『PyTorch』第四弹_通过LeNet初识pytorch神经网络_上
# Author : Hellcat# Time ??: 2018/2/11import torch as timport torch.nn as nnimport torch.nn.functional as Fclass LeNet(nn.Module): ???def __init__(self): ???????super(LeNet,self).__init__() ???????self.conv1 = nn.Conv2d(3, 6, 5) ???????self.conv2 = nn.Conv2d(6,16,5) ???????self.fc1 = nn.Linear(16*5*5,120) ???????self.fc2 = nn.Linear(120,84) ???????self.fc3 = nn.Linear(84,10) ???def forward(self,x): ???????x = F.max_pool2d(F.relu(self.conv1(x)),(2,2)) ???????x = F.max_pool2d(F.relu(self.conv2(x)),2) ???????x = x.view(x.size()[0], -1) ???????x = F.relu(self.fc1(x)) ???????x = F.relu(self.fc2(x)) ???????x = self.fc3(x) ???????return xif __name__ == "__main__": ???net = LeNet() ???# #########训练网络######### ???from torch import optim ???# 初始化Loss函数 & 优化器 ???loss_fn = nn.CrossEntropyLoss() ???optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9) ???for epoch in range(2): ???????running_loss = 0.0 ???????for step, data in enumerate(trainloader, 0): ?# step为训练次数， trainloader包含batch的数据和标签 ???????????inputs, labels = data ???????????inputs, labels = t.autograd.Variable(inputs), t.autograd.Variable(labels) ???????????# 梯度清零 ???????????optimizer.zero_grad() ???????????# forward ???????????outputs = net(inputs) ???????????# backward ???????????loss = loss_fn(outputs, labels) ???????????loss.backward() ???????????# update ???????????optimizer.step() ???????????running_loss += loss.data[0] ???????????if step % 2000 == 1999: ???????????????print("[{0:d}, {1:5d}] loss: {2:3f}".format(epoch+1, step+1, running_loss/2000)) ???????????????running_loss = 0. ???print("Finished Training")这是使用LeNet分类cifar_10的例子，数据处理部分由于不是重点，没有列上来，主要是对使用torch分类有一个直观理解，
初始化网络
初始化Loss函数 & 优化器
进入step循环：
梯度清零
向前传播
计算本次Loss
向后传播
更新参数
由于pytorch的网络是class，所以在不考虑持久化的情况下，后续处理都不是太难，值得一提的是预测函数，我们直接net(Variable(test_data))即可，输出是概率分布的Variable，我们只要调用：
_, predict = t.max(test_out, 1)
即可，这是因为torch.max融合max和argmax的功能，
>> a = torch.randn(4, 4)
>> a
0.0692 0.3142 1.2513 -0.5428
0.9288 0.8552 -0.2073 0.6409
1.0695 -0.0101 -2.4507 -1.2230
0.7426 -0.7666 0.4862 -0.6628
torch.FloatTensor of size 4x4]
>>> torch.max(a, 1)
(
1.2513
0.9288
1.0695
0.7426
[torch.FloatTensor of size 4]
,
2
0
0
0
[torch.LongTensor of size 4]
)
其他torch的高级功能没有使用到，本篇的目的是对于torch神经网络基本的使用有个理解。
『PyTorch』第四弹_通过LeNet初识pytorch神经网络_下
原文地址：https://www.cnblogs.com/hellcat/p/8442816.html