原标题:简单了解pytorch的forward
原文来自:博客园 原文链接:https://www.cnblogs.com/ziytong/p/10677771.html
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)
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