train_cifar_feature_matching_ali_tf.py 文件源码

def inference(input_img):
    # input_latent = Input(batch_shape=noise_dim, dtype=im_dtype)
    with tf.variable_scope('Net_Inf') as scope:
        xx = layers.convolution2d(input_img, 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.convolution2d(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.convolution2d(xx, 512, kernel_size=(5,5), stride=(2, 2),  padding='SAME', activation_fn=None)
        xx = layers.batch_norm(xx)
        xx = tf.nn.relu(xx)  
        xx = layers.flatten(xx)
        xx = layers.fully_connected(xx, num_outputs=latent_size, activation_fn=None)
        xx = layers.batch_norm(xx)
        inf_latent = tf.nn.tanh(xx)
    return inf_latent

# specify discriminative model