GAN_models.py 文件源码

def _discriminator(self, input_images, dims, train_phase, activation=tf.nn.relu, scope_name="discriminator",
                       scope_reuse=False):
        N = len(dims)
        with tf.variable_scope(scope_name) as scope:
            if scope_reuse:
                scope.reuse_variables()
            h = input_images
            skip_bn = True  # First layer of discriminator skips batch norm
            for index in range(N - 2):
                W = utils.weight_variable([4, 4, dims[index], dims[index + 1]], name="W_%d" % index)
                b = tf.zeros([dims[index+1]])
                h_conv = utils.conv2d_strided(h, W, b)
                if skip_bn:
                    h_bn = h_conv
                    skip_bn = False
                else:
                    h_bn = utils.batch_norm(h_conv, dims[index + 1], train_phase, scope="disc_bn%d" % index)
                h = activation(h_bn, name="h_%d" % index)
                utils.add_activation_summary(h)

            W_pred = utils.weight_variable([4, 4, dims[-2], dims[-1]], name="W_pred")
            b = tf.zeros([dims[-1]])
            h_pred = utils.conv2d_strided(h, W_pred, b)
        return None, h_pred, None  # Return the last convolution output. None values are returned to maintatin disc from other GAN