不要怂，就是GAN (生成式对抗网络) （三）之 判别器和生成器 TensorFlow Model

            import
             scipy.misc

            import
             numpy as np


            #
             保存图片函数
            def
             save_images(images, size, path):
    
    
            """
            
    Save the samples images
    The best size number is
            int(max(sqrt(image.shape[0]),sqrt(image.shape[1]))) + 1
    example:
        The batch_size is 64, then the size is recommended [8, 8]
        The batch_size is 32, then the size is recommended [6, 6]
    
            """
            #
             图片归一化，主要用于生成器输出是 tanh 形式的归一化
    img = (images + 1.0) / 2.0
    h, w = img.shape[1], img.shape[2]

    # 产生一个大画布，用来保存生成的 batch_size 个图像
    merge_img = np.zeros((h * size[0], w * size[1], 3))

    # 循环使得画布特定地方值为某一幅图像的值for idx, image in enumerate(images):
        i = idx % size[1]
        j = idx // size[1]
        merge_img[j*h:j*h+h, i*w:i*w+w, :] = image
    
    # 保存画布return scipy.misc.imsave(path, merge_img)

            import
             tensorflow as tf

            from ops import *

BATCH_SIZE = 64

# 定义生成器def generator(z, y, train = True):
    # y 是一个 [BATCH_SIZE, 10] 维的向量，把 y 转成四维张量
    yb = tf.reshape(y, [BATCH_SIZE, 1, 1, 10], name = ‘yb‘)
    # 把 y 作为约束条件和 z 拼接起来
    z = tf.concat(1, [z, y], name = ‘z_concat_y‘)
    # 经过一个全连接，BN 和激活层 ReLu
    h1 = tf.nn.relu(batch_norm_layer(fully_connected(z, 1024, ‘g_fully_connected1‘), 
                                     is_train = train, name = ‘g_bn1‘))
    # 把约束条件和上一层拼接起来
    h1 = tf.concat(1, [h1, y], name = ‘active1_concat_y‘)
    
    h2 = tf.nn.relu(batch_norm_layer(fully_connected(h1, 128 * 49, ‘g_fully_connected2‘), 
                                     is_train = train, name = ‘g_bn2‘))
    h2 = tf.reshape(h2, [64, 7, 7, 128], name = ‘h2_reshape‘)
    # 把约束条件和上一层拼接起来
    h2 = conv_cond_concat(h2, yb, name = ‘active2_concat_y‘)

    h3 = tf.nn.relu(batch_norm_layer(deconv2d(h2, [64,14,14,128], 
                                              name = ‘g_deconv2d3‘), 
                                              is_train = train, name = ‘g_bn3‘))
    h3 = conv_cond_concat(h3, yb, name = ‘active3_concat_y‘)
    
    # 经过一个 sigmoid 函数把值归一化为 0~1 之间，
    h4 = tf.nn.sigmoid(deconv2d(h3, [64, 28, 28, 1], 
                                name = ‘g_deconv2d4‘), name = ‘generate_image‘)
    
    return h4

# 定义判别器    def discriminator(image, y, reuse = False):
    
    # 因为真实数据和生成数据都要经过判别器，所以需要指定 reuse 是否可用if reuse:
        tf.get_variable_scope().reuse_variables()

    # 同生成器一样，判别器也需要把约束条件串联进来
    yb = tf.reshape(y, [BATCH_SIZE, 1, 1, 10], name = ‘yb‘)
    x = conv_cond_concat(image, yb, name = ‘image_concat_y‘)
    
    # 卷积，激活，串联条件。
    h1 = lrelu(conv2d(x, 11, name = ‘d_conv2d1‘), name = ‘lrelu1‘)
    h1 = conv_cond_concat(h1, yb, name = ‘h1_concat_yb‘)
    
    h2 = lrelu(batch_norm_layer(conv2d(h1, 74, name = ‘d_conv2d2‘), 
                                name = ‘d_bn2‘), name = ‘lrelu2‘)
    h2 = tf.reshape(h2, [BATCH_SIZE, -1], name = ‘reshape_lrelu2_to_2d‘)
    h2 = tf.concat(1, [h2, y], name = ‘lrelu2_concat_y‘)

    h3 = lrelu(batch_norm_layer(fully_connected(h2, 1024, name = ‘d_fully_connected3‘), 
                                name = ‘d_bn3‘), name = ‘lrelu3‘)
    h3 = tf.concat(1,[h3, y], name = ‘lrelu3_concat_y‘)
    
    # 全连接层，输出以为 loss 值
    h4 = fully_connected(h3, 1, name = ‘d_result_withouts_sigmoid‘)
    
    return tf.nn.sigmoid(h4, name = ‘discriminator_result_with_sigmoid‘), h4
    
# 定义训练过程中的采样函数    def sampler(z, y, train = True):
    tf.get_variable_scope().reuse_variables()
    return generator(z, y, train = train)