Improved_GAN_semi.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import module "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "this code is implementation of \"Improved Techniques for Training GANs\", but I didn't find a better implementation to reproduce the result of the generator. I'll be back to refine this notebook!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the network structure borrow from https://github.com/LDOUBLEV/semi-supervised-GAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import math\n",
    "import scipy\n",
    "import random\n",
    "import time\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading MNIST data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting data/mnist/train-images-idx3-ubyte.gz\n",
      "Extracting data/mnist/train-labels-idx1-ubyte.gz\n",
      "Extracting data/mnist/t10k-images-idx3-ubyte.gz\n",
      "Extracting data/mnist/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "os.environ['CUDA_VISIBLE_DEVICES']='1'\n",
    "mnist = input_data.read_data_sets(\"data/mnist\",one_hot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def bn(net,scope,is_training):\n",
    "    return tf.contrib.layers.batch_norm(net, decay=0.9,updates_collections=None, epsilon=1e-5,scale=True,\n",
    "                                            is_training=is_training, scope=scope)\n",
    "\n",
    "def conv(net, wscope, bscope, output_depth=64, receptive_field=[5,5], stride=[2,2], padding='SAME', isDiscriminator=False):\n",
    "    shape = net.get_shape()\n",
    "    weights = tf.get_variable(wscope, receptive_field+[shape[-1], output_depth],initializer=tf.truncated_normal_initializer(stddev=0.02))\n",
    "    if isDiscriminator:\n",
    "        tf.add_to_collection('regularizer', tf.contrib.layers.l2_regularizer(scale=0.0001)(weights)) # weight regularizer\n",
    "    biases = tf.get_variable(bscope, [output_depth], initializer=tf.constant_initializer(0.0))\n",
    "    net = tf.nn.conv2d(net, weights, strides=[1]+stride+[1], padding=padding)\n",
    "    net = tf.reshape(tf.nn.bias_add(net, biases), net.get_shape())\n",
    "    return net\n",
    "\n",
    "def linear(net,wscope,bscope,output_depth,isDiscriminator=False):\n",
    "    shape = net.get_shape()       \n",
    "    weights = tf.get_variable(wscope, [shape[-1], output_depth], tf.float32,tf.random_normal_initializer(stddev=0.02))\n",
    "    if isDiscriminator:\n",
    "        tf.add_to_collection('regularizer', tf.contrib.layers.l2_regularizer(scale=0.0001)(weights)) # weight regularizer\n",
    "    biases = tf.get_variable(bscope, [output_depth], initializer=tf.constant_initializer(0.0))\n",
    "    out_logit = tf.matmul(net, weights) + biases\n",
    "    return out_logit\n",
    "\n",
    "def deconv(net,wscope,bscope,output_shape,receptive_field=[5,5],stride=[2,2]):\n",
    "    weights = tf.get_variable(wscope, receptive_field+[output_shape[-1], net.get_shape()[-1]],\n",
    "                                       initializer=tf.random_normal_initializer(stddev=0.02))\n",
    "    #tf.add_to_collection('regularizer', tf.contrib.layers.l2_regularizer(scale=0.0001)(weights)) # weight regularizer\n",
    "    net = tf.nn.conv2d_transpose(net, weights, output_shape=output_shape, strides=[1]+stride+[1], padding='SAME')\n",
    "    biases = tf.get_variable(bscope, [output_shape[-1]], initializer=tf.constant_initializer(0.0))\n",
    "    net = tf.reshape(tf.nn.bias_add(net, biases), net.get_shape())\n",
    "    return net\n",
    "\n",
    "def get_shape(net):\n",
    "    return net.get_shape().as_list()\n",
    "\n",
    "def display(img_samples=None, batch_size=None, img_h=None, img_w=None, image_dims=None, step_ind=None):\n",
    "    \n",
    "    row = col = int(np.sqrt(batch_size))\n",
    "    img = np.zeros( [row*img_h, col*img_w] )\n",
    "    for i in range(row):\n",
    "        for j in range(col):\n",
    "            img[i*img_w:(i+1)*img_w, j*img_h:(j+1)*img_h] = img_samples[i*col+j, :].reshape(image_dims[:2])\n",
    "    #save the result      \n",
    "    scipy.misc.imsave('multi_{}.jpg'.format(step_ind),img)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discriminator Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# TODO: add gaussian noise to each layer\n",
    "def discriminator(inputs, batch_size, is_training=True, reuse=False):\n",
    "    \n",
    "    with tf.variable_scope(\"discriminator\", reuse=reuse):\n",
    "        outsli = []\n",
    "        \n",
    "        '''1st: Conv -> lrelu'''\n",
    "        net = conv(inputs,'d_wconv1','d_bconv1',64, [5,5], [2,2], 'SAME',True) # 28x28 -> 14x14 \n",
    "        net = tf.nn.leaky_relu(net)\n",
    "        outsli.append(net)\n",
    "        \n",
    "        '''2nd: Conv -> bn -> lrelu'''\n",
    "        net = conv(net,'d_wconv2','d_bconv2',64*2, [3,3], [2,2], 'SAME', True) # 14x14 -> 7x7\n",
    "        net = bn(net, scope = 'd_bn2',is_training=is_training)\n",
    "        net = tf.nn.leaky_relu(net)\n",
    "        outsli.append(net)\n",
    "        \n",
    "        '''3th: Conv -> bn -> lrelu'''\n",
    "        net = conv(net,'d_wconv3','d_bconv3',64*4, [3,3], [1,1], 'VALID', True) # 7x7 -> 5x5\n",
    "        net = bn(net, scope = 'd_bn3',is_training=is_training)\n",
    "        net = tf.nn.leaky_relu(net)\n",
    "        outsli.append(net)\n",
    "        \n",
    "        '''4th: Conv -> bn -> lrelu'''\n",
    "        net = conv(net,'d_wconv4','d_bconv4',64*4, [3,3], [2,2], 'VALID', True) # 5x5 -> 2x2\n",
    "        net = bn(net, scope = 'd_bn4',is_training=is_training)\n",
    "        net = tf.nn.leaky_relu(net)\n",
    "        outsli.append(net)\n",
    "        \n",
    "        net = tf.reshape(net, [batch_size, -1]) # batch_size x (2x2x64x4)\n",
    "\n",
    "        '''5th: linear '''\n",
    "        out_logit = linear(net,\"d_wlinear5\",\"d_blinear5\",11, True) # attention\n",
    "        '''6th: sigmoid'''\n",
    "        # out = tf.nn.sigmoid(out_logit)  \n",
    "        out = outsli\n",
    "\n",
    "        return out, out_logit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generator Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generator( z, batch_size, output_height=28, output_width=28,output_depth=1, is_training=True, reuse=False):\n",
    "    \n",
    "    height0,width0 = output_height,output_width # 28,28\n",
    "    height2,width2 = math.ceil(float(height0)/2),math.ceil(float(width0)/2) # 14,14\n",
    "    height4,width4 = math.ceil(float(height2)/2),math.ceil(float(width2)/2) # 7,7\n",
    "    height8,width8 = math.ceil(float(height4)/2),math.ceil(float(width4)/2) # 4,4\n",
    "    height16,width16 = math.ceil(float(height8)/2),math.ceil(float(width8)/2) # 2,2\n",
    "    \n",
    "    with tf.variable_scope(\"generator\", reuse=reuse):\n",
    "        \n",
    "        '''1st: linear -> reshape -> bn -> relu '''\n",
    "        net = linear(z, \"g_wlinear1\", \"g_blinear1\", 64*height16*width16)\n",
    "        net = tf.reshape(net,[batch_size, height16, width16, 64]) # -1,2,2,64\n",
    "        net = bn(net,scope='g_bn1',is_training=is_training)\n",
    "        net = tf.nn.relu(net)  \n",
    "    \n",
    "        '''2nd: deconv -> bn -> relu '''\n",
    "        output_shape = [batch_size, height8, width8, 64*4] # -1,4,4,64*4\n",
    "        net = deconv(net,'g_wdeconv2','g_bdeconv2',output_shape,[5,5],[2,2])\n",
    "        net = bn(net,scope='g_bn2',is_training=is_training)\n",
    "        net = tf.nn.relu(net)\n",
    "        \n",
    "        '''3th: deconv -> bn -> relu '''\n",
    "        output_shape = [batch_size, height4, width4, 64*2] # -1,7,7,64*2\n",
    "        net = deconv(net,'g_wdeconv3','g_bdeconv3',output_shape,[5,5],[2,2])\n",
    "        net = bn(net,scope='g_bn3',is_training=is_training)\n",
    "        net = tf.nn.relu(net)\n",
    "       \n",
    "        '''4th: deconv -> bn -> relu '''\n",
    "        output_shape = [batch_size, height2, width2, 64*2] # -1,14,14,64\n",
    "        net = deconv(net,'g_wdeconv4','g_bdeconv4',output_shape,[5,5])\n",
    "        net = bn(net,scope='g_bn4',is_training=is_training)\n",
    "        net = tf.nn.relu(net)\n",
    "        \n",
    "        '''5th: deconv -> tanh '''\n",
    "        output_shape = [batch_size, height0, width0, 1*1] # -1,28,28,1\n",
    "        net = deconv(net,'g_wdeconv5','g_bdeconv5',output_shape,[5,5])\n",
    "        out = tf.nn.sigmoid(net)\n",
    "\n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# set the global parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# some parameters\n",
    "img_w = 28\n",
    "img_h = 28\n",
    "image_dims = [28, 28, 1]\n",
    "batch_size = 64\n",
    "z_dim = 128\n",
    "y_dim = 10\n",
    "num_class = y_dim + 1\n",
    "learning_rate = 0.0002 #0.001\n",
    "beta1 = 0.5\n",
    "output_height,output_width,output_depth = [28,28,1]\n",
    "\n",
    "\"\"\" Graph Input \"\"\"\n",
    "# images\n",
    "inputs_supervised = tf.placeholder(tf.float32, [batch_size] + image_dims, name='supervised_images')\n",
    "inputs_unsupervised = tf.placeholder(tf.float32, [batch_size] + image_dims, name='unsupervised_images')\n",
    "\n",
    "#labels\n",
    "y = tf.placeholder(tf.float32, [batch_size,y_dim], name='y_label')\n",
    "# noises\n",
    "z = tf.placeholder(tf.float32, [batch_size, z_dim], name='z_noise')\n",
    "\n",
    "flag = tf.placeholder(tf.float32, shape=[], name='flag')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loss Function "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "different author has different implementation, you should choice one style of them"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### below loss function structure borrowed from  \n",
    "https://github.com/LDOUBLEV/semi-supervised-GAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-8-19ab26b49293>:31: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "\n",
      "Future major versions of TensorFlow will allow gradients to flow\n",
      "into the labels input on backprop by default.\n",
      "\n",
      "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\"\"\" Loss Function \"\"\"\n",
    "\n",
    "'''define the network'''\n",
    "# output of D for real images\n",
    "layerlist_real, d_logits_real = discriminator(inputs_supervised, batch_size, is_training=True, reuse=False)\n",
    "\n",
    "# output of D for fake images\n",
    "g_img = generator(z, batch_size, is_training=True, reuse=False)\n",
    "layerlist_fake, d_logits_fake = discriminator(g_img, batch_size, is_training=True, reuse=True)\n",
    "\n",
    "'''weight normalization for discriminator'''\n",
    "d_regular = tf.add_n(tf.get_collection('regularizer', 'discriminator'), 'loss')  # D regular loss\n",
    "\n",
    "    \n",
    "\n",
    "'''caculate the unsupervised loss'''\n",
    "unsupervised_label_real = tf.concat([tf.ones_like(y), tf.zeros(shape=(batch_size, 1))], axis=1)\n",
    "unsupervised_label_fake = tf.concat([tf.zeros_like(y), tf.ones(shape=(batch_size, 1))], axis=1)\n",
    "# logits_r, logits_f = tf.nn.softmax(d_logits_real), tf.nn.softmax(d_logits_fake)\n",
    "\n",
    "d_unloss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=unsupervised_label_real*0.9, logits=d_logits_real))\n",
    "d_unloss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=unsupervised_label_fake*0.9, logits=d_logits_fake))\n",
    "\n",
    "'''feature match'''\n",
    "feature_match = tf.constant(0, dtype=tf.float32)\n",
    "tmp = [tf.reduce_mean(tf.multiply(r-f, r-f))  for r,f in zip(layerlist_real, layerlist_fake)]\n",
    "for i in tmp: feature_match += i\n",
    "\n",
    "'''caculate the supervised loss'''\n",
    "supervised_label = tf.concat([y,tf.zeros(shape=(batch_size, 1))], axis=1)\n",
    "supervised_loss_real = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=supervised_label, logits=d_logits_real))\n",
    "supervised_loss_fake = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=unsupervised_label_fake*0.9, logits=d_logits_fake))  # same as d_loss_f\n",
    "\n",
    "d_loss_1, d_loss_2 = d_unloss_real - d_unloss_fake, supervised_loss_real\n",
    "\n",
    "''' loss of discriminator'''\n",
    "supervised_loss = supervised_loss_real + supervised_loss_fake\n",
    "unsupervised_loss = d_unloss_real + d_unloss_fake\n",
    "\n",
    "d_loss = unsupervised_loss + supervised_loss_real*flag + d_regular \n",
    "\n",
    "''' loss of generator '''\n",
    "g_loss = d_unloss_fake + 0.5*feature_match\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### below loss function structure borrowed from    \n",
    "https://github.com/bruno-31/ImprovedGAN-Tensorflow/blob/master/train_cifar.py and  \n",
    "https://github.com/openai/improved-gan/blob/master/mnist_svhn_cifar10/train_mnist_feature_matching.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" Loss Function \"\"\"\n",
    "\n",
    "'''define the network'''\n",
    "# output of D for real images\n",
    "layerlist_real, d_logits_real = discriminator(inputs_supervised, batch_size, is_training=True, reuse=False)\n",
    "\n",
    "# output of D for fake images\n",
    "g_img = generator(z, batch_size, is_training=True, reuse=False)\n",
    "layerlist_fake, d_logits_fake = discriminator(g_img, batch_size, is_training=True, reuse=True)\n",
    "\n",
    "layerlist_real_unsupervised, d_logits_unsupervised = discriminator(inputs_unsupervised, batch_size, is_training=True, reuse=True)\n",
    "\n",
    "\n",
    "'''caculate the unsupervised loss'''\n",
    "unsupervised_label_real = tf.concat([tf.ones_like(y), tf.zeros(shape=(batch_size, 1))], axis=1)\n",
    "unsupervised_label_fake = tf.concat([tf.zeros_like(y), tf.ones(shape=(batch_size, 1))], axis=1)\n",
    "# logits_r, logits_f = tf.nn.softmax(d_logits_real), tf.nn.softmax(d_logits_fake)\n",
    "\n",
    "d_unloss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=unsupervised_label_real*0.9, logits=d_logits_real))\n",
    "d_unloss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=unsupervised_label_fake*0.9, logits=d_logits_fake))\n",
    "\n",
    "'''feature match'''\n",
    "feature_match = tf.constant(0, dtype=tf.float32)\n",
    "tmp = [tf.reduce_mean(tf.multiply(r-f, r-f))  for r,f in zip(layerlist_real, layerlist_fake)]\n",
    "for i in tmp: feature_match += i\n",
    "\n",
    "'''caculate the supervised loss'''\n",
    "supervised_label = tf.concat([y,tf.zeros(shape=(batch_size, 1))], axis=1)\n",
    "supervised_loss_real = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=supervised_label, logits=d_logits_real))\n",
    "supervised_loss_fake = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=unsupervised_label_fake*0.9, logits=d_logits_fake))  # same as d_loss_f\n",
    "\n",
    "\n",
    "''' loss of discriminator'''\n",
    "\n",
    "l_unl = tf.reduce_logsumexp(d_logits_unsupervised, axis=1)\n",
    "l_gen = tf.reduce_logsumexp(d_logits_fake, axis=1)\n",
    "supervised_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=supervised_label, logits=d_logits_real))\n",
    "unsupervised_loss = - 0.5 * tf.reduce_mean(l_unl) \\\n",
    "                   + 0.5 * tf.reduce_mean(tf.nn.softplus(l_unl)) \\\n",
    "                   + 0.5 * tf.reduce_mean(tf.nn.softplus(l_gen))\n",
    "\n",
    "d_loss = unsupervised_loss + supervised_loss\n",
    "\n",
    "''' loss of generator '''\n",
    "g_loss = feature_match\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "split the generator parameters and discriminator parameters into two list, then define how to train the two subnetwork and get the fake image for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\" Training \"\"\"\n",
    "# divide trainable variables into a group for D and a group for G\n",
    "t_vars = tf.trainable_variables()\n",
    "d_vars = [var for var in t_vars if 'd_' in var.name]\n",
    "g_vars = [var for var in t_vars if 'g_' in var.name]\n",
    "# optimizers\n",
    "d_optim = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)\n",
    "g_optim = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)\n",
    "\n",
    "\n",
    "\"\"\"\" Testing \"\"\"\n",
    "# for test\n",
    "_, test_logits = discriminator(inputs_supervised, batch_size, is_training=False, reuse=True)\n",
    "test_labels_pred = tf.nn.softmax(test_logits)\n",
    "temp = tf.reshape(test_labels_pred[:, -1], shape=[batch_size, 1])\n",
    "for i in range(10):\n",
    "    temp = tf.concat([temp, tf.reshape(test_labels_pred[:, -1],shape=[batch_size, 1])], axis=1)\n",
    "test_labels_pred -= temp\n",
    "prediction = tf.nn.in_top_k(test_labels_pred, tf.argmax(supervised_label, axis=1), 1)\n",
    "\n",
    "\n",
    "fake_images = generator(z, batch_size, is_training=False, reuse=True)\n",
    "\n",
    "# graph inputs for visualize training results\n",
    "sample_z = np.random.uniform(-1, 1, size=(batch_size , z_dim)).astype(np.float32)\n",
    "\n",
    "test_labels_onehot = np.zeros([batch_size, y_dim],dtype = np.float32)\n",
    "test_labels_onehot[np.arange(batch_size), np.arange(batch_size)%int(y_dim)] = 1.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training a GAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step: [0] d_loss: 2.74950695, g_loss: 1.49454856\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/python3/lib/python3.6/site-packages/ipykernel_launcher.py:44: DeprecationWarning: `imsave` is deprecated!\n",
      "`imsave` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``imageio.imwrite`` instead.\n"
     ]
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      "Step: [5965] d_loss: 0.42334458, g_loss: 1.06738806\n",
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      "Step: [5967] d_loss: 0.41750610, g_loss: 1.06508124\n",
      "Step: [5968] d_loss: 0.41951534, g_loss: 1.02788329\n",
      "Step: [5969] d_loss: 0.42031214, g_loss: 1.02738595\n",
      "Step: [5970] d_loss: 0.42252183, g_loss: 1.08247364\n",
      "Step: [5971] d_loss: 0.42443985, g_loss: 1.01759839\n",
      "Step: [5972] d_loss: 0.41711068, g_loss: 1.04393578\n",
      "Step: [5973] d_loss: 0.41957030, g_loss: 1.10346711\n",
      "Step: [5974] d_loss: 0.41966537, g_loss: 1.05003297\n",
      "Step: [5975] d_loss: 0.41939989, g_loss: 1.06679320\n",
      "Step: [5976] d_loss: 0.42103767, g_loss: 1.06302714\n",
      "Step: [5977] d_loss: 0.41940290, g_loss: 1.03257763\n",
      "Step: [5978] d_loss: 0.41435933, g_loss: 1.04443526\n",
      "Step: [5979] d_loss: 0.41665491, g_loss: 1.04943419\n",
      "Step: [5980] d_loss: 0.41754273, g_loss: 1.10278094\n",
      "Step: [5981] d_loss: 0.41804671, g_loss: 1.06343913\n",
      "Step: [5982] d_loss: 0.41573051, g_loss: 1.01342344\n",
      "Step: [5983] d_loss: 0.41842619, g_loss: 1.03773069\n",
      "Step: [5984] d_loss: 0.41955206, g_loss: 1.04546273\n",
      "Step: [5985] d_loss: 0.41892791, g_loss: 1.02304196\n",
      "Step: [5986] d_loss: 0.42021507, g_loss: 1.07220125\n",
      "Step: [5987] d_loss: 0.41850242, g_loss: 1.08105755\n",
      "Step: [5988] d_loss: 0.41776493, g_loss: 1.04896641\n",
      "Step: [5989] d_loss: 0.41542277, g_loss: 1.03302765\n",
      "Step: [5990] d_loss: 0.42026633, g_loss: 1.08847427\n",
      "Step: [5991] d_loss: 0.41747612, g_loss: 1.09427345\n",
      "Step: [5992] d_loss: 0.41864580, g_loss: 1.08661842\n",
      "Step: [5993] d_loss: 0.41882846, g_loss: 1.08221829\n",
      "Step: [5994] d_loss: 0.42190775, g_loss: 1.06237721\n",
      "Step: [5995] d_loss: 0.42005280, g_loss: 1.08070052\n",
      "Step: [5996] d_loss: 0.42030248, g_loss: 1.07950878\n",
      "Step: [5997] d_loss: 0.41823509, g_loss: 1.07131839\n",
      "Step: [5998] d_loss: 0.41810471, g_loss: 1.05212867\n",
      "Step: [5999] d_loss: 0.41686156, g_loss: 1.07683158\n",
      "testing................\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'sbatch_size' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-e7323faea0b0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'testing................'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10000\u001b[0m\u001b[0;34m//\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m     \u001b[0mtestx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtextl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmnist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m     \u001b[0mprediction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0minputs_supervised\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mtestx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mtextl\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m     \u001b[0mcount\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'sbatch_size' is not defined"
     ]
    }
   ],
   "source": [
    "num_steps = 6000\n",
    "# loop for epoch\n",
    "start_time = time.time()\n",
    "sess = tf.Session()\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "for step_ind in range(num_steps):\n",
    "    \n",
    "    '''get the real data'''\n",
    "    real_image_batch = mnist.train.next_batch(batch_size)\n",
    "    batch_images,batch_labels = real_image_batch\n",
    "    batch_images = batch_images.reshape([batch_size]+image_dims).astype(np.float32)\n",
    "    batch_labels = real_image_batch[1].astype(np.float32)\n",
    "    '''get the noise data'''\n",
    "    batch_z = np.random.uniform(-1, 1, [batch_size, z_dim]).astype(np.float32)\n",
    "    \n",
    "    flag_input = 1 if step_ind < 400 else 0 \n",
    "\n",
    "    feed_dict_input = {inputs_supervised:batch_images,inputs_unsupervised:batch_images, z:batch_z, y:batch_labels, flag:1}\n",
    "    # update D network\n",
    "    _ , D_loss, d1, d2 = sess.run([d_optim, d_loss, d_loss_1, d_loss_2], feed_dict=feed_dict_input)\n",
    "    _ , D_loss, d1, d2 = sess.run([d_optim, d_loss, d_loss_1, d_loss_2], feed_dict=feed_dict_input)\n",
    "\n",
    "    # update G network\n",
    "    _, G_loss = sess.run([g_optim,g_loss], feed_dict=feed_dict_input)\n",
    "   # _, G_loss = sess.run([g_optim,g_loss], feed_dict=feed_dict_input)\n",
    "\n",
    "    # display training status\n",
    "    print(\"Step: [%d] d_loss: %.8f, g_loss: %.8f\" % (step_ind, D_loss, G_loss) )\n",
    "\n",
    "    # save training results for every 300 steps\n",
    "    if np.mod(step_ind, 300) == 0:\n",
    "        samples = sess.run(fake_images, feed_dict={z: sample_z})\n",
    "        display(samples, batch_size, img_h, img_w, image_dims, step_ind)\n",
    "        \n",
    "''' testing the di'''       \n",
    "print('testing................')\n",
    "count = 0\n",
    "for i in range(10000//batch_size):\n",
    "    testx, textl = mnist.test.next_batch(batch_size)\n",
    "    prediction1 = sess.run(prediction, feed_dict={inputs_supervised:testx, y:textl})\n",
    "    count += np.sum(prediction1)\n",
    "print(f'acc: {count/10000}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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