使用Tensorflow实现DCGAN来生成手写数字的详细代码

好的，以下是使用Tensorflow实现DCGAN生成手写数字的详细代码：

首先，我们需要导入必要的库和数据集。这里我们使用Tensorflow自带的MNIST数据集。

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/")

接下来，我们定义生成器和判别器的网络结构。生成器的输入是一个随机噪声，输出是一张28x28的手写数字图片。判别器的输入是一张28x28的手写数字图片，输出是一个判别得分，表示这张图片是真实图片的概率。

def generator(z, reuse=None):
    with tf.variable_scope('gen', reuse=reuse):
        hidden1 = tf.layers.dense(inputs=z, units=128, activation=tf.nn.leaky_relu)
        hidden2 = tf.layers.dense(inputs=hidden1, units=256, activation=tf.nn.leaky_relu)
        output = tf.layers.dense(inputs=hidden2, units=784, activation=tf.nn.tanh)
        return output
    
def discriminator(X, reuse=None):
    with tf.variable_scope('dis', reuse=reuse):
        hidden1 = tf.layers.dense(inputs=X, units=128, activation=tf.nn.leaky_relu)
        hidden2 = tf.layers.dense(inputs=hidden1, units=256, activation=tf.nn.leaky_relu)
        logits = tf.layers.dense(inputs=hidden2, units=1)
        output = tf.sigmoid(logits)
        return output, logits

然后，我们定义生成器和判别器的输入和损失函数。其中，生成器的损失函数是生成的假图片与真实图片的差异，判别器的损失函数是真实图片和假图片的区分度。

real_images = tf.placeholder(tf.float32, shape=[None, 784])
z = tf.placeholder(tf.float32, shape=[None, 100])

G = generator(z)
D_output_real, D_logits_real = discriminator(real_images)
D_output_fake, D_logits_fake = discriminator(G, reuse=True)

def loss_func(logits_in, labels_in):
    return tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits_in, labels=labels_in))

D_real_loss = loss_func(D_logits_real, tf.ones_like(D_logits_real) * 0.9)
D_fake_loss = loss_func(D_logits_fake, tf.zeros_like(D_logits_fake))
D_loss = D_real_loss + D_fake_loss
G_loss = loss_func(D_logits_fake, tf.ones_like(D_logits_fake))

接下来，我们定义生成器和判别器的优化器，并且训练模型。

lr = 0.001

tvars = tf.trainable_variables()
d_vars = [var for var in tvars if 'dis' in var.name]
g_vars = [var for var in tvars if 'gen' in var.name]

D_trainer = tf.train.AdamOptimizer(lr).minimize(D_loss, var_list=d_vars)
G_trainer = tf.train.AdamOptimizer(lr).minimize(G_loss, var_list=g_vars)

batch_size = 100
epochs = 100
init = tf.global_variables_initializer()

samples = []

with tf.Session() as sess:
    sess.run(init)
    for epoch in range(epochs):
        num_batches = mnist.train.num_examples // batch_size
        for i in range(num_batches):
            batch = mnist.train.next_batch(batch_size)
            batch_images = batch[0].reshape((batch_size, 784))
            batch_images = batch_images * 2 - 1
            batch_z = np.random.uniform(-1, 1, size=(batch_size, 100))
            _ = sess.run(D_trainer, feed_dict={real_images: batch_images, z: batch_z})
            _ = sess.run(G_trainer, feed_dict={z: batch_z})
        
        print("Epoch:", epoch)
        
        sample_z = np.random.uniform(-1, 1, size=(1, 100))
        gen_sample = sess.run(generator(z, reuse=True),feed_dict={z: sample_z})
        
        samples.append(gen_sample)
        
plt.imshow(samples[0].reshape(28,28))
plt.show()

最后，我们可以看到生成的手写数字图片。随着训练的迭代次数增加，生成的图片会越来越真实。