简单了解pytorch的forward

import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
import torch

class Net(nn.Module):  # 需要继承这个类
    def __init__(self):
        super(Net, self).__init__()
        # 建立了两个卷积层，self.conv1, self.conv2，注意，这些层都是不包含激活函数的
        self.conv1 = nn.Conv2d(1, 6, 5)  # 1 input image channel, 6 output channels, 5x5 square convolution kernel
        self.conv2 = nn.Conv2d(6, 16, 5)
        # 三个全连接层
        self.fc1 = nn.Linear(16 * 5 * 5, 120)  # an affine operation: y = Wx + b
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):  # 注意，2D卷积层的输入data维数是 batchsize*channel*height*width
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))  # Max pooling over a (2, 2) window
        x = F.max_pool2d(F.relu(self.conv2(x)), 2)  # If the size is a square you can only specify a single number
        x = x.view(-1, self.num_flat_features(x))
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)

        print(x)
        print('y=--------')
        return x

    def num_flat_features(self, x):
        size = x.size()[1:]  # all dimensions except the batch dimension
        num_features = 1
        for s in size:
            num_features *= s
        return num_features


net = Net()
# create your optimizer
optimizer = optim.SGD(net.parameters(), lr = 0.01)
num_iteations = 20
input = Variable(torch.randn(2, 1, 32, 32))
print('input=',input)
#target = Variable(torch.Tensor([5],dtype=torch.long))
target = Variable(torch.LongTensor([5,7]))
# in your training loop:
for i in range(num_iteations):
    optimizer.zero_grad() # zero the gradient buffers，如果不归0的话，gradients会累加

    output = net(input) # 这里就体现出来动态建图了，你还可以传入其他的参数来改变网络的结构
    criterion = nn.CrossEntropyLoss()
    loss = criterion(output, target)
    loss.backward() # 得到grad，i.e.给Variable.grad赋值
    optimizer.step() # Does the update，i.e. Variable.data -= learning_rate*Variable.grad

这里是给出的一个代码。

init只是规定了conv的输入通道数量、输出通道数量和卷积核尺寸。

然后在神经网络中，充当卷积层的是forward部分。

input = Variable(torch.randn(2, 1, 32, 32))    #batchsize，channel，height，width

target = Variable(torch.LongTensor([5,7]))     #我希望两个神经网络，第一个等于5，第二个等于7.当然随便两个数。（不代表5*7维矩阵呀）