应用生成对抗网络GAN生成图片或者视频的代码
时间: 2023-09-19 21:08:07 浏览: 53
以下是一个简单的基于TensorFlow的GAN生成图片的代码示例:
```
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
# 定义生成器网络
def generator(z, reuse=None):
with tf.variable_scope('gen', reuse=reuse):
# 定义全连接层和激活函数
hidden1 = tf.layers.dense(inputs=z, units=128, activation=tf.nn.leaky_relu)
# 定义输出层和激活函数
hidden2 = tf.layers.dense(inputs=hidden1, units=784, activation=tf.nn.tanh)
output = tf.reshape(hidden2, shape=[-1, 28, 28])
return output
# 定义判别器网络
def discriminator(X, reuse=None):
with tf.variable_scope('dis', reuse=reuse):
# 定义全连接层和激活函数
hidden1 = tf.layers.dense(inputs=tf.reshape(X, shape=[-1, 784]), units=128, activation=tf.nn.leaky_relu)
# 定义输出层和激活函数
logits = tf.layers.dense(inputs=hidden1, units=1)
output = tf.sigmoid(logits)
return output, logits
# 定义噪声向量维度
z_dim = 100
# 定义占位符
X = tf.placeholder(tf.float32, shape=[None, 28, 28], name='X')
z = tf.placeholder(tf.float32, shape=[None, z_dim], name='z')
# 定义生成器和判别器
G = generator(z)
D_output_real, D_logits_real = discriminator(X)
D_output_fake, D_logits_fake = discriminator(G, reuse=True)
# 定义损失函数
D_real_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logits_real, labels=tf.ones_like(D_logits_real)))
D_fake_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logits_fake, labels=tf.zeros_like(D_logits_fake)))
D_loss = D_real_loss + D_fake_loss
G_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logits_fake, labels=tf.ones_like(D_logits_fake)))
# 定义优化器
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)
# 加载MNIST数据集
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/")
# 定义会话并训练模型
batch_size = 100
epochs = 100
samples = []
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for e in range(epochs):
for i in range(mnist.train.num_examples // batch_size):
batch = mnist.train.next_batch(batch_size)
batch_images = batch[0].reshape((batch_size, 28, 28))
batch_images = batch_images * 2 - 1
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
_ = sess.run(D_trainer, feed_dict={X: batch_images, z: batch_z})
_ = sess.run(G_trainer, feed_dict={z: batch_z})
print("Epoch: {}/{}".format(e+1, epochs))
# 每个epoch结束后,将生成器生成的图像保存到样本列表中
sample_z = np.random.uniform(-1, 1, size=(1, z_dim))
gen_sample = sess.run(generator(z, reuse=True), feed_dict={z: sample_z})
samples.append(gen_sample)
# 将样本列表中的图像展示出来
fig, axes = plt.subplots(nrows=10, ncols=10, figsize=(20, 20))
for i in range(10):
for j in range(10):
axes[i][j].imshow(samples[(i*10)+j][0], cmap='gray')
axes[i][j].axis('off')
plt.show()
```
这个代码实现的是一个简单的基于全连接层的GAN,可以生成28x28大小的手写数字图像。如果需要生成视频,需要使用GAN的变种,例如Conditional GAN(CGAN)或Variational Autoencoder GAN (VAE-GAN)等。