def main(): (x_train, t_train), (x_test, t_test) = load_mnist(flatten=False) # 処理に時間のかかる場合はデータを削除 #x_train, t_train = x_train[:5000], t_train[:5000] #x_test, t_test = x_test[:1000], t_test[:1000] max_epochs = 20 network = 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) trainer = Trainer(network, x_train, t_train, x_test, t_test, epochs=max_epochs, mini_batch_size=100, optimizer='Adam', optimizer_param={'lr': 0.001}, evaluate_sample_num_per_epoch=1000) trainer.train() # パラメータの保存 network.save_params("params.pkl") print("Saved Network Parameters!") # グラフの描画 markers = {'train': 'o', 'test': 's'} x = np.arange(max_epochs) plt.plot(x, trainer.train_acc_list, marker='o', label='train', markevery=2) plt.plot(x, trainer.train_acc_list, marker='s', label='test', markevery=2) plt.xlabel = ("epochs") plt.ylabel = ("accuracy") plt.ylim(0, 1.0) plt.legend(loc='lower right') plt.show()
# t_test = t_test[:1000] max_epochs = 20 network = 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) trainer = Trainer(network, x_train, t_train, x_test, t_test, epochs=max_epochs, mini_batch_size=100, optimizer='Adam', optimizer_param={'lr': 0.001}, evaluate_sample_num_per_epoch=1000) trainer.train() # 保存参数 network.save_params('params.pkl') print('Saved Network Parameters!') # 绘制图表 x = np.arange(max_epochs) plt.plot(x, trainer.train_acc_list, marker='o', label='train', markevery=2) plt.plot(x, trainer.test_acc_list, marker='s', label='test', markevery=2) plt.xlabel('epochs') plt.ylabel('accuracy') plt.ylim(0, 1.0) plt.legend(loc='lower right') plt.show()
hidden_size=100, output_size=10, weight_init_std=0.01) trainer = Trainer(network, x_train, t_train, x_test, t_test, epochs=20, mini_batch_size=100, optimizer='Adam', optimizer_param={'lr': 0.001}, evaluate_sample_num_per_epoch=1000) trainer.train() # パラメータの保存 network.save_params("params.pkl") print("Saved Network Parameters!") # グラフの描画 markers = {'train': 'o', 'test': 's'} x = np.arange(max_epochs) plt.plot(x, train_acc_list, marker='o', label='train', markevery=2) plt.plot(x, test_acc_list, marker='s', label='test', markevery=2) plt.xlabel("epochs") plt.ylabel("accuracy") plt.ylim(0, 1.0) plt.legend(loc='lower right') plt.show()
# 処理に時間のかかる場合はデータを削減 #x_train, t_train = x_train[:5000], t_train[:5000] #x_test, t_test = x_test[:1000], t_test[:1000] max_epochs = 20 network = 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) trainer = Trainer(network, x_train, t_train, x_test, t_test, epochs=max_epochs, mini_batch_size=100, optimizer='Adam', optimizer_param={'lr': 0.001}, evaluate_sample_num_per_epoch=1000) trainer.train() # パラメータの保存 network.save_params("params.pkl") print("Saved Network Parameters!") # グラフの描画 markers = {'train': 'o', 'test': 's'} x = np.arange(max_epochs) plt.plot(x, trainer.train_acc_list, marker='o', label='train', markevery=2) plt.plot(x, trainer.test_acc_list, marker='s', label='test', markevery=2) plt.xlabel("epochs") plt.ylabel("accuracy") plt.ylim(0, 1.0) plt.legend(loc='lower right') plt.show()
epochs=max_epochs, mini_batch_size=100, optimizer="Adam", optimizer_param={"lr": 0.001}, evaluate_sample_num_per_epoch=1000) trainer.train(size, tmp_acc_list) #acc_dict[size]=sum(tmp_acc_list)/10 #print(tmp_acc_list) #print(acc_dict) """ with open ( "each_acc.txt", mode = "w") as f: for key,value in acc_dict.items(): f.write(f'{key} {value}\n') """ #パラメータの保存 network.save_params("params_color.pkl") print("Saved Network Parameters!") """ #グラフの描画 markers = {"train":"o", "test":"s"} x=np.arange(max_epochs) plt.plot(x, trainer.train_acc_list, marker='o', label='train', markevery=2) plt.plot(x, trainer.test_acc_list, marker='s', label='test', markevery=2) plt.xlabel("epochs") plt.ylabel("accuracy") plt.ylim(0, 1.0) plt.legend(loc='lower right') plt.show() """
hidden_size=100, output_size=10, weight_init_std=0.01) trainer = Trainer(network, x_train, t_train, x_test, t_test, epochs=max_epochs, mini_batch_size=100, optimizer='Adam', optimizer_param={'lr': 0.001}, evaluate_sample_num_per_epoch=1000) trainer.train() # 保存参数 network.save_params("db/param_result/conv_params.pkl") print("Saved Network Parameters!") # 绘制图形 markers = {'train': 'o', 'test': 's'} x = np.arange(max_epochs) plt.plot(x, trainer.train_acc_list, marker='o', label='train', markevery=2) plt.plot(x, trainer.test_acc_list, marker='s', label='test', markevery=2) plt.xlabel("epochs") plt.ylabel("accuracy") plt.ylim(0, 1.0) plt.legend(loc='lower right') plt.show()