cnn_train.py 文件源码

def test(self, cgp, model_file, comp_graph='comp_graph.dot', batchsize=256):
        chainer.cuda.get_device(0).use()  # Make a specified GPU current
        model = CGP2CNN(cgp, self.n_class)
        print('\tLoad model from', model_file)
        serializers.load_npz(model_file, model)
        model.to_gpu(0)
        test_accuracy, test_loss = self.__test(model, batchsize)
        print('\tparamNum={}'.format(model.param_num))
        print('\ttest mean loss={}, test accuracy={}'.format(test_loss / self.test_data_num, test_accuracy / self.test_data_num))

        if comp_graph is not None:
            with open(comp_graph, 'w') as o:
                g = computational_graph.build_computational_graph((model.loss,))
                o.write(g.dump())
                del g
                print('\tCNN graph generated ({}).'.format(comp_graph))

        return test_accuracy, test_loss