Pytorch入门

简单容易上手，感觉比keras好理解多了，和mxnet很像（似乎mxnet有点借鉴pytorch），记一记。

直接从例子开始学，基础知识咱已经看了很多论文了。。。

import torchimport torch.nn as nnimport torch.nn.functional as F# Linear 层 就是全连接层class Net(nn.Module): # 继承nn.Module，只用定义forward，反向传播会自动生成def __init__(self): # 初始化方法,这里的初始化是为了forward函数可以直接调过来super(Net,self).__init__() # 调用父类初始化方法# (input_channel,output_channel,kernel_size)self.conv1 = nn.Conv2d(1,6,5) # 第一层卷积self.conv2 = nn.Conv2d(6,16,5)# 第二层卷积self.fc1 = nn.Linear(16*5*5,120) # 这里16*5*5是前向算的self.fc2 = nn.Linear(120,84) # 第二层全连接self.fc3 = nn.Linear(84,10) # 第三层全连接->分类def forward(self,x):x = F.max_pool2d(F.relu(self.conv1(x)),(2,2)) # 卷积一次激活一次然后2*2池化一次x = F.max_pool2d(F.relu(self.conv2(x)),2) # (2,2)与直接写 2 等价x = x.view(-1,self.num_flatten_features(x)) # 将x展开成向量x = F.relu(self.fc1(x)) # 全连接 + 激活x = F.relu(self.fc2(x)) # 全连接+ 激活x = self.fc3(x) # 最后再全连接return xdef num_flatten_features(self,x):size = x.size()[1:] # 除了batch_size以外的维度,(batch_size,channel,h,w)num_features = 1for s in size:num_features*=sreturn num_features# ok,模型定义完毕。net = Net()print(net)'''Net((conv1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))(conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))(fc1): Linear(in_features=400, out_features=120, bias=True)(fc2): Linear(in_features=120, out_features=84, bias=True)(fc3): Linear(in_features=84, out_features=10, bias=True))'''params = list(net.parameters())print(len(params))print(params[0].size())'''10torch.Size([6, 1, 5, 5])'''inpt = torch.randn(1,1,32,32)out = net(inpt)print(out)'''tensor([[-0.0265, -0.1246, -0.0796, 0.1028, -0.0595, 0.0383, 0.0038, -0.0019,0.1181, 0.1373]], grad_fn=<AddmmBackward>)'''target = torch.randn(10)criterion = nn.MSELoss()loss = criterion(out,target)print(loss)'''tensor(0.5742, grad_fn=<MseLossBackward>)'''net.zero_grad()# 梯度归零print(net.conv1.bias.grad)loss.backward()print(net.conv1.bias.grad)'''Nonetensor([-0.0039, 0.0052, 0.0034, -0.0002, 0.0018, 0.0096])'''import torch.optim as optimoptimizer = optim.SGD(net.parameters(),lr = 0.01)optimizer.zero_grad()output = net(inpt)loss = criterion(output,target)loss.backward()optimizer.step()# 一个step完成，多个step就写在循环里

pytorch简直太好理解了。。继续蓄力！！

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