learn_08_GAN_DCGAN_implementation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DCGAN Implementation\n",
    "- 使用image->mnist image\n",
    "- 使用random vector\n",
    "- 输出：img(类似于mnist中的)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From e:\\environments\\mytf\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting ./MNIST_DATA/train-images-idx3-ubyte.gz\n",
      "WARNING:tensorflow:From e:\\environments\\mytf\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting ./MNIST_DATA/train-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From e:\\environments\\mytf\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.one_hot on tensors.\n",
      "Extracting ./MNIST_DATA/t10k-images-idx3-ubyte.gz\n",
      "Extracting ./MNIST_DATA/t10k-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From e:\\environments\\mytf\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "1 Data provider\n",
    "    a.img data\n",
    "    b.random vector data\n",
    "2 build calculate graph\n",
    "    a.gnerator\n",
    "    b.discriminator\n",
    "    c.DCGAN(combine a. b.)\n",
    "3 training process\n",
    "\"\"\"\n",
    "import os\n",
    "import sys\n",
    "import pickle\n",
    "import tensorflow as tf\n",
    "from tensorflow import logging\n",
    "from tensorflow import gfile\n",
    "import numpy as np\n",
    "import pprint\n",
    "import math\n",
    "import random\n",
    "from PIL import Image\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "output_dir = './local_run'\n",
    "mnist_dir = './MNIST_DATA/'\n",
    "'''\n",
    "network 原因无法自动下载导入 手动创建目录导入\n",
    "mnist = input_data.read_data_sets(mnist_dir,one_hot=True)\n",
    "'''\n",
    "if not gfile.Exists(mnist_dir):\n",
    "    gfile.Exists(mnist_dir)\n",
    "mnist = input_data.read_data_sets(mnist_dir,one_hot=True)    \n",
    "if not gfile.Exists(output_dir):\n",
    "    gfile.MakeDirs(output_dir)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "(55000, 784)\n",
      "(10000, 784)\n",
      "(55000, 10)\n",
      "(10000, 10)\n"
     ]
    }
   ],
   "source": [
    "def get_default_params():\n",
    "    return tf.contrib.training.HParams(\n",
    "        v_dim = 100,\n",
    "        init_conv_size = 4,\n",
    "        g_channels = [128,64,32,1],\n",
    "        d_channels = [32,64,128,256],\n",
    "        batch_size = 128,\n",
    "        learning_rate = 1e-3,\n",
    "        beta1 = 0.9,\n",
    "        img_size = 32,#生成img size 32*32 ！= 784\n",
    "    )\n",
    "hps = get_default_params()\n",
    "print(hps.init_conv_size)\n",
    "# 28*28 = 784\n",
    "print(mnist.train.images.shape)\n",
    "print(mnist.test.images.shape)\n",
    "print(mnist.train.labels.shape)\n",
    "print(mnist.test.labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 32, 32, 1)\n",
      "(1, 100)\n"
     ]
    }
   ],
   "source": [
    "class Mnist_DCGANDataSet:\n",
    "    \"\"\"\n",
    "    对于mnist提供数据封装 同时提供random vector\n",
    "    提供next_batch 的 random vector + mnist img\n",
    "    \"\"\"\n",
    "    def __init__(self,mnist_img_data,v_dim,img_size,need_shuffle):\n",
    "        self._data = mnist_img_data\n",
    "        self._data_size = len(self._data)\n",
    "        self._v = np.random.standard_normal((self._data_size,v_dim))\n",
    "        \n",
    "        self._need_shuffle = need_shuffle\n",
    "        self._indicator = 0\n",
    "        '''img size 32*32 ！= 784 更新self._data'''\n",
    "        self._resize_mnist_img(img_size)\n",
    "        self._random_shuffle()\n",
    "        \n",
    "    def _random_shuffle(self):\n",
    "        idx = np.random.permutation(self._data_size)\n",
    "        '''只有np下的array才可以使用这种索引方式改变数据排列'''\n",
    "        self._v = self._v[idx]\n",
    "        self._data = self._data[idx]\n",
    "    \n",
    "    def _resize_mnist_img(self,img_size):\n",
    "        \"\"\"read mnist data and resize mnist img to goal size\n",
    "        1 numpy -> PIL Image\n",
    "        2 PIL Image resize\n",
    "        3 PIL Image -> numpy \n",
    "        \"\"\"\n",
    "        data = np.asarray(self._data*255,np.uint8)\n",
    "        # 784 - > 28*28\n",
    "        data = data.reshape((self._data_size,28,28))\n",
    "        resized_data = []\n",
    "        for i in range(self._data_size):\n",
    "            img = data[i]\n",
    "            img = Image.fromarray(img)\n",
    "            img = img.resize((img_size,img_size))\n",
    "            img = np.asarray(img)\n",
    "            # 需要通道信息 mnist单通道\n",
    "            img = img.reshape((img_size,img_size,1))\n",
    "            resized_data.append(img)\n",
    "        # ->(datasize,img_size,img_size,1)\n",
    "        resized_data = np.asarray(resized_data,np.float32)\n",
    "        resized_data = resized_data / 127.5 - 1\n",
    "        self._data = resized_data\n",
    "        \n",
    "    def next_batch(self,batch_size):\n",
    "        if batch_size > self._data_size:\n",
    "            raise Exception('batch_size is too large')\n",
    "        end_indicator = self._indicator + batch_size\n",
    "        if end_indicator > self._data_size:\n",
    "            if self._need_shuffle:\n",
    "                self._random_shuffle()\n",
    "            self._indicator = 0\n",
    "            end_indicator = batch_size\n",
    "        batch_data = self._data[self._indicator:end_indicator]\n",
    "        batch_v = self._v[self._indicator:end_indicator]\n",
    "        self._indicator = end_indicator\n",
    "        return batch_data,batch_v\n",
    "    \n",
    "mnist_dataset = Mnist_DCGANDataSet(mnist.train.images,\n",
    "                                  hps.v_dim,hps.img_size,True)\n",
    "batch_data,batch_v = mnist_dataset.next_batch(1)\n",
    "\n",
    "'''\n",
    "img = Image.fromarray(np.asarray((batch_data.reshape((32,32))+1)*127.5,\n",
    "                      np.uint8))\n",
    "img.show()    \n",
    "'''\n",
    "print(batch_data.shape)\n",
    "print(batch_v.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''calculate graph construct'''\n",
    "'''生成器'''\n",
    "def conv2d_transpose(inputs,out_channel,name,\n",
    "                    training,with_bn_relu=True):\n",
    "    \"\"\"\n",
    "    由于DCGAN中bn在初始层结束层没有 所以需要选择 \n",
    "    实现对于完整的反卷积层封装\n",
    "    \"\"\"\n",
    "    with tf.variable_scope(name):\n",
    "        conv2d_trans = tf.layers.conv2d_transpose(inputs,\n",
    "                                                 out_channel,\n",
    "                                                 [5,5],\n",
    "                                                 strides=(2,2),\n",
    "                                                 padding='SAME')\n",
    "        if with_bn_relu:\n",
    "            bn = tf.layers.batch_normalization(conv2d_trans,\n",
    "                                              training=training)\n",
    "            return tf.nn.relu(bn)\n",
    "        else:\n",
    "            return conv2d_trans\n",
    "\n",
    "class Generator:\n",
    "    \"\"\"生成器定义 借助反卷积\"\"\"\n",
    "    def __init__(self,channels,init_conv_size):\n",
    "        self._channels = channels\n",
    "        self._init_conv_size = init_conv_size\n",
    "        self._reuse = False\n",
    "        \n",
    "    def __call__(self,inputs,training):\n",
    "        \"\"\"魔法方法：实现对象像函数一样调用\"\"\"\n",
    "        # 保证无论是placeholder 还是 np数组都变成tensor\n",
    "        inputs = tf.convert_to_tensor(inputs)\n",
    "        with tf.variable_scope('generator',reuse=self._reuse):\n",
    "            \"\"\"\n",
    "            random v-(fc)->channel[0]*init_conv_size**2->reshape\n",
    "            \"\"\"\n",
    "            with tf.variable_scope('input2img_fc'):\n",
    "                fc = tf.layers.dense(inputs,\n",
    "                                    self._channels[0]*self._init_conv_size**2)\n",
    "                conv0 = tf.reshape(fc,\n",
    "                                   [-1,self._init_conv_size,\n",
    "                                    self._init_conv_size,\n",
    "                                    self._channels[0]])\n",
    "                bn0 = tf.layers.batch_normalization(conv0,training=training)\n",
    "                relu0 = tf.nn.relu(bn0)\n",
    "            \n",
    "            deconv_inputs = relu0\n",
    "            # 搭建反卷积层\n",
    "            for i in range(1,len(self._channels)):\n",
    "                with_bn_relu = (i!=len(self._channels)-1)\n",
    "                deconv_inputs = conv2d_transpose(deconv_inputs,\n",
    "                                                self._channels[i],\n",
    "                                                'deconv-%d'%i,\n",
    "                                                training,\n",
    "                                                with_bn_relu)\n",
    "                img_inputs = deconv_inputs\n",
    "                with tf.variable_scope('genetate_image'):\n",
    "                    imgs = tf.tanh(img_inputs)\n",
    "                    \n",
    "        self._reuse = True\n",
    "        '''成员变量保存所有生成器参数 GAN中分别训练所有变量'''\n",
    "        # 铜鼓get_collection 获取制定scope中的一些量 比如变量等 通过key关键字制定 \n",
    "        # 关键字保存在tf.GraphKeys....\n",
    "        self.variables = tf.get_collection(key=tf.GraphKeys.TRAINABLE_VARIABLES,\n",
    "                                          scope='generator')\n",
    "        return imgs\n",
    "    \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''判别器'''\n",
    "def conv2d(inputs,out_channel,name,training,leak=0.1):\n",
    "    \"\"\"统一经过bn 对于conv2d封装 这里使用leaky relu\"\"\"\n",
    "    def leaky_relu(x,leak=0.01,name ='leaky_relu'):\n",
    "        #单纯比较大下可以使用maximum\n",
    "        '''\n",
    "        tf.maximum(a,b) 返回a,b之间的最大值\n",
    "        tf.argmax(a,b) 返回在b维上a的最大值\n",
    "        tf.reduce_max(a) 返回在张量a上的最大值\n",
    "        '''\n",
    "        return tf.maximum(x,x*leak,name=name)\n",
    "    \n",
    "    with tf.variable_scope(name):\n",
    "        conv2d = tf.layers.conv2d(inputs,out_channel,\n",
    "                                 [5,5],strides=(2,2),\n",
    "                                 padding='SAME')\n",
    "        bn = tf.layers.batch_normalization(conv2d,training=training)\n",
    "        return leaky_relu(bn,leak=leak)\n",
    "\n",
    "class Discriminator:\n",
    "    def __init__(self,channels):\n",
    "        self._channels = channels\n",
    "        self._reuse = False\n",
    "    def __call__(self,inputs,training):\n",
    "        conv_inputs = tf.convert_to_tensor(inputs)\n",
    "        with tf.variable_scope('discriminator',reuse=self._reuse):\n",
    "            for i in range(len(self._channels)):\n",
    "                conv_inputs = conv2d(conv_inputs,self._channels[i],\n",
    "                                    'conv-%d'%i,training,0.1)\n",
    "            fc_inputs = conv_inputs\n",
    "            with tf.variable_scope('fc'):\n",
    "                flatten = tf.layers.flatten(fc_inputs)\n",
    "                # 生成器只需呀判断真假 所以输出为1/2 均可\n",
    "                logits = tf.layers.dense(flatten,2,name='logits')\n",
    "        self._reuse = True\n",
    "        self.variables = tf.get_collection(key=tf.GraphKeys.TRAINABLE_VARIABLES,\n",
    "                                          scope='discriminator')\n",
    "        return logits           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''定义DCGAN combine gen and dis'''\n",
    "class DCGAN:\n",
    "    \"\"\"build structure and build ops\"\"\"\n",
    "    def __init__(self,hps):\n",
    "        self._g_channels = hps.g_channels\n",
    "        self._d_channels = hps.d_channels\n",
    "        self._batch_size = hps.batch_size\n",
    "        self._init_conv_size = hps.init_conv_size\n",
    "        self._v_dim = hps.v_dim\n",
    "        self._img_size = hps.img_size\n",
    "        self._learning_rate = hps.learning_rate\n",
    "        self._beta1 = hps.beta1\n",
    "        \n",
    "        self._generator = Generator(self._g_channels,self._init_conv_size)\n",
    "        self._discriminator = Discriminator(self._d_channels)\n",
    "        \n",
    "    def build(self):\n",
    "        \"\"\"build structure and return placeholder loss acc等\"\"\"\n",
    "        self._v_placeholder = tf.placeholder(\n",
    "            tf.float32,(self._batch_size,self._v_dim))\n",
    "        self._img_placeholder = tf.placeholder(\n",
    "            tf.float32,(self._batch_size,self._img_size,self._img_size,1))\n",
    "        # output\n",
    "        self._generated_img = self._generator(self._v_placeholder,True)\n",
    "        '''\n",
    "        需要使用同一网络结构进行两次输入运算 而不是每个输入对应一个网络结构\n",
    "        所以需要用reuse 制定 网络结构重用\n",
    "        '''\n",
    "        fake_img_logits = self._discriminator(self._generated_img,True)\n",
    "        real_img_logits = self._discriminator(self._img_placeholder,True)\n",
    "        # loss **对于原有数学公式进行拆分 得到用tf自带函数实现的方式 技巧**\n",
    "        '''\n",
    "        Value passed to parameter 'labels' has DataType int8 \n",
    "        not in list of allowed values: int32, int64\n",
    "        '''\n",
    "        loss_on_fake_to_real = tf.reduce_mean(\n",
    "            tf.nn.sparse_softmax_cross_entropy_with_logits(\n",
    "                labels = tf.ones([self._batch_size],tf.int32),\n",
    "                logits = fake_img_logits))\n",
    "        \n",
    "        loss_on_real_to_real = tf.reduce_mean(\n",
    "            tf.nn.sparse_softmax_cross_entropy_with_logits(\n",
    "                labels = tf.ones([self._batch_size],tf.int32),\n",
    "                logits = real_img_logits))\n",
    "        \n",
    "        loss_on_fake_to_fake = tf.reduce_mean(\n",
    "            tf.nn.sparse_softmax_cross_entropy_with_logits(\n",
    "                labels = tf.zeros([self._batch_size],tf.int32),\n",
    "                logits = fake_img_logits))\n",
    "        \n",
    "        '''以下是新的tf中的管理变量的方式 使用collection'''\n",
    "        # 保存\n",
    "        tf.add_to_collection('g_losses',loss_on_fake_to_real)\n",
    "        tf.add_to_collection('d_losses',loss_on_real_to_real)\n",
    "        tf.add_to_collection('d_losses',loss_on_fake_to_fake)\n",
    "        # 提取\n",
    "        self._loss = {\n",
    "            'gen': tf.add_n(tf.get_collection('g_losses'),name='total_g_loss'),\n",
    "            'disc':tf.add_n(tf.get_collection('d_losses'),name='total_d_loss')\n",
    "        }\n",
    "        \n",
    "        return self._v_placeholder,self._img_placeholder,self._generated_img,self._loss\n",
    "    \n",
    "    def build_op(self):\n",
    "        \"\"\"return builded operation\n",
    "        must be called after build function\n",
    "        \"\"\"\n",
    "        g_optimizer = tf.train.AdamOptimizer(self._learning_rate,self._beta1)\n",
    "        d_optimizer = tf.train.AdamOptimizer(self._learning_rate,self._beta1)\n",
    "        '''分别执行不同的操作 借助gen disc中的variables'''\n",
    "        # apply_gradient minimize 等方法都可以应用在指定的变量上 很灵活\n",
    "        g_opt_op = g_optimizer.minimize(self._loss['gen'],\n",
    "                              var_list = self._generator.variables)\n",
    "        d_opt_op = d_optimizer.minimize(self._loss['disc'],\n",
    "                              var_list = self._discriminator.variables)\n",
    "        \n",
    "        '''\n",
    "        难点 控制而知运行顺序 使用tf高级操作 control_dependencies \n",
    "        中的操作在列表中操作执行后才会执行\n",
    "        no_op空操作 占位符存在\n",
    "        '''\n",
    "        with tf.control_dependencies([g_opt_op,d_opt_op]):\n",
    "            return tf.no_op(name='train_op')\n",
    "\n",
    "tf.reset_default_graph()\n",
    "dcgan = DCGAN(hps)\n",
    "v_placeholder,img_placeholder,generated_imgs,loss = dcgan.build()\n",
    "train_op = dcgan.build_op()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:step:   0,g_loss:0.979,d_loss:1.641\n",
      "INFO:tensorflow:step:   1,g_loss:1.001,d_loss:0.951\n",
      "INFO:tensorflow:step:   2,g_loss:16.916,d_loss:0.002\n",
      "INFO:tensorflow:step:   3,g_loss:18.290,d_loss:0.348\n",
      "INFO:tensorflow:step:   4,g_loss:13.416,d_loss:0.029\n",
      "INFO:tensorflow:step:   5,g_loss:0.677,d_loss:2.608\n",
      "INFO:tensorflow:step:   6,g_loss:17.501,d_loss:0.067\n",
      "INFO:tensorflow:step:   7,g_loss:20.445,d_loss:0.997\n",
      "INFO:tensorflow:step:   8,g_loss:18.123,d_loss:0.115\n",
      "INFO:tensorflow:step:   9,g_loss:11.285,d_loss:0.032\n",
      "INFO:tensorflow:step:  10,g_loss:0.583,d_loss:3.028\n",
      "INFO:tensorflow:step:  11,g_loss:13.094,d_loss:0.001\n",
      "INFO:tensorflow:step:  12,g_loss:17.781,d_loss:0.069\n",
      "INFO:tensorflow:step:  13,g_loss:19.219,d_loss:0.076\n",
      "INFO:tensorflow:step:  14,g_loss:19.378,d_loss:0.479\n",
      "INFO:tensorflow:step:  15,g_loss:17.565,d_loss:0.240\n",
      "INFO:tensorflow:step:  16,g_loss:13.000,d_loss:0.112\n",
      "INFO:tensorflow:step:  17,g_loss:5.579,d_loss:0.049\n",
      "INFO:tensorflow:step:  18,g_loss:0.062,d_loss:4.315\n",
      "INFO:tensorflow:step:  19,g_loss:12.351,d_loss:0.005\n",
      "INFO:tensorflow:step:  20,g_loss:17.840,d_loss:0.125\n",
      "INFO:tensorflow:step:  21,g_loss:19.978,d_loss:0.521\n",
      "INFO:tensorflow:step:  22,g_loss:20.738,d_loss:0.763\n",
      "INFO:tensorflow:step:  23,g_loss:20.883,d_loss:0.256\n",
      "INFO:tensorflow:step:  24,g_loss:20.658,d_loss:0.007\n",
      "INFO:tensorflow:step:  25,g_loss:20.231,d_loss:0.008\n",
      "INFO:tensorflow:step:  26,g_loss:19.518,d_loss:0.001\n",
      "INFO:tensorflow:step:  27,g_loss:18.480,d_loss:0.002\n",
      "INFO:tensorflow:step:  28,g_loss:16.958,d_loss:0.000\n",
      "INFO:tensorflow:step:  29,g_loss:14.218,d_loss:0.000\n",
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      "INFO:tensorflow:step:9999,g_loss:3.100,d_loss:0.092\n"
     ]
    }
   ],
   "source": [
    "'''train process'''\n",
    "def combine_graph(batch_imgs,img_size,rows=8,cols=16):\n",
    "    \"\"\"combine small imgs in a batch into a big img to show\n",
    "    Args:\n",
    "    - rows*clos = batch_size\n",
    "    \"\"\"\n",
    "    result_big_img = []\n",
    "    for i in range(rows):\n",
    "        row_imgs = []\n",
    "        for j in range(cols):\n",
    "            img = batch_imgs[i*cols+j]\n",
    "            img = img.reshape((img_size,img_size))\n",
    "            img = (img+1)*127.5\n",
    "            row_imgs.append(img)\n",
    "        '''hstack vstack减少一个维度'''\n",
    "        row_imgs = np.hstack(row_imgs)\n",
    "        result_big_img.append(row_imgs)\n",
    "    result_big_img = np.vstack(result_big_img)\n",
    "    result_big_img = np.asarray(result_big_img,np.uint8)\n",
    "    result_big_img = Image.fromarray(result_big_img)\n",
    "    return result_big_img\n",
    "\n",
    "init_op = tf.global_variables_initializer()\n",
    "train_steps = 10000\n",
    "sample_steps = 200\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init_op)\n",
    "    for i in range(train_steps):\n",
    "        need_sample = ((i+1)%sample_steps == 0)\n",
    "        batch_imgs,batch_v = mnist_dataset.next_batch(hps.batch_size)\n",
    "        fetches = [train_op,loss['gen'],loss['disc']]\n",
    "        \n",
    "        if need_sample:\n",
    "            fetches.append(generated_imgs)\n",
    "        outputs = sess.run(fetches,feed_dict={\n",
    "                            v_placeholder:batch_v,\n",
    "                            img_placeholder:batch_imgs})\n",
    "        \n",
    "        _,g_loss_val,d_loss_val = outputs[0:3]\n",
    "        logging.info('step:%4d,g_loss:%4.3f,d_loss:%4.3f'\n",
    "                     %(i,g_loss_val,d_loss_val))\n",
    "        if need_sample:\n",
    "            generated_imgs_val = outputs[-1]\n",
    "            gen_img_path = os.path.join(output_dir,'%04d-gen.jpg'%(i+1))\n",
    "            real_img_path = os.path.join(output_dir,'%04d-real.jpg'%(i+1))\n",
    "            \n",
    "            generated_img = combine_graph(generated_imgs_val,hps.img_size)\n",
    "            real_img = combine_graph(batch_imgs,hps.img_size)\n",
    "            # Image 对象调用保存函数\n",
    "            generated_img.save(gen_img_path)\n",
    "            real_img.save(real_img_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 问题：生成器学的很差 辨别器太强 对于这种情况如何调优？？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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