DCGAN

简介

DCGAN即使用卷积网络的对抗网络，其原理和GAN一样，只是把CNN的卷积技术用于GAN模式的网络里，G（生成器）网在生成数据时，使用反卷积的重构技术来重构原始图片。D（判别器）网用卷积技术来识别图片特征，进而作出判别。

https://arxiv.org/abs/1511.06434

架构

判别器

生成器

在DCGAN中，生成式模型G(z)使用一个比较特殊的深度卷积网络来实现，如下图所示：

反卷积

从前面两幅图中可以看出，DCGAN的生成式模型G(z)中出现了上采样（upsampling）。

卷积神经网络的下采样很好理解，加入pooling层即可，然而这里的上采样要如何实现呢？

这里，DCGAN通过“微步幅卷积”（fractionally-strided convolution）进行上采样。

假设有一个3×3的输入，希望输出的尺寸比这要大，那么可以把这个3×3的输入通过在像素之间插入0的方式来进行扩展，如下图所示。当扩展到7×7的尺寸后，再进行卷积，就可以得到尺寸比原来大的输出。

特点

调优

https://github.com/hindupuravinash/the-gan-

实现

深度卷积神经网络生成Mnist手写数据集—-DCGAN

导入环境

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data

数据准备与超参数设置

mnist = input_data.read_data_sets('data')
# 定义参数
batch_size = 64
noise_size = 100
epochs = 5
n_samples = 25
learning_rate = 0.001

数据处理

def get_inputs(noise_dim, image_height, image_width, image_depth):
    # 真实数据
    inputs_real = tf.placeholder(tf.float32, [None, image_height, image_width, image_depth], name='inputs_real')
    # 噪声数据
    inputs_noise = tf.placeholder(tf.float32, [None, noise_dim], name='inputs_noise')

    return inputs_real, inputs_noise

构建DCGAN网络结构

生成器

def get_generator(noise_img, output_dim, is_train=True, alpha=0.01):
    with tf.variable_scope("generator", reuse=(not is_train)):
        # 100 x 1 to 4 x 4 x 512
        # 全连接层
        layer1 = tf.layers.dense(noise_img, 4 * 4 * 512)
        layer1 = tf.reshape(layer1, [-1, 4, 4, 512])
        # batch normalization
        layer1 = tf.layers.batch_normalization(layer1, training=is_train)
        # Leaky ReLU
        layer1 = tf.maximum(alpha * layer1, layer1)
        # dropout
        layer1 = tf.nn.dropout(layer1, keep_prob=0.8)

        # 4 x 4 x 512 to 7 x 7 x 256
        layer2 = tf.layers.conv2d_transpose(layer1, 256, 4, strides=1, padding='valid')
        layer2 = tf.layers.batch_normalization(layer2, training=is_train)
        layer2 = tf.maximum(alpha * layer2, layer2)
        layer2 = tf.nn.dropout(layer2, keep_prob=0.8)

        # 7 x 7 256 to 14 x 14 x 128
        layer3 = tf.layers.conv2d_transpose(layer2, 128, 3, strides=2, padding='same')
        layer3 = tf.layers.batch_normalization(layer3, training=is_train)
        layer3 = tf.maximum(alpha * layer3, layer3)
        layer3 = tf.nn.dropout(layer3, keep_prob=0.8)

        # 14 x 14 x 128 to 28 x 28 x 1
        logits = tf.layers.conv2d_transpose(layer3, output_dim, 3, strides=2, padding='same')
        # MNIST原始数据集的像素范围在0-1，这里的生成图片范围为(-1,1)
        # 因此在训练时，记住要把MNIST像素范围进行resize
        outputs = tf.tanh(logits)

        return outputs

判别器

def get_discriminator(inputs_img, reuse=False, alpha=0.01):
    with tf.variable_scope("discriminator", reuse=reuse):
        # 28 x 28 x 1 to 14 x 14 x 128
        # 第一层不加入BN
        layer1 = tf.layers.conv2d(inputs_img, 128, 3, strides=2, padding='same')
        layer1 = tf.maximum(alpha * layer1, layer1)
        layer1 = tf.nn.dropout(layer1, keep_prob=0.8)

        # 14 x 14 x 128 to 7 x 7 x 256
        layer2 = tf.layers.conv2d(layer1, 256, 3, strides=2, padding='same')
        layer2 = tf.layers.batch_normalization(layer2, training=True)
        layer2 = tf.maximum(alpha * layer2, layer2)
        layer2 = tf.nn.dropout(layer2, keep_prob=0.8)

        # 7 x 7 x 256 to 4 x 4 x 512
        layer3 = tf.layers.conv2d(layer2, 512, 3, strides=2, padding='same')
        layer3 = tf.layers.batch_normalization(layer3, training=True)
        layer3 = tf.maximum(alpha * layer3, layer3)
        layer3 = tf.nn.dropout(layer3, keep_prob=0.8)

        # 4 x 4 x 512 to 4*4*512 x 1
        flatten = tf.reshape(layer3, (-1, 4 * 4 * 512))
        logits = tf.layers.dense(flatten, 1)
        outputs = tf.sigmoid(logits)

        return logits, outputs

计算损失值

def get_loss(inputs_real, inputs_noise, image_depth, smooth=0.1):
    g_outputs = get_generator(inputs_noise, image_depth, is_train=True)
    d_logits_real, d_outputs_real = get_discriminator(inputs_real)
    d_logits_fake, d_outputs_fake = get_discriminator(g_outputs, reuse=True)

    # 计算Loss
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
                                                                    labels=tf.ones_like(d_outputs_fake) * (1 - smooth)))

    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,
                                                                         labels=tf.ones_like(d_outputs_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_outputs_fake)))
    d_loss = tf.add(d_loss_real, d_loss_fake)

    return g_loss, d_loss

初始化优化器

def get_optimizer(g_loss, d_loss, learning_rate=0.001):
    train_vars = tf.trainable_variables()

    g_vars = [var for var in train_vars if var.name.startswith("generator")]
    d_vars = [var for var in train_vars if var.name.startswith("discriminator")]

    # Optimizer
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        g_opt = tf.train.AdamOptimizer(learning_rate).minimize(g_loss, var_list=g_vars)
        d_opt = tf.train.AdamOptimizer(learning_rate).minimize(d_loss, var_list=d_vars)

    return g_opt, d_opt

显示图片

def plot_images(samples):
    fig, axes = plt.subplots(nrows=5, ncols=5, sharex=True, sharey=True, figsize=(7, 7))
    for img, ax in zip(samples, axes.flatten()):
        ax.imshow(img.reshape((28, 28)), cmap='Greys_r')
        ax.get_xaxis().set_visible(False)
        ax.get_yaxis().set_visible(False)
    fig.tight_layout(pad=0)
    plt.show()

def show_generator_output(sess, n_images, inputs_noise, output_dim):
    noise_shape = inputs_noise.get_shape().as_list()[-1]
    # 生成噪声图片
    examples_noise = np.random.uniform(-1, 1, size=[n_images, noise_shape])

    samples = sess.run(get_generator(inputs_noise, output_dim, False),
                       feed_dict={inputs_noise: examples_noise})

    result = np.squeeze(samples, -1)
    return result

开始训练

def train(noise_size, data_shape, batch_size, n_samples):
    # 存储loss
    losses = []
    steps = 0

    inputs_real, inputs_noise = get_inputs(noise_size, data_shape[1], data_shape[2], data_shape[3])
    g_loss, d_loss = get_loss(inputs_real, inputs_noise, data_shape[-1])
    print("FUNCTION READY!!")
    g_train_opt, d_train_opt = get_optimizer(g_loss, d_loss, learning_rate)
    print("TRAINING....")
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        # 迭代epoch
        for e in range(epochs):
            for batch_i in range(mnist.train.num_examples // batch_size):
                steps += 1
                batch = mnist.train.next_batch(batch_size)

                batch_images = batch[0].reshape((batch_size, data_shape[1], data_shape[2], data_shape[3]))
                # scale to -1, 1
                batch_images = batch_images * 2 - 1

                # noise
                batch_noise = np.random.uniform(-1, 1, size=(batch_size, noise_size))

                # run optimizer
                sess.run(g_train_opt, feed_dict={inputs_real: batch_images,
                                                 inputs_noise: batch_noise})
                sess.run(d_train_opt, feed_dict={inputs_real: batch_images,
                                                 inputs_noise: batch_noise})

                if steps % 101 == 0:
                    train_loss_d = d_loss.eval({inputs_real: batch_images,
                                                inputs_noise: batch_noise})
                    train_loss_g = g_loss.eval({inputs_real: batch_images,
                                                inputs_noise: batch_noise})
                    losses.append((train_loss_d, train_loss_g))
                    print("Epoch {}/{}....".format(e + 1, epochs),
                          "Discriminator Loss: {:.4f}....".format(train_loss_d),
                          "Generator Loss: {:.4f}....".format(train_loss_g))

            if e % 1 == 0:
                # 显示图片
                samples = show_generator_output(sess, n_samples, inputs_noise, data_shape[-1])
                plot_images(samples)


with tf.Graph().as_default():
    train(noise_size, [-1, 28, 28, 1], batch_size, n_samples)
    print("OPTIMIZER END!!")