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#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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# File: lenet.py
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# datetime: 2020/8/7 21:24
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# software: PyCharm
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import torch.nn as nn
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import torch
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import torch.optim as optim
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import numpy as np
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import struct
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import matplotlib.pyplot as plt
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def data_fetch_preprocessing():
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train_image = open('train-images.idx3-ubyte', 'rb')
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test_image = open('t10k-images.idx3-ubyte', 'rb')
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train_label = open('train-labels.idx1-ubyte', 'rb')
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test_label = open('t10k-labels.idx1-ubyte', 'rb')
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magic, n = struct.unpack('>II',
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train_label.read(8))
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# 原始数据的标签
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y_train_label = np.array(np.fromfile(train_label,
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dtype=np.uint8), ndmin=1)
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y_train = np.ones((10, 60000)) * 0.01
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for i in range(60000):
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y_train[y_train_label[i]][i] = 0.99
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# 测试数据的标签
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magic_t, n_t = struct.unpack('>II',
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test_label.read(8))
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y_test = np.fromfile(test_label,
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dtype=np.uint8).reshape(10000, 1)
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# print(y_train[0])
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# 训练数据共有60000个
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# print(len(labels))
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magic, num, rows, cols = struct.unpack('>IIII', train_image.read(16))
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x_train = np.fromfile(train_image, dtype=np.uint8).reshape(len(y_train_label), 784)
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magic_2, num_2, rows_2, cols_2 = struct.unpack('>IIII', test_image.read(16))
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x_test = np.fromfile(test_image, dtype=np.uint8).reshape(len(y_test), 784)
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# print(x_train.shape)
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# 可以通过这个函数观察图像
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# data=x_train[:,0].reshape(28,28)
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# plt.imshow(data,cmap='Greys',interpolation=None)
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# plt.show()
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# 关闭打开的文件
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train_image.close()
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train_label.close()
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test_image.close()
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test_label.close()
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return x_train, y_train_label, x_test, y_test
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class convolution_neural_network(nn.Module):
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def __init__(self):
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super(convolution_neural_network, self).__init__()
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# 定义卷积层
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self.conv = nn.Sequential(
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nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, stride=1, padding=0), # 28x28x1-->24x24x6
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nn.Sigmoid(),
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nn.MaxPool2d(kernel_size=2, stride=2), # 12x12x6
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nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, stride=1, padding=0), # 8x8x16
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nn.Sigmoid(),
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nn.MaxPool2d(kernel_size=2, stride=2) # 4x4x16
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)
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self.fc = nn.Sequential(
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nn.Linear(in_features=256, out_features=120),
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nn.Sigmoid(),
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nn.Linear(in_features=120, out_features=84),
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nn.Sigmoid(),
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nn.Linear(in_features=84, out_features=10),
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)
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def forward(self, img):
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feature = self.conv(img)
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output = self.fc(feature.view(img.shape[0], -1))
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return output
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if __name__ == '__main__':
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# 获取数据
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x_train, y_train, x_test, y_test = data_fetch_preprocessing()
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x_train = x_train.reshape(60000, 1, 28, 28)
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# 建立模型实例
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LeNet = convolution_neural_network()
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# plt.imshow(x_train[2][0], cmap='Greys', interpolation=None)
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# plt.show()
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# 交叉熵损失函数
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loss_function = nn.CrossEntropyLoss()
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loss_list = []
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optimizer = optim.Adam(params=LeNet.parameters(), lr=0.001)
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# epoch = 5
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for e in range(5):
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precision = 0
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for i in range(60000):
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prediction = LeNet(torch.tensor(x_train[i]).float().reshape(-1, 1, 28, 28))
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# print(prediction)
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# print(torch.from_numpy(y_train[i]).reshape(1,-1))
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# exit(-1)
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if torch.argmax(prediction) == y_train[i]:
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precision += 1
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loss = loss_function(prediction, torch.tensor([y_train[i]]).long())
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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loss_list.append(loss)
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print('第%d轮迭代，loss=%.3f，准确率：%.3f' % (e, loss_list[-1],precision/60000)) |
