DCGAN实战

建立神经网络

我们将通过部署以下函数来建立GANs的主要部分

• model_input
• discirminator
• generator
• model_loss
• model_opt
• train

Input

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(tf.float32, [None,image_width,image_height,image_channels],name = 'input_real')
    input_z=tf.placeholder(tf.float32,[None, z_dim],name= 'input_z')
    lr = tf.placeholder(tf.float32, name='lr')
    return input_real,input_z,lr

discriminator

def discriminator(images, reuse=False,alpha = 0.2):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    with tf.variable_scope('discriminator', reuse=reuse):
        #print(images.shape) 28*28*3
        x1 = tf.layers.conv2d(images, 32, 5, strides=2, padding='same')
        relu1 = tf.maximum(alpha * x1, x1)

        x2 = tf.layers.conv2d(relu1, 64, 5, strides=2, padding='same