conv_param={ 'filter_num': (32, 32, 64), 'filter_size': 3, 'pad': 1, 'stride': 1 }, hidden_size=512, output_size=10, weight_init_std=0.01) # パラメータの復帰 network.load_params("params.pkl") print("Loaded Network Parameters!") start = time.time() test_acc = network.accuracy(x_test, t_test) elapsed_time = time.time() - start print("=== " + "test acc:" + str(test_acc) + " ===") print("elapsed_time:{0}".format(elapsed_time) + "[sec]") #network.save_params("params.pkl") #print("Saved Network Parameters!") np.savetxt('W1.h', network.params['W1'].reshape(32, -1), delimiter=',', newline=',\n', header='float W1[32][27]={', footer='};', comments='') np.savetxt('mean1.h',
x_test, t_test = x_test[:1000], t_test[:1000] # ハイパーパラメータの設定 max_epochs = 20 batch_size = 50 model = SimpleConvNet(input_dim=(1, 28, 28), conv_param={ 'filter_num': 30, 'filter_size': 5, 'pad': 0, 'stride': 1 }, hidden_size=100, output_size=10, weight_init_std=0.01) optimizer = AdaGrad(lr=0.001) trainer = Trainer(model, optimizer) trainer.fit(x_train, t_train, x_test, t_test, max_epochs, batch_size, eval_interval=10) # グラフの描画 trainer.plot() print('test accuracy : ' + str(model.accuracy(x_test, t_test)))