\"\n",
    "        self.generator_images = self.generator(self._z, is_training=False, reuse=True)\n",
    "\n",
    "    def train(self):\n",
    "        self.saver = tf.train.Saver()\n",
    "\n",
    "        # sample noise inputs\n",
    "        self.sample_z = np.random.uniform(-1, 1, size=(self._batch_size, self._z_dim))\n",
    "\n",
    "        # loop for epoch\n",
    "        start_epoch = 0\n",
    "        start_batch_id = 0\n",
    "        counter = 1\n",
    "        start_time = time.time()\n",
    "        tf.global_variables_initializer().run()\n",
    "        for epoch in range(start_epoch, self._epoch):\n",
    "            for idx in range(start_batch_id, self._batch_nums):\n",
    "                batch_images = self._data_X[idx * self._batch_size:(idx + 1) * self._batch_size]\n",
    "                batch_z = np.random.uniform(-1, 1, [self._batch_size, self._z_dim]).astype(np.float32)\n",
    "                # cal d,g loss\n",
    "                _, d_loss = self._sess.run([self.d_optim, self.D_loss], feed_dict={self._inputs: batch_images, self._z: batch_z})\n",
    "                _, g_loss = self._sess.run([self.g_optim, self.G_loss], feed_dict={self._z: batch_z})\n",
    "                # training progress\n",
    "                counter += 1\n",
    "                print(\"Epoch: [%2d] [%4d/%4d] time: %4.4f, d_loss: %.8f, g_loss: %.8f\"% (epoch, idx, self._batch_nums, time.time() - start_time, d_loss, g_loss))\n",
    "                if np.mod(counter, 300) == 0:\n",
    "                    samples = self._sess.run(self.generator_images, feed_dict={self._z: self.sample_z})\n",
    "                    tot_num_samples = min(self._sample_counts, self._batch_size)\n",
    "                    manifold_h = int(np.floor(np.sqrt(tot_num_samples)))\n",
    "                    manifold_w = int(np.floor(np.sqrt(tot_num_samples)))\n",
    "                    save_images(samples[:manifold_h * manifold_w, :, :, :], [manifold_h, manifold_w],\n",
    "                                './' + check_folder(self._res_dir + '/' + self.model_dir) + '/' + self._model_name + '_train_{:02d}_{:04d}.png'.format(epoch, idx))\n",
    "            start_batch_id = 0\n",
    "            self.visualize_results(epoch)\n",
    "        self.save(self._ckpt_dir, counter) # save model\n",
    "\n",
    "    def visualize_results(self, epoch):\n",
    "        tot_num_samples = min(self._sample_counts, self._batch_size)\n",
    "        image_frame_dim = int(np.floor(np.sqrt(tot_num_samples)))\n",
    "        z_sample = np.random.uniform(-1, 1, size=(self._batch_size, self._z_dim)) # random noise\n",
    "        samples = self._sess.run(self.generator_images, feed_dict={self._z: z_sample})\n",
    "        save_images(samples[:image_frame_dim * image_frame_dim, :, :, :], [image_frame_dim, image_frame_dim],\n",
    "                    check_folder(self._res_dir + '/' + self.model_dir) + '/' + self._model_name + '_epoch%03d' % epoch + '_test_all_classes.png')\n",
    "\n",
    "    @property\n",
    "    def model_dir(self):\n",
    "        return \"{}_{}_{}_{}\".format(\n",
    "            self._model_name, self._dataset,\n",
    "            self._batch_size, self._z_dim)\n",
    "\n",
    "    def save(self, checkpoint_dir, step):\n",
    "        checkpoint_dir = os.path.join(checkpoint_dir, self.model_dir, self._model_name)\n",
    "        if not os.path.exists(checkpoint_dir):\n",
    "            os.makedirs(checkpoint_dir)\n",
    "        self.saver.save(self._sess, os.path.join(checkpoint_dir, self._model_name + '.model'), global_step=step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data/mnist\n",
      "reading gz data ./data/mnist/train-images-idx3-ubyte.gz\n",
      "reading gz data ./data/mnist/train-labels-idx1-ubyte.gz\n",
      "reading gz data ./data/mnist/t10k-images-idx3-ubyte.gz\n",
      "reading gz data ./data/mnist/t10k-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From lib/ops.py:35: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
      "\n",
      "WARNING:tensorflow:From lib/ops.py:36: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/Codespaces/miniconda3/envs/gan_env/lib/python2.7/site-packages/tensorflow/python/ops/nn_impl.py:180: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "Epoch: [ 0] [   0/ 546] time: 2.7983, d_loss: 1.43293023, g_loss: 0.67543036\n",
      "Epoch: [ 0] [   1/ 546] time: 2.9806, d_loss: 1.44356513, g_loss: 0.68639791\n",
      "Epoch: [ 0] [   2/ 546] time: 3.3457, d_loss: 1.42303538, g_loss: 0.70779729\n",
      "Epoch: [ 0] [   3/ 546] time: 3.7443, d_loss: 1.40485501, g_loss: 0.72196198\n",
      "Epoch: [ 0] [   4/ 546] time: 3.9088, d_loss: 1.39297056, g_loss: 0.73427999\n",
      "Epoch: [ 0] [   5/ 546] time: 4.0516, d_loss: 1.37945294, g_loss: 0.74004912\n",
      "Epoch: [ 0] [   6/ 546] time: 4.4346, d_loss: 1.38193369, g_loss: 0.74890316\n",
      "Epoch: [ 0] [   7/ 546] time: 4.8362, d_loss: 1.37198722, g_loss: 0.75251907\n",
      "Epoch: [ 0] [   8/ 546] time: 5.0281, d_loss: 1.36477208, g_loss: 0.75367260\n",
      "Epoch: [ 0] [   9/ 546] time: 5.3533, d_loss: 1.36513281, g_loss: 0.74843025\n",
      "Epoch: [ 0] [  10/ 546] time: 5.6959, d_loss: 1.35600150, g_loss: 0.75242615\n",
      "Epoch: [ 0] [  11/ 546] time: 5.9738, d_loss: 1.35253882, g_loss: 0.75518113\n",
      "Epoch: [ 0] [  12/ 546] time: 6.2552, d_loss: 1.34359980, g_loss: 0.76328921\n",
      "Epoch: [ 0] [  13/ 546] time: 6.5843, d_loss: 1.33418787, g_loss: 0.76574206\n",
      "Epoch: [ 0] [  14/ 546] time: 6.8844, d_loss: 1.33272552, g_loss: 0.76106459\n",
      "Epoch: [ 0] [  15/ 546] time: 7.2764, d_loss: 1.31967258, g_loss: 0.76828736\n",
      "Epoch: [ 0] [  16/ 546] time: 7.6351, d_loss: 1.33657241, g_loss: 0.76825851\n",
      "Epoch: [ 0] [  17/ 546] time: 7.9858, d_loss: 1.32746553, g_loss: 0.76866388\n",
      "Epoch: [ 0] [  18/ 546] time: 8.4944, d_loss: 1.31129181, g_loss: 0.77888954\n",
      "Epoch: [ 0] [  19/ 546] time: 8.8240, d_loss: 1.31434941, g_loss: 0.78000462\n",
      "Epoch: [ 0] [  20/ 546] time: 9.3575, d_loss: 1.31078005, g_loss: 0.78043026\n",
      "Epoch: [ 0] [  21/ 546] time: 9.7142, d_loss: 1.28971791, g_loss: 0.78867233\n",
      "Epoch: [ 0] [  22/ 546] time: 9.8875, d_loss: 1.29236484, g_loss: 0.79197448\n",
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      "Epoch: [ 0] [  27/ 546] time: 11.7456, d_loss: 1.25887144, g_loss: 0.80582047\n",
      "Epoch: [ 0] [  28/ 546] time: 11.9538, d_loss: 1.25596476, g_loss: 0.81992948\n",
      "Epoch: [ 0] [  29/ 546] time: 12.0996, d_loss: 1.26334071, g_loss: 0.82151103\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " [*] Training finished!\n",
      " [*] Testing finished!\n"
     ]
    }
   ],
   "source": [
    "!rm -rf ./checkpoint\n",
    "\n",
    "with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:\n",
    "    gan = GAN(sess, epoch=2, batch_size=128, z_dim=62, dataset='mnist', model_name='GAN', checkpoint_dir='checkpoint', result_dir='results', logs='logs')\n",
    "    gan.build_model()\n",
    "    gan.train()\n",
    "    print(\" [*] Training finished!\")\n",
    "    gan.visualize_results(1)\n",
    "    print(\" [*] Testing finished!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py2(tf1)",
   "language": "python",
   "name": "gan_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.18"
  },
  "vscode": {
   "interpreter": {
    "hash": "d4d1e4263499bec80672ea0156c357c1ee493ec2b1c70f0acce89fc37c4a6abe"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}