一个简单的卷积神经网络——train

# 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_convolution_net import SimpleConvNet from 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 # ------------------------------------------创建网络--------------------------------------------------- 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) # -------------------------------------------训练网络--------------------------------------------------- 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()