Method build_graph

examples/GAN/Image2Image.py:119–143

(self, input, output)

        return l

    def build_graph(self, input, output):
        input, output = input / 128.0 - 1, output / 128.0 - 1

        with argscope([Conv2D, Conv2DTranspose], kernel_initializer=tf.truncated_normal_initializer(stddev=0.02)):
            with tf.variable_scope('gen'):
                fake_output = self.generator(input)
            with tf.variable_scope('discrim'):
                real_pred = self.discriminator(input, output)
                fake_pred = self.discriminator(input, fake_output)

        self.build_losses(real_pred, fake_pred)
        errL1 = tf.reduce_mean(tf.abs(fake_output - output), name='L1_loss')
        self.g_loss = tf.add(self.g_loss, LAMBDA * errL1, name='total_g_loss')
        add_moving_summary(errL1, self.g_loss)

        # tensorboard visualization
        if IN_CH == 1:
            input = tf.image.grayscale_to_rgb(input)
        if OUT_CH == 1:
            output = tf.image.grayscale_to_rgb(output)
            fake_output = tf.image.grayscale_to_rgb(fake_output)

        visualize_tensors('input,output,fake', [input, output, fake_output], max_outputs=max(30, BATCH))

        self.collect_variables()

    def optimizer(self):
        lr = tf.get_variable('learning_rate', initializer=2e-4, trainable=False)