train_convnet.py

mnist-master/train_convnet.py

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+# coding: utf-8
+import sys, os
+sys.path.append(os.pardir)  # 为了导入父目录的文件而进行的设定
+import numpy as np
+import matplotlib.pyplot as plt
+from dataset.mnist import load_mnist
+from simple_convnet import SimpleConvNet
+from common.trainer import Trainer
+
+# 读入数据
+(x_train, t_train), (x_test, t_test) = load_mnist(flatten=False)
+
+# 处理花费时间较长的情况下减少数据 
+#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()
+
+# 保存参数
+network.save_params("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()