generate_from_ckpt.py 文件源码

def generator(z):
    weights = slim.model_variable(
        'fn_weights', shape=(FLAGS.z_dim, 4 * 4 * 512), initializer=ly.xavier_initializer())
    bias = slim.model_variable(
        'fn_bias', shape=(4 * 4 * 512, ), initializer=tf.zeros_initializer)
    train = tf.nn.relu(fully_connected(z, weights, bias))
    train = tf.reshape(train, (-1, 4, 4, 512))
    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=None, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02), biases_initializer=None)
    bias = slim.model_variable('bias', shape=(
        1, ), initializer=tf.zeros_initializer)
    train += bias
    train = tf.nn.tanh(train)
    return train