pytorch学习笔记（10）--完整的模型训练（待完善）

一、神经网络训练

# file     : train.py
# time     : 2022/8/11 上午10:03
# function :
import torchvision.datasets
from model import *
from torch.utils.data import DataLoader

# DataSet
train_data = torchvision.datasets.CIFAR10("../dataset", train=True, transform=torchvision.transforms.ToTensor(), download=False)
test_data = torchvision.datasets.CIFAR10("../dataset", train=False, transform=torchvision.transforms.ToTensor(), download=False)

# len长度
train_data_size = len(train_data)
test_data_size = len(test_data)

print(format(train_data_size))
# ctrl+D
print(format(test_data_size))

# liyong DataLoader
train_dataloader = DataLoader(train_data, batch_size=64)
test_dataloader = DataLoader(test_data, batch_size=64)

# create
tudui = Tudui()

#loss
loss_fn = nn.CrossEntropyLoss()

#
# learning_rate = 0.01
# 1e-2 = 1 x (10)^(-2) = 1/100 = 0.01
learning_rate = 1e-2
optimizer = torch.optim.SGD(tudui.parameters(), lr=learning_rate)

# shezhi
total_train_step = 0
#
total_test_step = 0
#
epoch = 10

for i in range(epoch):
    print("------------第{} 轮训练开始----------- ".format(i+1))

    #
    for data in train_dataloader:
        imgs, targets = data
        output = tudui(imgs)
        loss = loss_fn(output, targets)
        #
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        total_train_step = total_train_step + 1
        print("训练次数：{}, loss:{}".format(total_train_step, loss.item))