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43 lines
1.4 KiB
43 lines
1.4 KiB
# coding: utf-8
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import sys, os
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sys.path.append(os.pardir) # 为了导入父目录的文件而进行的设定
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import numpy as np
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import matplotlib.pyplot as plt
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from dataset.mnist import load_mnist
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from simple_convnet import SimpleConvNet
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from common.trainer import Trainer
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# 读入数据
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(x_train, t_train), (x_test, t_test) = load_mnist(flatten=False)
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# 处理花费时间较长的情况下减少数据
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#x_train, t_train = x_train[:5000], t_train[:5000]
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#x_test, t_test = x_test[:1000], t_test[:1000]
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max_epochs = 20
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network = SimpleConvNet(input_dim=(1,28,28),
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conv_param = {'filter_num': 30, 'filter_size': 5, 'pad': 0, 'stride': 1},
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hidden_size=100, output_size=10, weight_init_std=0.01)
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trainer = Trainer(network, x_train, t_train, x_test, t_test,
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epochs=max_epochs, mini_batch_size=100,
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optimizer='Adam', optimizer_param={'lr': 0.001},
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evaluate_sample_num_per_epoch=1000)
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trainer.train()
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# 保存参数
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network.save_params("params.pkl")
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print("Saved Network Parameters!")
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# 绘制图形
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markers = {'train': 'o', 'test': 's'}
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x = np.arange(max_epochs)
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plt.plot(x, trainer.train_acc_list, marker='o', label='train', markevery=2)
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plt.plot(x, trainer.test_acc_list, marker='s', label='test', markevery=2)
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plt.xlabel("epochs")
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plt.ylabel("accuracy")
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plt.ylim(0, 1.0)
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plt.legend(loc='lower right')
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plt.show()
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