dcgan_like_sample.py 文件源码

def generator(z, label):
    z = tf.concat(1, [z,label])
    train = ly.fully_connected(
        z, 1024, activation_fn=tf.nn.relu, normalizer_fn=ly.batch_norm)
    train = tf.concat(1, [train, label])
    train = ly.fully_connected(
        z, 4*4*512, activation_fn=tf.nn.relu, normalizer_fn=ly.batch_norm)
    train = tf.reshape(train, (-1, 4, 4, 512))
    yb = tf.ones([FLAGS.batch_size, 4, 4, 10])*tf.reshape(label, [FLAGS.batch_size, 1, 1, 10]) 
    train = tf.concat(3, [train, yb])
    train = ly.conv2d_transpose(train, 256, 3, stride=2,
                                activation_fn=tf.nn.relu, normalizer_fn=ly.batch_norm, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02))
    train = ly.conv2d_transpose(train, 128, 3, stride=2,
                                activation_fn=tf.nn.relu, normalizer_fn=ly.batch_norm, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02))
    train = ly.conv2d_transpose(train, 64, 3, stride=2,
                                activation_fn=tf.nn.relu, normalizer_fn=ly.batch_norm, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02))
    train = ly.conv2d_transpose(train, 1, 3, stride=1,
                                activation_fn=tf.nn.tanh, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02))
    return train