train_cifar_feature_matching_ali_tf.py 文件源码

def generator(input_latent):
    # input_latent = Input(batch_shape=noise_dim, dtype=im_dtype)
    with tf.variable_scope('Net_Gen') as scope:
        xx = layers.fully_connected(input_latent, num_outputs=4*4*512, activation_fn=None)
        xx = layers.batch_norm(xx)
        xx = tf.nn.relu(xx)
        xx = tf.reshape(xx, (batch_size, 4,4,512))
        xx = layers.conv2d_transpose(xx, 256, kernel_size=(5,5), stride=(2, 2), padding='SAME', activation_fn=None)
        xx = layers.batch_norm(xx)
        xx = tf.nn.relu(xx)
        xx = layers.conv2d_transpose(xx, 128, kernel_size=(5,5), stride=(2, 2), padding='SAME', activation_fn=None)
        xx = layers.batch_norm(xx)
        xx = tf.nn.relu(xx)    
        xx = layers.conv2d_transpose(xx, 3, kernel_size=(5,5), stride=(2, 2),  padding='SAME', activation_fn=None)
        xx = layers.batch_norm(xx)
        gen_dat = tf.nn.tanh(xx)

    return gen_dat