Base/frameworks/keras/demo/Keras_GAN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [机器之心GitHub项目：GAN完整理论推导与实现](https://zhuanlan.zhihu.com/p/29837245)\n",
    "\n",
    "[原notebookGITHUB](https://github.com/jiqizhixin/ML-Tutorial-Experiment/blob/master/Experiments/Keras_GAN.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Reshape\n",
    "from keras.layers.core import Activation\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.convolutional import UpSampling2D\n",
    "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
    "from keras.layers.core import Flatten\n",
    "from keras.optimizers import SGD\n",
    "from keras.datasets import mnist\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import argparse\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generator_model():\n",
    "    #下面搭建生成器的架构，首先导入序贯模型（sequential），即多个网络层的线性堆叠\n",
    "    model = Sequential()\n",
    "    #添加一个全连接层，输入为100维向量，输出为1024维\n",
    "    model.add(Dense(input_dim=100, output_dim=1024))\n",
    "    #添加一个激活函数tanh\n",
    "    model.add(Activation('tanh'))\n",
    "    #添加一个全连接层，输出为128×7×7维度\n",
    "    model.add(Dense(128*7*7))\n",
    "    #添加一个批量归一化层，该层在每个batch上将前一层的激活值重新规范化，即使得其输出数据的均值接近0，其标准差接近1\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Activation('tanh'))\n",
    "    #Reshape层用来将输入shape转换为特定的shape，将含有128*7*7个元素的向量转化为7×7×128张量\n",
    "    model.add(Reshape((7, 7, 128), input_shape=(128*7*7,)))\n",
    "    #2维上采样层，即将数据的行和列分别重复2次\n",
    "    model.add(UpSampling2D(size=(2, 2)))\n",
    "    #添加一个2维卷积层，卷积核大小为5×5，激活函数为tanh，共64个卷积核，并采用padding以保持图像尺寸不变\n",
    "    model.add(Conv2D(64, (5, 5), padding='same'))\n",
    "    model.add(Activation('tanh'))\n",
    "    model.add(UpSampling2D(size=(2, 2)))\n",
    "    #卷积核设为1即输出图像的维度\n",
    "    model.add(Conv2D(1, (5, 5), padding='same'))\n",
    "    model.add(Activation('tanh'))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def discriminator_model():\n",
    "    #下面搭建判别器架构，同样采用序贯模型\n",
    "    model = Sequential()\n",
    "    \n",
    "    #添加2维卷积层，卷积核大小为5×5，激活函数为tanh，输入shape在‘channels_first’模式下为（samples,channels，rows，cols）\n",
    "    #在‘channels_last’模式下为（samples,rows,cols,channels），输出为64维\n",
    "    model.add(\n",
    "            Conv2D(64, (5, 5),\n",
    "            padding='same',\n",
    "            input_shape=(28, 28, 1))\n",
    "            )\n",
    "    model.add(Activation('tanh'))\n",
    "    #为空域信号施加最大值池化，pool_size取（2，2）代表使图片在两个维度上均变为原长的一半\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Conv2D(128, (5, 5)))\n",
    "    model.add(Activation('tanh'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    #Flatten层把多维输入一维化，常用在从卷积层到全连接层的过渡\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(1024))\n",
    "    model.add(Activation('tanh'))\n",
    "    #一个结点进行二值分类，并采用sigmoid函数的输出作为概念\n",
    "    model.add(Dense(1))\n",
    "    model.add(Activation('sigmoid'))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generator_containing_discriminator(g, d):\n",
    "    #将前面定义的生成器架构和判别器架构组拼接成一个大的神经网络，用于判别生成的图片\n",
    "    model = Sequential()\n",
    "    #先添加生成器架构，再令d不可训练，即固定d\n",
    "    #因此在给定d的情况下训练生成器，即通过将生成的结果投入到判别器进行辨别而优化生成器\n",
    "    model.add(g)\n",
    "    d.trainable = False\n",
    "    model.add(d)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def combine_images(generated_images):\n",
    "    #生成图片拼接\n",
    "    num = generated_images.shape[0]\n",
    "    width = int(math.sqrt(num))\n",
    "    height = int(math.ceil(float(num)/width))\n",
    "    shape = generated_images.shape[1:3]\n",
    "    image = np.zeros((height*shape[0], width*shape[1]),\n",
    "                     dtype=generated_images.dtype)\n",
    "    for index, img in enumerate(generated_images):\n",
    "        i = int(index/width)\n",
    "        j = index % width\n",
    "        image[i*shape[0]:(i+1)*shape[0], j*shape[1]:(j+1)*shape[1]] = \\\n",
    "            img[:, :, 0]\n",
    "    return image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 对于每一次迭代：\n",
    "\n",
    "-  从真实数据分布 P_data 抽取 m 个样本\n",
    "-  从先验分布 P_prior(z) 抽取 m 个噪声样本\n",
    "-  将噪声样本投入 G 而生成数据，即x^tilde = G(Z^i)；通过最大化 V 的近似而更新判别器参数θ_d\n",
    "\n",
    "以上是学习判别器 D 的过程。因为学习 D 的过程是计算 JS 散度的过程，并且我们希望能最大化价值函数，所以该步骤会重复 k 次。\n",
    "\n",
    "-  从先验分布 P_prior(z) 中抽取另外 m 个噪声样本 {z^1,...,z^m}\n",
    "-  通过极小化 V^tilde 而更新生成器参数θ_g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(BATCH_SIZE):\n",
    "    \n",
    "    # 国内好像不能直接导入数据集，我们试了几次都不行，后来将数据集下载到本地'~/.keras/datasets/'，也就是当前目录（我的是用户文件夹下）下的.keras文件夹中。\n",
    "    #下载的地址为：https://s3.amazonaws.com/img-datasets/mnist.npz\n",
    "    (X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
    "    #iamge_data_format选择\"channels_last\"或\"channels_first\"，该选项指定了Keras将要使用的维度顺序。\n",
    "    #\"channels_first\"假定2D数据的维度顺序为(channels, rows, cols)，3D数据的维度顺序为(channels, conv_dim1, conv_dim2, conv_dim3)\n",
    "    \n",
    "    #转换字段类型，并将数据导入变量中\n",
    "    X_train = (X_train.astype(np.float32) - 127.5)/127.5\n",
    "    X_train = X_train[:, :, :, None]\n",
    "    X_test = X_test[:, :, :, None]\n",
    "    # X_train = X_train.reshape((X_train.shape, 1) + X_train.shape[1:])\n",
    "    \n",
    "    #将定义好的模型架构赋值给特定的变量\n",
    "    d = discriminator_model()\n",
    "    g = generator_model()\n",
    "    d_on_g = generator_containing_discriminator(g, d)\n",
    "    \n",
    "    #定义生成器模型判别器模型更新所使用的优化算法及超参数\n",
    "    d_optim = SGD(lr=0.001, momentum=0.9, nesterov=True)\n",
    "    g_optim = SGD(lr=0.001, momentum=0.9, nesterov=True)\n",
    "    \n",
    "    #编译三个神经网络并设置损失函数和优化算法，其中损失函数都是用的是二元分类交叉熵函数。编译是用来配置模型学习过程的\n",
    "    g.compile(loss='binary_crossentropy', optimizer=\"SGD\")\n",
    "    d_on_g.compile(loss='binary_crossentropy', optimizer=g_optim)\n",
    "    \n",
    "    #前一个架构在固定判别器的情况下训练了生成器，所以在训练判别器之前先要设定其为可训练。\n",
    "    d.trainable = True\n",
    "    d.compile(loss='binary_crossentropy', optimizer=d_optim)\n",
    "    \n",
    "    #下面在满足epoch条件下进行训练\n",
    "    for epoch in range(30):\n",
    "        print(\"Epoch is\", epoch)\n",
    "        \n",
    "        #计算一个epoch所需要的迭代数量，即训练样本数除批量大小数的值取整；其中shape[0]就是读取矩阵第一维度的长度\n",
    "        print(\"Number of batches\", int(X_train.shape[0]/BATCH_SIZE))\n",
    "        \n",
    "        #在一个epoch内进行迭代训练\n",
    "        for index in range(int(X_train.shape[0]/BATCH_SIZE)):\n",
    "            \n",
    "            #随机生成的噪声服从均匀分布，且采样下界为-1、采样上界为1，输出BATCH_SIZE×100个样本；即抽取一个批量的随机样本\n",
    "            noise = np.random.uniform(-1, 1, size=(BATCH_SIZE, 100))\n",
    "            \n",
    "            #抽取一个批量的真实图片\n",
    "            image_batch = X_train[index*BATCH_SIZE:(index+1)*BATCH_SIZE]\n",
    "            \n",
    "            #生成的图片使用生成器对随机噪声进行推断；verbose为日志显示，0为不在标准输出流输出日志信息，1为输出进度条记录\n",
    "            generated_images = g.predict(noise, verbose=0)\n",
    "            \n",
    "            #每经过100次迭代输出一张生成的图片\n",
    "            if index % 100 == 0:\n",
    "                image = combine_images(generated_images)\n",
    "                image = image*127.5+127.5\n",
    "                Image.fromarray(image.astype(np.uint8)).save(\"./GAN/\"+str(epoch)+\"_\"+str(index)+\".png\")\n",
    "            \n",
    "            #将真实的图片和生成的图片以多维数组的形式拼接在一起，真实图片在上，生成图片在下\n",
    "            X = np.concatenate((image_batch, generated_images))\n",
    "            \n",
    "            #生成图片真假标签，即一个包含两倍批量大小的列表；前一个批量大小都是1，代表真实图片，后一个批量大小都是0，代表伪造图片\n",
    "            y = [1] * BATCH_SIZE + [0] * BATCH_SIZE\n",
    "            \n",
    "            #判别器的损失；在一个batch的数据上进行一次参数更新\n",
    "            d_loss = d.train_on_batch(X, y)\n",
    "            print(\"batch %d d_loss : %f\" % (index, d_loss))\n",
    "            \n",
    "            #随机生成的噪声服从均匀分布\n",
    "            noise = np.random.uniform(-1, 1, (BATCH_SIZE, 100))\n",
    "            \n",
    "            #固定判别器\n",
    "            d.trainable = False\n",
    "            \n",
    "            #计算生成器损失；在一个batch的数据上进行一次参数更新\n",
    "            g_loss = d_on_g.train_on_batch(noise, [1] * BATCH_SIZE)\n",
    "            \n",
    "            #令判别器可训练\n",
    "            d.trainable = True\n",
    "            print(\"batch %d g_loss : %f\" % (index, g_loss))\n",
    "            \n",
    "            #每100次迭代保存一次生成器和判别器的权重\n",
    "            if index % 100 == 9:\n",
    "                g.save_weights('generator', True)\n",
    "                d.save_weights('discriminator', True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\software\\python\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=1024, input_dim=100)`\n",
      "  \"\"\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch is 0\n",
      "Number of batches 468\n",
      "batch 0 d_loss : 0.651978\n",
      "batch 0 g_loss : 0.653149\n",
      "batch 1 d_loss : 0.639725\n",
      "batch 1 g_loss : 0.645179\n",
      "batch 2 d_loss : 0.624481\n",
      "batch 2 g_loss : 0.633186\n",
      "batch 3 d_loss : 0.600257\n",
      "batch 3 g_loss : 0.619147\n",
      "batch 4 d_loss : 0.581192\n",
      "batch 4 g_loss : 0.603321\n",
      "batch 5 d_loss : 0.554355\n",
      "batch 5 g_loss : 0.597126\n",
      "batch 6 d_loss : 0.537645\n",
      "batch 6 g_loss : 0.584211\n",
      "batch 7 d_loss : 0.520177\n",
      "batch 7 g_loss : 0.567349\n",
      "batch 8 d_loss : 0.503279\n",
      "batch 8 g_loss : 0.559891\n",
      "batch 9 d_loss : 0.493053\n",
      "batch 9 g_loss : 0.545466\n",
      "batch 10 d_loss : 0.484570\n",
      "batch 10 g_loss : 0.540249\n",
      "batch 11 d_loss : 0.469190\n",
      "batch 11 g_loss : 0.531435\n",
      "batch 12 d_loss : 0.463894\n",
      "batch 12 g_loss : 0.526381\n",
      "batch 13 d_loss : 0.453370\n",
      "batch 13 g_loss : 0.520574\n",
      "batch 14 d_loss : 0.458233\n",
      "batch 14 g_loss : 0.508590\n",
      "batch 15 d_loss : 0.457384\n",
      "batch 15 g_loss : 0.507561\n",
      "batch 16 d_loss : 0.458243\n",
      "batch 16 g_loss : 0.506943\n",
      "batch 17 d_loss : 0.461206\n",
      "batch 17 g_loss : 0.498258\n",
      "batch 18 d_loss : 0.458951\n",
      "batch 18 g_loss : 0.497975\n",
      "batch 19 d_loss : 0.460366\n",
      "batch 19 g_loss : 0.497394\n",
      "batch 20 d_loss : 0.463955\n",
      "batch 20 g_loss : 0.496585\n",
      "batch 21 d_loss : 0.478680\n",
      "batch 21 g_loss : 0.500596\n",
      "batch 22 d_loss : 0.477412\n",
      "batch 22 g_loss : 0.500952\n",
      "batch 23 d_loss : 0.479661\n",
      "batch 23 g_loss : 0.506071\n",
      "batch 24 d_loss : 0.487687\n",
      "batch 24 g_loss : 0.509279\n",
      "batch 25 d_loss : 0.491930\n",
      "batch 25 g_loss : 0.517566\n",
      "batch 26 d_loss : 0.499258\n",
      "batch 26 g_loss : 0.529529\n",
      "batch 27 d_loss : 0.498764\n",
      "batch 27 g_loss : 0.534673\n",
      "batch 28 d_loss : 0.514322\n",
      "batch 28 g_loss : 0.545454\n",
      "batch 29 d_loss : 0.509265\n",
      "batch 29 g_loss : 0.557002\n",
      "batch 30 d_loss : 0.507164\n",
      "batch 30 g_loss : 0.570490\n",
      "batch 31 d_loss : 0.502942\n",
      "batch 31 g_loss : 0.584526\n",
      "batch 32 d_loss : 0.503026\n",
      "batch 32 g_loss : 0.601911\n",
      "batch 33 d_loss : 0.498786\n",
      "batch 33 g_loss : 0.621503\n",
      "batch 34 d_loss : 0.491122\n",
      "batch 34 g_loss : 0.647376\n",
      "batch 35 d_loss : 0.493958\n",
      "batch 35 g_loss : 0.666375\n",
      "batch 36 d_loss : 0.486555\n",
      "batch 36 g_loss : 0.677024\n",
      "batch 37 d_loss : 0.490598\n",
      "batch 37 g_loss : 0.702301\n",
      "batch 38 d_loss : 0.479216\n",
      "batch 38 g_loss : 0.724128\n",
      "batch 39 d_loss : 0.460473\n",
      "batch 39 g_loss : 0.743823\n",
      "batch 40 d_loss : 0.452565\n",
      "batch 40 g_loss : 0.766780\n",
      "batch 41 d_loss : 0.448236\n",
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      "batch 47 g_loss : 0.904820\n",
      "batch 48 d_loss : 0.355749\n",
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-17-b12e920899c2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m128\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-6-15eec0321f96>\u001b[0m in \u001b[0;36mtrain\u001b[1;34m(BATCH_SIZE)\u001b[0m\n\u001b[0;32m     47\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m             \u001b[1;31m#生成的图片使用生成器对随机噪声进行推断；verbose为日志显示，0为不在标准输出流输出日志信息，1为输出进度条记录\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 49\u001b[1;33m             \u001b[0mgenerated_images\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnoise\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     51\u001b[0m             \u001b[1;31m#每经过100次迭代输出一张生成的图片\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\software\\python\\lib\\site-packages\\keras\\models.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, x, batch_size, verbose)\u001b[0m\n\u001b[0;32m    911\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuilt\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    912\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 913\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    914\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    915\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mpredict_on_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\software\\python\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, x, batch_size, verbose, steps)\u001b[0m\n\u001b[0;32m   1711\u001b[0m         \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1712\u001b[0m         return self._predict_loop(f, ins, batch_size=batch_size,\n\u001b[1;32m-> 1713\u001b[1;33m                                   verbose=verbose, steps=steps)\n\u001b[0m\u001b[0;32m   1714\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1715\u001b[0m     def train_on_batch(self, x, y,\n",
      "\u001b[1;32mf:\\software\\python\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_predict_loop\u001b[1;34m(self, f, ins, batch_size, verbose, steps)\u001b[0m\n\u001b[0;32m   1267\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1268\u001b[0m                     \u001b[0mins_batch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_slice_arrays\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_ids\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1269\u001b[1;33m                 \u001b[0mbatch_outs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1270\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1271\u001b[0m                     \u001b[0mbatch_outs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\software\\python\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m   2271\u001b[0m         updated = session.run(self.outputs + [self.updates_op],\n\u001b[0;32m   2272\u001b[0m                               \u001b[0mfeed_dict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2273\u001b[1;33m                               **self.session_kwargs)\n\u001b[0m\u001b[0;32m   2274\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2275\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\software\\python\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    893\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    894\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 895\u001b[1;33m                          run_metadata_ptr)\n\u001b[0m\u001b[0;32m    896\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    897\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\software\\python\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1122\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1123\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m-> 1124\u001b[1;33m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[0;32m   1125\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1126\u001b[0m       \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\software\\python\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_run\u001b[1;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1319\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1320\u001b[0m       return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[1;32m-> 1321\u001b[1;33m                            options, run_metadata)\n\u001b[0m\u001b[0;32m   1322\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1323\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\software\\python\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m   1325\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1326\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1327\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1328\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1329\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\software\\python\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[1;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m   1304\u001b[0m           return tf_session.TF_Run(session, options,\n\u001b[0;32m   1305\u001b[0m                                    \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1306\u001b[1;33m                                    status, run_metadata)\n\u001b[0m\u001b[0;32m   1307\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1308\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "train(128)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "跑30次迭代每次有400多batch时间太长，所以在第一个100处保存了就先退出了。\n",
    "\n",
    "但是看了下下面生成的效果不好，还是再继续跑跑吧..吃饭去了先"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate(BATCH_SIZE, nice= False ):\n",
    "    #训练完模型后，可以运行该函数生成图片\n",
    "    g = generator_model()\n",
    "    g.compile(loss='binary_crossentropy', optimizer=\"SGD\")\n",
    "    g.load_weights('generator')\n",
    "    if nice:\n",
    "        d = discriminator_model()\n",
    "        d.compile(loss='binary_crossentropy', optimizer=\"SGD\")\n",
    "        d.load_weights('discriminator')\n",
    "        noise = np.random.uniform(-1, 1, (BATCH_SIZE*20, 100))\n",
    "        generated_images = g.predict(noise, verbose=1)\n",
    "        d_pret = d.predict(generated_images, verbose=1)\n",
    "        index = np.arange(0, BATCH_SIZE*20)\n",
    "        index.resize((BATCH_SIZE*20, 1))\n",
    "        pre_with_index = list(np.append(d_pret, index, axis=1))\n",
    "        pre_with_index.sort(key=lambda x: x[0], reverse=True)\n",
    "        nice_images = np.zeros((BATCH_SIZE,) + generated_images.shape[1:3], dtype=np.float32)\n",
    "        nice_images = nice_images[:, :, :, None]\n",
    "        for i in range(BATCH_SIZE):\n",
    "            idx = int(pre_with_index[i][1])\n",
    "            nice_images[i, :, :, 0] = generated_images[idx, :, :, 0]\n",
    "        image = combine_images(nice_images)\n",
    "    else:\n",
    "        noise = np.random.uniform(-1, 1, (BATCH_SIZE, 100))\n",
    "        generated_images = g.predict(noise, verbose=0)\n",
    "        image = combine_images(generated_images)\n",
    "    image = image*127.5+127.5\n",
    "    Image.fromarray(image.astype(np.uint8)).save(\"./MNIST_data/generated_image.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\software\\python\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=1024, input_dim=100)`\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "generate(128)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "初始：\n",
    "![start](./data/0_0.png)\n",
    "\n",
    "没跑完，结果还是可以看出一个大致的轮廓：\n",
    "\n",
    "![result](./data/2_100.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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