使用 TensorFlow 的简单的 GAN

您可以按照 Jupyter 笔记本中的代码ch-14a_SimpleGAN。

为了使用 TensorFlow 构建 GAN，我们使用以下步骤构建三个网络，两个判别器模型和一个生成器模型：

1. 首先添加用于定义网络的超参数：

# graph hyperparameters
g_learning_rate = 0.00001
d_learning_rate = 0.01
n_x = 784  # number of pixels in the MNIST image 

# number of hidden layers for generator and discriminator
g_n_layers = 3
d_n_layers = 1
# neurons in each hidden layer
g_n_neurons = [256, 512, 1024]
d_n_neurons = [256]

# define parameter ditionary
d_params = {}
g_params = {}

activation = tf.nn.leaky_relu
w_initializer = tf.glorot_uniform_initializer
b_initializer = tf.zeros_initializer

1. 接下来，定义生成器网络：

z_p = tf.placeholder(dtype=tf.float32, name='z_p', 
        shape=[None, n_z])
layer = z_p

# add generator network weights, biases and layers
with tf.variable_scope('g'):
 for i in range(0, g_n_layers):  w_name = 'w_{0:04d}'.format(i)
        g_params[w_name] = tf.get_variable(
            name=w_name,
            shape=[n_z if i == 0 else g_n_neurons[i - 1], 
                    g_n_neurons[i]],
            initializer=w_initializer())
        b_name = 'b_{0:04d}'.format(i)
        g_params[b_name] = tf.get_variable(
            name=b_name, shape=[g_n_neurons[i]], 
            initializer=b_initializer())
        layer = activation(
            tf.matmul(layer, g_params[w_name]) + g_params[b_name])
    # output (logit) layer
  i = g_n_layers
    w_name = 'w_{0:04d}'.format(i)
    g_params[w_name] = tf.get_variable(
        name=w_name,
        shape=[g_n_neurons[i - 1], n_x],
        initializer=w_initializer())
    b_name = 'b_{0:04d}'.format(i)
    g_params[b_name] = tf.get_variable(
        name=b_name, shape=[n_x], initializer=b_initializer())
    g_logit = tf.matmul(layer, g_params[w_name]) + g_params[b_name]
    g_model = tf.nn.tanh(g_logit)

1. 接下来，定义我们将构建的两个判别器网络的权重和偏差：

with tf.variable_scope('d'):
 for i in range(0, d_n_layers):  w_name = 'w_{0:04d}'.format(i)
        d_params[w_name] = tf.get_variable(
            name=w_name,
            shape=[n_x if i == 0 else d_n_neurons[i - 1], 
                    d_n_neurons[i]],
            initializer=w_initializer())

        b_name = 'b_{0:04d}'.format(i)
        d_params[b_name] = tf.get_variable(
            name=b_name, shape=[d_n_neurons[i]], 
            initializer=b_initializer())

    #output (logit) layer
  i = d_n_layers
    w_name = 'w_{0:04d}'.format(i)
    d_params[w_name] = tf.get_variable(
        name=w_name, shape=[d_n_neurons[i - 1], 1], 
        initializer=w_initializer())

    b_name = 'b_{0:04d}'.format(i)
    d_params[b_name] = tf.get_variable(
        name=b_name, shape=[1], initializer=b_initializer())

1. 现在使用这些参数，构建将真实图像作为输入并输出分类的判别器：

# define discriminator_real

# input real images
x_p = tf.placeholder(dtype=tf.float32, name='x_p', 
        shape=[None, n_x])

layer = x_p

with tf.variable_scope('d'):
 for i in range(0, d_n_layers):  w_name = 'w_{0:04d}'.format(i)
        b_name = 'b_{0:04d}'.format(i)

        layer = activation(
            tf.matmul(layer, d_params[w_name]) + d_params[b_name])
        layer = tf.nn.dropout(layer,0.7)
    #output (logit) layer
  i = d_n_layers
    w_name = 'w_{0:04d}'.format(i)
    b_name = 'b_{0:04d}'.format(i)
    d_logit_real = tf.matmul(layer, 
        d_params[w_name]) + d_params[b_name]
    d_model_real = tf.nn.sigmoid(d_logit_real)

1. 接下来，使用相同的参数构建另一个判别器网络，但提供生成器的输出作为输入：

# define discriminator_fake

# input generated fake images
z = g_model
layer = z

with tf.variable_scope('d'):
 for i in range(0, d_n_layers):  w_name = 'w_{0:04d}'.format(i)
        b_name = 'b_{0:04d}'.format(i)
        layer = activation(
            tf.matmul(layer, d_params[w_name]) + d_params[b_name])
        layer = tf.nn.dropout(layer,0.7)
    #output (logit) layer
  i = d_n_layers
    w_name = 'w_{0:04d}'.format(i)
    b_name = 'b_{0:04d}'.format(i)
    d_logit_fake = tf.matmul(layer, 
        d_params[w_name]) + d_params[b_name]
    d_model_fake = tf.nn.sigmoid(d_logit_fake)

1. 现在我们已经建立了三个网络，它们之间的连接是使用损失，优化器和训练函数完成的。在训练生成器时，我们只训练生成器的参数，在训练判别器时，我们只训练判别器的参数。我们使用var_list参数将此指定给优化器的minimize()函数。以下是为两种网络定义损失，优化器和训练函数的完整代码：

g_loss = -tf.reduce_mean(tf.log(d_model_fake))
d_loss = -tf.reduce_mean(tf.log(d_model_real) + tf.log(1 - d_model_fake))

g_optimizer = tf.train.AdamOptimizer(g_learning_rate)
d_optimizer = tf.train.GradientDescentOptimizer(d_learning_rate)

g_train_op = g_optimizer.minimize(g_loss, 
                var_list=list(g_params.values()))
d_train_op = d_optimizer.minimize(d_loss, 
                var_list=list(d_params.values()))

1. 现在我们已经定义了模型，我们必须训练模型。训练按照以下算法完成：

For each epoch:
 For each batch:  get real images x_batch
    generate noise z_batch
    train discriminator using z_batch and x_batch
    generate noise z_batch
    train generator using z_batch

笔记本电脑的完整训练代码如下：

n_epochs = 400
batch_size = 100
n_batches = int(mnist.train.num_examples / batch_size)
n_epochs_print = 50

with tf.Session() as tfs:
  tfs.run(tf.global_variables_initializer())
    for epoch in range(n_epochs):
  epoch_d_loss = 0.0
        epoch_g_loss = 0.0
        for batch in range(n_batches):
  x_batch, _ = mnist.train.next_batch(batch_size)
            x_batch = norm(x_batch)
            z_batch = np.random.uniform(-1.0,1.0,size=[batch_size,n_z])
            feed_dict = {x_p: x_batch,z_p: z_batch}
            _,batch_d_loss = tfs.run([d_train_op,d_loss], 
                                    feed_dict=feed_dict)
            z_batch = np.random.uniform(-1.0,1.0,size=[batch_size,n_z])
            feed_dict={z_p: z_batch}
            _,batch_g_loss = tfs.run([g_train_op,g_loss], 
                                    feed_dict=feed_dict)
            epoch_d_loss += batch_d_loss 
            epoch_g_loss += batch_g_loss

        if epoch%n_epochs_print == 0:
  average_d_loss = epoch_d_loss / n_batches
            average_g_loss = epoch_g_loss / n_batches
            print('epoch: {0:04d}   d_loss = {1:0.6f}  g_loss = {2:0.6f}'
                  .format(epoch,average_d_loss,average_g_loss))
            # predict images using generator model trained            
 x_pred = tfs.run(g_model,feed_dict={z_p:z_test})
            display_images(x_pred.reshape(-1,pixel_size,pixel_size))

我们每 50 个周期印刷生成的图像：

正如我们所看到的那样，生成器在周期 0 中只产生噪声，但是在周期 350 中，它经过训练可以产生更好的手写数字形状。您可以尝试使用周期，正则化，网络架构和其他超参数进行试验，看看是否可以产生更快更好的结果。