对抗网络是14年Goodfellow Ian在论文Generative Adversarial Nets中提出来的。 原理方面，对抗网络可以简单归纳为一个生成器(generator)和一个判断器(discriminator)之间博弈的过程。整个网络训练的过程中，
两个模块的分工
判断网络，直观来看就是一个简单的神经网络结构，输入就是一副图像，输出就是一个概率值，用于判断真假使用（概率值大于0.5那就是真，小于0.5那就是假）生成网络，同样也可以看成是一个神经网络模型，输入是一组随机数Z，输出是一个图像。两个模块的训练目的
判别网络的目的：就是能判别出来属于的一张图它是来自真实样本集还是假样本集。假如输入的是真样本，网络输出就接近1，输入的是假样本，网络输出接近0，那么很完美，达到了很好判别的目的。生成网络的目的：生成网络是造样本的，它的目的就是使得自己造样本的能力尽可能强，强到判别网络没法判断是真样本还是假样本。GAN的训练
需要注意的是生成模型与对抗模型可以说是完全独立的两个模型，好比就是完全独立的两个神经网络模型，他们之间没有什么联系。
那么训练这样的两个模型的大方法就是：单独交替迭代训练。因为是2个网络，不好一起训练，所以才去交替迭代训练，我们一一来看。
首先我们先随机产生一个生成网络模型（当然可能不是最好的生成网络），那么给一堆随机数组，就会得到一堆假的样本集（因为不是最终的生成模型，那么现在生成网络可能就处于劣势，导致生成的样本很糟糕，可能很容易就被判别网络判别出来了说这货是假冒的），但是先不管这个，假设我们现在有了这样的假样本集，真样本集一直都有，现在我们人为的定义真假样本集的标签，因为我们希望真样本集的输出尽可能为1，假样本集为0，很明显这里我们就已经默认真样本集所有的类标签都为1，而假样本集的所有类标签都为0.
对于生成网络，回想下我们的目标，是生成尽可能逼真的样本。那么原始的生成网络生成的样本你怎么知道它真不真呢？就是送到判别网络中，所以在训练生成网络的时候，我们需要联合判别网络一起才能达到训练的目的。就是如果我们单单只用生成网络，那么想想我们怎么去训练？误差来源在哪里？细想一下没有，但是如果我们把刚才的判别网络串接在生成网络的后面，这样我们就知道真假了，也就有了误差了。所以对于生成网络的训练其实是对生成-判别网络串接的训练，就像图中显示的那样。好了那么现在来分析一下样本，原始的噪声数组Z我们有，也就是生成了假样本我们有，此时很关键的一点来了，我们要把这些假样本的标签都设置为1，也就是认为这些假样本在生成网络训练的时候是真样本。这样才能起到迷惑判别器的目的，也才能使得生成的假样本逐渐逼近为正样本。
下面是代码部分，这里，我们利用训练的两个数据集分别是
mnistCeleba来生成手写数字以及人脸
首先是数据集的下载
import math import os import hashlib from urllib.request import urlretrieve import zipfile import gzip import shutil data_dir = './data' def download_extract(database_name, data_path): """ Download and extract database :param database_name: Database name """ DATASET_CELEBA_NAME = 'celeba' DATASET_MNIST_NAME = 'mnist' if database_name == DATASET_CELEBA_NAME: url = 'https://s3-us-west-1.amazonaws.com/udacity-dlnfd/datasets/celeba.zip' hash_code = '00d2c5bc6d35e252742224ab0c1e8fcb' extract_path = os.path.join(data_path, 'img_align_celeba') save_path = os.path.join(data_path, 'celeba.zip') extract_fn = _unzip elif database_name == DATASET_MNIST_NAME: url = 'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz' hash_code = 'f68b3c2dcbeaaa9fbdd348bbdeb94873' extract_path = os.path.join(data_path, 'mnist') save_path = os.path.join(data_path, 'train-images-idx3-ubyte.gz') extract_fn = _ungzip if os.path.exists(extract_path): print('Found {} Data'.format(database_name)) return if not os.path.exists(data_path): os.makedirs(data_path) if not os.path.exists(save_path): with DLProgress(unit='B', unit_scale=True, miniters=1, desc='Downloading {}'.format(database_name)) as pbar: urlretrieve( url, save_path, pbar.hook) assert hashlib.md5(open(save_path, 'rb').read()).hexdigest() == hash_code, \ '{} file is corrupted. Remove the file and try again.'.format(save_path) os.makedirs(extract_path) try: extract_fn(save_path, extract_path, database_name, data_path) except Exception as err: shutil.rmtree(extract_path) # Remove extraction folder if there is an error raise err # Remove compressed data os.remove(save_path) # download mnist download_extract('mnist', data_dir) # download celeba download_extract('celeba', data_dir我们先看看我们的mnist还有celeba数据集是什么样子
# the number of images show_n_images =16 %matplotlib inline import os from glob import glob from matplotlib import pyplot def get_batch(image_files, width, height, mode): data_batch = np.array( [get_image(sample_file, width, height, mode) for sample_file in image_files]).astype(np.float32) # Make sure the images are in 4 dimensions if len(data_batch.shape) < 4: data_batch = data_batch.reshape(data_batch.shape + (1,)) return data_batch def images_square_grid(images, mode): # Get maximum size for square grid of images save_size = math.floor(np.sqrt(images.shape[0])) # Scale to 0-255 images = (((images - images.min()) * 255) / (images.max() - images.min())).astype(np.uint8) # Put images in a square arrangement images_in_square = np.reshape( images[:save_size*save_size], (save_size, save_size, images.shape[1], images.shape[2], images.shape[3])) if mode == 'L': images_in_square = np.squeeze(images_in_square, 4) # Combine images to grid image new_im = Image.new(mode, (images.shape[1] * save_size, images.shape[2] * save_size)) for col_i, col_images in enumerate(images_in_square): for image_i, image in enumerate(col_images): im = Image.fromarray(image, mode) new_im.paste(im, (col_i * images.shape[1], image_i * images.shape[2])) return new_im mnist_images = get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L') pyplot.imshow(images_square_grid(mnist_images, 'L'), cmap='gray')mninst：
show_n_images = 9 mnist_images = get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB') pyplot.imshow(images_square_grid(mnist_images, 'RGB'))celeba
现在我们开始搭建网络
这里我建议用GPU来训练，tensorflow的版本最好是1.1.0
from distutils.version import LooseVersion import warnings import tensorflow as tf # Check TensorFlow Version assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__) print('TensorFlow Version: {}'.format(tf.__version__)) # Check for a GPU if not tf.test.gpu_device_name(): warnings.warn('No GPU found. Please use a GPU to train your neural network.') else: print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))接着我们要做的是构建输入
def model_inputs(image_width, image_height, image_channels, z_dim): ## Real imag inputs_real = tf.placeholder(tf.float32,(None, image_width,image_height,image_channels), name = 'input_real') ## input z inputs_z = tf.placeholder(tf.float32,(None, z_dim), name='input_z') ## Learning rate learning_rate = tf.placeholder(tf.float32, name = 'lr') return inputs_real, inputs_z, learning_rate构建Discriminator
def discriminator(images, reuse=False): """ Create the discriminator network :param images: Tensor of input image(s) :param reuse: Boolean if the weights should be reused :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator) """ # TODO: Implement Function ## scope here with tf.variable_scope('discriminator', reuse=reuse): alpha = 0.2 ### leak relu coeff # drop out probability keep_prob = 0.8 # input layer 28 * 28 * color channel x1 = tf.layers.conv2d(images, 128, 5, strides=2, padding='same', kernel_initializer= tf.contrib.layers.xavier_initializer(seed=2)) ## No batch norm here ## leak relu here / alpha = 0.2 relu1 = tf.maximum(alpha * x1, x1) # applied drop out here drop1 = tf.nn.dropout(relu1, keep_prob= keep_prob) # 14 * 14 * 128 # Layer 2 x2 = tf.layers.conv2d(drop1, 256, 5, strides=2, padding='same', kernel_initializer= tf.contrib.layers.xavier_initializer(seed=2)) ## employ batch norm here bn2 = tf.layers.batch_normalization(x2, training=True) ## leak relu relu2 = tf.maximum(alpha * bn2, bn2) drop2 = tf.nn.dropout(relu2, keep_prob=keep_prob) # 7 * 7 * 256 # Layer3 x3 = tf.layers.conv2d(drop2, 512, 5, strides=2, padding='same', kernel_initializer= tf.contrib.layers.xavier_initializer(seed=2)) bn3 = tf.layers.batch_normalization(x3, training=True) relu3 = tf.maximum(alpha * bn3, bn3) drop3 = tf.nn.dropout(relu3, keep_prob=keep_prob) # 4 * 4 * 512 # Output # Flatten flatten = tf.reshape(relu3, (-1, 4 * 4 * 512)) logits = tf.layers.dense(flatten,1) # activation out = tf.nn.sigmoid(logits) return out, logits接着是 Generator
def generator(z, out_channel_dim, is_train=True): """ Create the generator network :param z: Input z :param out_channel_dim: The number of channels in the output image :param is_train: Boolean if generator is being used for training :return: The tensor output of the generator """ # TODO: Implement Function with tf.variable_scope('generator', reuse = not is_train): # First Fully connect layer x0 = tf.layers.dense(z, 4 * 4 * 512) # Reshape x0 = tf.reshape(x0,(-1,4,4,512)) # Use the batch norm bn0 = tf.layers.batch_normalization(x0, training= is_train) # Leak relu relu0 = tf.nn.relu(bn0) # 4 * 4 * 512 # Conv transpose here x1 = tf.layers.conv2d_transpose(relu0, 256, 4, strides=1, padding='valid') bn1 = tf.layers.batch_normalization(x1, training=is_train) relu1 = tf.nn.relu(bn1) # 7 * 7 * 256 x2 = tf.layers.conv2d_transpose(relu1, 128, 3, strides=2, padding='same') bn2 = tf.layers.batch_normalization(x2, training=is_train) relu2 = tf.nn.relu(bn2) # 14 * 14 * 128 # Last cov logits = tf.layers.conv2d_transpose(relu2, out_channel_dim, 3, strides=2, padding='same') ## without batch norm here out = tf.tanh(logits) return out然后我们来定义loss，这里，加入了smoother
def model_loss(input_real, input_z, out_channel_dim): """ Get the loss for the discriminator and generator :param input_real: Images from the real dataset :param input_z: Z input :param out_channel_dim: The number of channels in the output image :return: A tuple of (discriminator loss, generator loss) """ # TODO: Implement Function g_model = generator(input_z, out_channel_dim, is_train=True) d_model_real, d_logits_real = discriminator(input_real, reuse = False) d_model_fake, d_logits_fake = discriminator(g_model, reuse= True) ## add smooth here smooth = 0.1 d_loss_real = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real) * (1 - smooth))) d_loss_fake = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake))) g_loss = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels= tf.ones_like(d_model_fake))) d_loss = d_loss_real + d_loss_fake return d_loss, g_loss接着我们需要定义网络优化的过程，这里我们需要用到batch_normlisation, 不懂的话去搜下文档
def model_opt(d_loss, g_loss, learning_rate, beta1): """ Get optimization operations :param d_loss: Discriminator loss Tensor :param g_loss: Generator loss Tensor :param learning_rate: Learning Rate Placeholder :param beta1: The exponential decay rate for the 1st moment in the optimizer :return: A tuple of (discriminator training operation, generator training operation) """ t_vars = tf.trainable_variables() d_vars = [var for var in t_vars if var.name.startswith('discriminator')] g_vars = [var for var in t_vars if var.name.startswith('generator')] update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(update_ops): d_train_opt = tf.train.AdamOptimizer(learning_rate,beta1=beta1).minimize(d_loss,var_list = d_vars) g_train_opt = tf.train.AdamOptimizer(learning_rate,beta1=beta1).minimize(g_loss,var_list = g_vars) return d_train_opt, g_train_opt现在，我们网络的模块，损失函数，以及优化的过程都定义好了，现在我们就要开始训练我们的网络了，我们的训练过程定义如下。
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode): """ Train the GAN :param epoch_count: Number of epochs :param batch_size: Batch Size :param z_dim: Z dimension :param learning_rate: Learning Rate :param beta1: The exponential decay rate for the 1st moment in the optimizer :param get_batches: Function to get batches :param data_shape: Shape of the data :param data_image_mode: The image mode to use for images ("RGB" or "L") """ losses = [] samples = [] input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim) d_loss, g_loss = model_loss(input_real,input_z,data_shape[-1]) d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1) steps = 0 with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for epoch_i in range(epoch_count): for batch_images in get_batches(batch_size): # TODO: Train Model steps += 1 # Reshape the image and pass to Discriminator batch_images = batch_images.reshape(batch_size, data_shape[1], data_shape[2], data_shape[3]) # Rescale the data to -1 and 1 batch_images = batch_images * 2 # Sample the noise batch_z = np.random.uniform(-1,1,size = (batch_size, z_dim)) ## Run optimizer _ = sess.run(d_opt, feed_dict = {input_real:batch_images, input_z:batch_z, lr:learning_rate }) _ = sess.run(g_opt, feed_dict = {input_real:batch_images, input_z:batch_z, lr:learning_rate}) if steps % 10 == 0: train_loss_d = d_loss.eval({input_real:batch_images, input_z:batch_z}) train_loss_g = g_loss.eval({input_real:batch_images, input_z:batch_z}) losses.append((train_loss_d,train_loss_g)) print("Epoch {}/{}...".format(epoch_i+1, epochs), "Discriminator Loss: {:.4f}...".format(train_loss_d), "Generator Loss: {:.4f}".format(train_loss_g)) if steps % 100 == 0: show_generator_output(sess, 25, input_z, data_shape[-1], data_image_mode)开始训练，超参数的设置
对于MNIST
batch_size = 64 z_dim = 100 learning_rate = 0.001 beta1 = 0.5 epochs = 2 mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg'))) with tf.Graph().as_default(): train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches, mnist_dataset.shape, mnist_dataset.image_mode)训练效果如下
开始的时候，网络的参数很差，我们生成的手写数字的效果自然就不好
随着训练的进行，轮廓逐渐清晰，效果如下，到最后：
我们看到数字的轮廓基本是清晰可以辨认的，当然，这只是两个epoch的结果，如果有足够的时间经过更长时间的训练，效果会更好。我们同样展示下对celeba人脸数据集的训练结果
batch_size = 32 z_dim = 100 learning_rate = 0.001 beta1 = 0.4 epochs = 1 celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))) with tf.Graph().as_default(): train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches, celeba_dataset.shape, celeba_dataset.image_mode)训练开始：
经过一个epoch之后：
人脸的轮廓基本清晰了。这里我们就是用了DCGAN最简单的方式来实现，原理过程说的不是很详细，同时，可能这个参数设置也不是很合理，训练的也不够成分，但是我想可以帮大家快速掌握实现一个简单的DCGAN的方法了。
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