gan.py 文件源码

def discriminator(self, opts, input_, is_training,
                      prefix='DISCRIMINATOR', reuse=False):

        shape = tf.shape(input_)
        num = shape[0]

        with tf.variable_scope(prefix, reuse=reuse):
            h0 = input_
            h0 = tf.add(h0, tf.random_normal(shape, stddev=0.3))
            h0 = ops.linear(opts, h0, 1000, scope='h0_linear')
            # h0 = ops.batch_norm(opts, h0, is_training, reuse, scope='bn_layer1')
            h0 = tf.nn.relu(h0)
            h1 = tf.add(h0, tf.random_normal([num, 1000], stddev=0.5))
            h1 = ops.linear(opts, h1, 500, scope='h1_linear')
            # h1 = ops.batch_norm(opts, h1, is_training, reuse, scope='bn_layer2')
            h1 = tf.nn.relu(h1)
            h2 = tf.add(h1, tf.random_normal([num, 500], stddev=0.5))
            h2 = ops.linear(opts, h2, 250, scope='h2_linear')
            # h2 = ops.batch_norm(opts, h2, is_training, reuse, scope='bn_layer3')
            h2 = tf.nn.relu(h2)
            h3 = tf.add(h2, tf.random_normal([num, 250], stddev=0.5))
            h3 = ops.linear(opts, h3, 250, scope='h3_linear')
            # h3 = ops.batch_norm(opts, h3, is_training, reuse, scope='bn_layer4')
            h3 = tf.nn.relu(h3)
            h4 = tf.add(h3, tf.random_normal([num, 250], stddev=0.5))
            h4 = ops.linear(opts, h4, 250, scope='h4_linear')
            # h4 = ops.batch_norm(opts, h4, is_training, reuse, scope='bn_layer5')
            h4 = tf.nn.relu(h4)
            h5 = ops.linear(opts, h4, 10, scope='h5_linear')

        return h5, h3