model.py 文件源码

def domain_classifier(self, images, name="G", reuse=False): 
    random_uniform_init = tf.random_uniform_initializer(minval=-0.1, maxval=0.1)    
    with tf.variable_scope(name):
        tf.get_variable_scope().reuse_variables()
        with tf.variable_scope("images"):
                # "generator/images"
                images_W = tf.get_variable("images_W", [self.img_dims, self.G_hidden_size], "float32", random_uniform_init)
        images_emb = tf.matmul(images, images_W)    # B,H

        l2_loss = tf.constant(0.0)
    with tf.variable_scope("domain"):
        if reuse:
        tf.get_variable_scope().reuse_variables()
        with tf.variable_scope("output"):
            output_W = tf.get_variable("output_W", [self.G_hidden_size, self.num_domains],
                                                "float32", random_uniform_init)
                output_b = tf.get_variable("output_b", [self.num_domains], "float32", random_uniform_init)
        l2_loss += tf.nn.l2_loss(output_W)
        l2_loss += tf.nn.l2_loss(output_b)
        logits = tf.nn.xw_plus_b(images_emb, output_W, output_b, name="logits")
        predictions = tf.argmax(logits, 1, name="predictions")

        return predictions, logits, l2_loss