本篇不涉及模型原理，只是分享下代码。想要了解模型原理的可以去看网上很多大牛的博客。
目前代码实现了CNN和LSTM两个网络，整个代码分为四部分：
Config：项目中涉及的参数；CNN：卷积神经网络结构；LSTM：长短期记忆网络结构；-
TrainProcess：模型训练及评估，参数
model控制训练何种模型（CNN or LSTM）。
完整代码
—Talk is cheap, show me the code.
# -*- coding: utf-8 -*-
# @author: Awesome_Tang
# @date: 2019-04-05
# @version: python3.7
import torch
from torchvision import datasets, transforms
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from datetime import datetime
class Config:
batch_size = 64
epoch = 10
alpha = 1e-3
print_per_step = 100 # 控制输出
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
"""
Conv2d参数：
第一位：input channels 输入通道数
第二位：output channels 输出通道数
第三位：kernel size 卷积核尺寸
第四位：stride 步长，默认为1
第五位：padding size 默认为0，不补
"""
self.conv1 = nn.Sequential(
nn.Conv2d(1, 32, 3, 1, 2),
nn.ReLU(),
nn.MaxPool2d(2, 2)
)
self.conv2 = nn.Sequential(
nn.Conv2d(32, 64, 5),
nn.ReLU(),
nn.MaxPool2d(2, 2)
)
self.fc1 = nn.Sequential(
nn.Linear(64 * 5 * 5, 128),
nn.BatchNorm1d(128),
nn.ReLU()
)
self.fc2 = nn.Sequential(
nn.Linear(128, 64),
nn.BatchNorm1d(64), # 加快收敛速度的方法（注：批标准化一般放在全连接层后面，激活函数层的前面）
nn.ReLU()
)
self.fc3 = nn.Linear(64, 10)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = x.view(x.size()[0], -1)
x = self.fc1(x)
x = self.fc2(x)
x = self.fc3(x)
return x
class LSTM(nn.Module):
def __init__(self):
super(LSTM, self).__init__()
self.lstm = nn.LSTM(
input_size=28,
hidden_size=64,
num_layers=1,
batch_first=True,
)
self.output = nn.Linear(64, 10)
def forward(self, x):
r_out, (_, _) = self.lstm(x, None)
out = self.output(r_out[:, -1, :])
return out
class TrainProcess:
def __init__(self, model="CNN"):
self.train, self.test = self.load_data()
self.model = model
if self.model == "CNN":
self.net = CNN()
elif self.model == "LSTM":
self.net = LSTM()
else:
raise ValueError('"CNN" or "LSTM" is expected, but received "%s".' % model)
self.criterion = nn.CrossEntropyLoss() # 定义损失函数
self.optimizer = optim.Adam(self.net.parameters(), lr=Config.alpha)
@staticmethod
def load_data():
print("Loading Data......")
"""加载MNIST数据集，本地数据不存在会自动下载"""
train_data = datasets.MNIST(root='./data/',
train=True,
transform=transforms.ToTensor(),
download=True)
test_data = datasets.MNIST(root='./data/',
train=False,
transform=transforms.ToTensor())
# 返回一个数据迭代器
# shuffle：是否打乱顺序
train_loader = torch.utils.data.DataLoader(dataset=train_data,
batch_size=Config.batch_size,
shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_data,
batch_size=Config.batch_size,
shuffle=False)
return train_loader, test_loader
def train_step(self):
steps = 0
start_time = datetime.now()
print("Training & Evaluating based on '%s'......" % self.model)
for epoch in range(Config.epoch):
print("Epoch {:3}.".format(epoch + 1))
for data, label in self.train:
data, label = Variable(data.cpu()), Variable(label.cpu())
# LSTM输入为3维，CNN输入为4维
if self.model == "LSTM":
data = data.view(-1, 28, 28)
self.optimizer.zero_grad() # 将梯度归零
outputs = self.net(data) # 将数据传入网络进行前向运算
loss = self.criterion(outputs, label) # 得到损失函数
loss.backward() # 反向传播
self.optimizer.step() # 通过梯度做一步参数更新
# 每100次打印一次结果
if steps % Config.print_per_step == 0:
_, predicted = torch.max(outputs, 1)
correct = int(sum(predicted == label)) # 计算预测正确个数
accuracy = correct / Config.batch_size # 计算准确率
end_time = datetime.now()
time_diff = (end_time - start_time).seconds
time_usage = '{:3}m{:3}s'.format(int(time_diff / 60), time_diff % 60)
msg = "Step {:5}, Loss:{:6.2f}, Accuracy:{:8.2%}, Time usage:{:9}."
print(msg.format(steps, loss, accuracy, time_usage))
steps += 1
test_loss = 0.
test_correct = 0
for data, label in self.test:
data, label = Variable(data.cpu()), Variable(label.cpu())
if self.model == "LSTM":
data = data.view(-1, 28, 28)
outputs = self.net(data)
loss = self.criterion(outputs, label)
test_loss += loss * Config.batch_size
_, predicted = torch.max(outputs, 1)
correct = int(sum(predicted == label))
test_correct += correct
accuracy = test_correct / len(self.test.dataset)
loss = test_loss / len(self.test.dataset)
print("Test Loss: {:5.2f}, Accuracy: {:6.2%}".format(loss, accuracy))
end_time = datetime.now()
time_diff = (end_time - start_time).seconds
print("Time Usage: {:5.2f} mins.".format(time_diff / 60.))
if __name__ == "__main__":
p = TrainProcess(model='CNN')
p.train_step()
Peace~~