PyTorch实现LeNet-5

目录

建立模型 lenet5

训练模型

测试模型

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下面文章写得很好，代码也写得很清晰。

PyTorch实现经典网络之LeNet5 - 简书 (jianshu.com)

数据集：MNIST in CSV | Kaggle

可能是环境不一样，修改了两处代码：

1. train 41行：

loss_list.append(loss.item())

2. test 32行：

plt.plot(testList, accuracy_list, "r-", label="Test")

源代码：

建立模型 lenet5

import torch.nn as nn


class LeNet5(nn.Module):
    def __init__(self):
        super(LeNet5, self).__init__()
        # 包含一个卷积层和池化层，分别对应LeNet5中的C1和S2，
        # 卷积层的输入通道为1，输出通道为6，设置卷积核大小5x5，步长为1
        # 池化层的kernel大小为2x2
        self._conv1 = nn.Sequential(
            nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, stride=1),
            nn.MaxPool2d(kernel_size=2)
        )
        # 包含一个卷积层和池化层，分别对应LeNet5中的C3和S4，
        # 卷积层的输入通道为6，输出通道为16，设置卷积核大小5x5，步长为1
        # 池化层的kernel大小为2x2
        self._conv2 = nn.Sequential(
            nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, stride=1),
            nn.MaxPool2d(kernel_size=2)
        )
        # 对应LeNet5中C5卷积层，由于它跟全连接层类似，所以这里使用了nn.Linear模块
        # 卷积层的输入通特征为4x4x16，输出特征为120x1
        self._fc1 = nn.Sequential(
            nn.Linear(in_features=4 * 4 * 16, out_features=120)
        )
        # 对应LeNet5中的F6，输入是120维向量，输出是84维向量
        self._fc2 = nn.Sequential(
            nn.Linear(in_features=120, out_features=84)
        )
        # 对应LeNet5中的输出层，输入是84维向量，输出是10维向量
        self._fc3 = nn.Sequential(
            nn.Linear(in_features=84, out_features=10)
        )

    def forward(self, input):
        # 前向传播
        # MNIST DataSet image's format is 28x28x1
        # [28,28,1]--->[24,24,6]--->[12,12,6]
        conv1_output = self._conv1(input)
        # [12,12,6]--->[8,8,,16]--->[4,4,16]
        conv2_output = self._conv2(conv1_output)
        # 将[n,4,4,16]维度转化为[n,4*4*16]
        conv2_output = conv2_output.view(-1, 4 * 4 * 16)
        # [n,256]--->[n,120]
        fc1_output = self._fc1(conv2_output)
        # [n,120]-->[n,84]
        fc2_output = self._fc2(fc1_output)
        # [n,84]-->[n,10]
        fc3_output = self._fc3(fc2_output)
        return fc3_output

训练模型

import torch
import torch.nn as nn
import torch.optim as optim

import pandas as pd
import matplotlib.pyplot as plt
from AI.CNN.LeNet_5.lenet5 import LeNet5

train_data = pd.DataFrame(pd.read_csv("Data/mnist_train.csv"))

model = LeNet5()
print(model)

# 定义交叉熵损失函数
loss_fc = nn.CrossEntropyLoss()
# 用model的参数初始化一个随机梯度下降优化器
optimizer = optim.SGD(params=model.parameters(), lr=0.001, momentum=0.78)
loss_list = []
x = []

# 迭代次数1000次
for i in range(1000):
    # 小批量数据集大小设置为30
    batch_data = train_data.sample(n=30, replace=False)
    # 每一条数据的第一个值是标签数据
    batch_y = torch.from_numpy(batch_data.iloc[:, 0].values).long()
    # 图片信息，一条数据784维将其转化为通道数为1，大小28*28的图片。
    batch_x = torch.from_numpy(batch_data.iloc[:, 1::].values).float().view(-1, 1, 28, 28)

    # 前向传播计算输出结果
    prediction = model.forward(batch_x)
    # 计算损失值
    loss = loss_fc(prediction, batch_y)
    # Clears the gradients of all optimized
    optimizer.zero_grad()
    # back propagation algorithm
    loss.backward()
    # Performs a single optimization step (parameter update).
    optimizer.step()
    print("第%d次训练，loss为%.3f" % (i, loss.item()))
    loss_list.append(loss.item())
    x.append(i)

# Saves an object to a disk file.
torch.save(model.state_dict(), "TrainedModel/LeNet5.pkl")
print('Networks''s keys: ', model.state_dict().keys())
print(x)
print(loss_list)

plt.figure()
plt.xlabel("number of epochs")
plt.ylabel("loss")
plt.plot(x, loss_list, "r-")
plt.show()

测试模型

import torch
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from AI.CNN.LeNet_5.lenet5 import LeNet5

model = LeNet5()
test_data = pd.DataFrame(pd.read_csv("Data/mnist_test.csv"))
# Load model parameters
model.load_state_dict(torch.load("TrainedModel/LeNet5.pkl"))

accuracy_list = []
testList = []

with torch.no_grad():
    # 进行一百次测试
    for i in range(100):
        # 每次从测试集中随机挑选50个样本
        batch_data = test_data.sample(n=50, replace=False)
        batch_x = torch.from_numpy(batch_data.iloc[:, 1::].values).float().view(-1, 1, 28, 28)
        batch_y = batch_data.iloc[:, 0].values
        prediction = np.argmax(model(batch_x).numpy(), axis=1)
        acccurcy = np.mean(prediction == batch_y)
        print("第%d组测试集，准确率为%.3f" % (i, acccurcy))
        accuracy_list.append(acccurcy)
        testList.append(i)

plt.figure()
plt.xlabel("number of tests")
plt.ylabel("accuracy rate")
plt.ylim(0, 1)
plt.plot(testList, accuracy_list, "r-", label="Test")
plt.legend()
plt.show()