GAN_models.py 文件源码

def _generator(self, z, dims, train_phase, activation=tf.nn.relu, scope_name="generator"):
        N = len(dims)
        image_size = self.resized_image_size // (2 ** (N - 1))
        with tf.variable_scope(scope_name) as scope:
            W_z = utils.weight_variable([self.z_dim, dims[0] * image_size * image_size], name="W_z")
            b_z = utils.bias_variable([dims[0] * image_size * image_size], name="b_z")
            h_z = tf.matmul(z, W_z) + b_z
            h_z = tf.reshape(h_z, [-1, image_size, image_size, dims[0]])
            h_bnz = utils.batch_norm(h_z, dims[0], train_phase, scope="gen_bnz")
            h = activation(h_bnz, name='h_z')
            utils.add_activation_summary(h)

            for index in range(N - 2):
                image_size *= 2
                W = utils.weight_variable([5, 5, dims[index + 1], dims[index]], name="W_%d" % index)
                b = utils.bias_variable([dims[index + 1]], name="b_%d" % index)
                deconv_shape = tf.pack([tf.shape(h)[0], image_size, image_size, dims[index + 1]])
                h_conv_t = utils.conv2d_transpose_strided(h, W, b, output_shape=deconv_shape)
                h_bn = utils.batch_norm(h_conv_t, dims[index + 1], train_phase, scope="gen_bn%d" % index)
                h = activation(h_bn, name='h_%d' % index)
                utils.add_activation_summary(h)

            image_size *= 2
            W_pred = utils.weight_variable([5, 5, dims[-1], dims[-2]], name="W_pred")
            b_pred = utils.bias_variable([dims[-1]], name="b_pred")
            deconv_shape = tf.pack([tf.shape(h)[0], image_size, image_size, dims[-1]])
            h_conv_t = utils.conv2d_transpose_strided(h, W_pred, b_pred, output_shape=deconv_shape)
            pred_image = tf.nn.tanh(h_conv_t, name='pred_image')
            utils.add_activation_summary(pred_image)

        return pred_image