发我DCGAN的模型及训练及测试代码

抱歉，我作为AI语言模型不能够提供代码。但是，以下是DCGAN的Python代码示例，您可以根据自己的需要进行修改和优化：

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

# Load the MNIST dataset
mnist = input_data.read_data_sets('MNIST_data')

# Define the generator network
def generator(z, reuse=None):
    with tf.variable_scope('gen', reuse=reuse):
        hidden1 = tf.layers.dense(inputs=z, units=128)
        # Leaky ReLU activation
        alpha = 0.01
        hidden1 = tf.maximum(alpha * hidden1, hidden1)
        hidden2 = tf.layers.dense(inputs=hidden1, units=128)
        hidden2 = tf.maximum(alpha * hidden2, hidden2)
        output = tf.layers.dense(inputs=hidden2, units=784, activation=tf.nn.tanh)
        return output

# Define the discriminator network
def discriminator(X, reuse=None):
    with tf.variable_scope('dis', reuse=reuse):
        hidden1 = tf.layers.dense(inputs=X, units=128)
        alpha = 0.01
        hidden1 = tf.maximum(alpha * hidden1, hidden1)
        hidden2 = tf.layers.dense(inputs=hidden1, units=128)
        hidden2 = tf.maximum(alpha * hidden2, hidden2)
        # Sigmoid activation function for binary classification
        logits = tf.layers.dense(hidden2, units=1)
        output = tf.sigmoid(logits)
        return output, logits

# Define the placeholders for input and noise variables
real_images = tf.placeholder(tf.float32, shape=[None, 784])
z = tf.placeholder(tf.float32, shape=[None, 100])

# Generate fake images from the generator network
G = generator(z)

# Calculate the discriminator output for real and fake images
D_output_real, D_logits_real = discriminator(real_images)
D_output_fake, D_logits_fake = discriminator(G, reuse=True)

# Define the loss functions for generator and discriminator
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_real))
D_loss = D_real_loss + D_fake_loss
G_loss = loss_func(D_logits_fake, tf.ones_like(D_logits_fake))

# Define the optimizer for generator and discriminator
learning_rate = 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(learning_rate).minimize(D_loss, var_list=d_vars)
G_trainer = tf.train.AdamOptimizer(learning_rate).minimize(G_loss, var_list=g_vars)

# Define the batch size and number of epochs
batch_size = 100
epochs = 100
samples = []

# Start the TensorFlow session
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    # Train the DCGAN
    for epoch in range(epochs):
        for i in range(mnist.train.num_examples // batch_size):
            batch = mnist.train.next_batch(batch_size)

            # Generate random noise for the generator input
            batch_images = batch[0].reshape((batch_size, 784))
            batch_images = batch_images * 2 - 1
            batch_noise = np.random.uniform(-1, 1, size=(batch_size, 100))

            # Train the discriminator on real and fake images
            _ = sess.run(D_trainer, feed_dict={real_images: batch_images, z: batch_noise})

            # Train the generator by fooling the discriminator
            _ = sess.run(G_trainer, feed_dict={z: batch_noise})

        # Print the loss function for discriminator and generator after each epoch
        print("Epoch {} completed. D Loss: {:.4f}, G Loss: {:.4f}".format(epoch+1, sess.run(D_loss, feed_dict={real_images: batch_images, z: batch_noise}), sess.run(G_loss, feed_dict={z: batch_noise})))

        # Save generated images for testing
        sample_noise = np.random.uniform(-1, 1, size=(1, 100))
        gen_sample = sess.run(generator(z, reuse=True), feed_dict={z: sample_noise})
        samples.append(gen_sample)

# Plot the generated images
fig, axes = plt.subplots(nrows=1, ncols=10, sharex=True, sharey=True, figsize=(20, 2))
for img, ax in zip(samples[0], axes):
    ax.imshow(img.reshape((28, 28)), cmap='Greys_r')
    ax.xaxis.set_visible(False)
    ax.yaxis.set_visible(False)
plt.show()