[Pytorch]|[Pytorch] dropout and eval() [Pytorch]dropoutandeval()

import torch import torch.nn as nn import torch.nn.functional as F import numpy as np import torchvision import torchvision.datasets as dsets from torchvision.transforms import transforms import cv2class Net(nn.Module):def __init__(self):super(Net, self).__init__() self.fc = nn.Linear(5, 5) self.dp = nn.Dropout(0.5)def forward(self, x):x = self.fc(x) x = self.dp(x)return xif __name__ == '__main__':x = torch.FloatTensor([1]*5) z = torch.FloatTensor([1]*5) print(x) net = Net() criterion = nn.MSELoss() optim = torch.optim.SGD(net.parameters(), 0.1)optim.zero_grad() net.train() y = net(x) loss = criterion(y, z) loss.backward() optim.step() print(y) print(net(x)) # net(x) is not the same as y -> dropout() changes result every time net.eval() optim.zero_grad() y = net(x) loss = criterion(y, z) loss.backward() optim.step() print(y) print(net(x)) # eval() or mode(train=False) only changes the state of some modules, e.g., dropout, but do not disable loss back-propogation. # By setting train=False, dropout() does not work and is temporarily removed from the chain of update.with torch.no_grad(): optim.zero_grad() y = net(x) loss = criterion(y, z) # loss.backward() -> torch.no_grad sets torch.parameters() to be an empty set, and conducting loss.backward() will raise error. optim.step() print(y) print(net(x)) # net(x) == y -> with no loss.backward(), params of network are fixed.

【[Pytorch]|[Pytorch] dropout and eval()】Output:

tensor([ 1.,1.,1.,1.,1.]) tensor([ 0.0000, -0.0000,1.6945,0.7744,0.0000]) tensor([ 0.0000, -0.0000,0.0000,0.9910,0.0000]) tensor([ 0.3758, -1.1484,0.5139,0.4955,0.7577]) tensor([ 0.5256, -0.6328,0.6306,0.6166,0.8158]) tensor([ 0.5256, -0.6328,0.6306,0.6166,0.8158]) tensor([ 0.5256, -0.6328,0.6306,0.6166,0.8158])