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([5, 5, dims[index], dims[index + 1]], name="W_%d" % index)
                b = utils.bias_variable([dims[index + 1]], name="b_%d" % index)
                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)

            shape = h.get_shape().as_list()
            image_size = self.resized_image_size // (2 ** (N - 2))  # dims has input dim and output dim
            h_reshaped = tf.reshape(h, [self.batch_size, image_size * image_size * shape[3]])
            W_pred = utils.weight_variable([image_size * image_size * shape[3], dims[-1]], name="W_pred")
            b_pred = utils.bias_variable([dims[-1]], name="b_pred")
            h_pred = tf.matmul(h_reshaped, W_pred) + b_pred

        return tf.nn.sigmoid(h_pred), h_pred, h