import torch
import torchvision
from torch.autograd import Variable
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import cv2
from torch import nn
import numpy as np
import torch.nn.functional as F
import advertorch.defenses as defenses
from numpy import *
seed = 2014
torch.manual_seed(seed)
np.random.seed(seed) # Numpy module.
random.seed(seed) # Python random module.
torch.manual_seed(seed)
train_dataset = datasets.FashionMNIST('./fashionmnist_data/', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
]))
train_loader = DataLoader(dataset = train_dataset, batch_size = 500, shuffle = True)
test_loader = torch.utils.data.DataLoader(
datasets.FashionMNIST('./fashionmnist_data/', train=False, transform=transforms.Compose([
transforms.ToTensor(),
])),batch_size=1, shuffle=True)
epoch = 12
class Linear_cliassifer(torch.nn.Module):
def __init__(self) :
super(Linear_cliassifer, self).__init__()
self.Gs = defenses.GaussianSmoothing2D(3, 1, 3)
self.Line1 = torch.nn.Linear(28 * 28, 10)
def forward(self, x):
x = self.Gs(x)
x = self.Line1(x.view(-1, 28 * 28))
return x
net = Linear_cliassifer()
cost = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=0.001)
for k in range(epoch):
sum_loss = 0.0
train_correct = 0
for i, data in enumerate(train_loader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = cost(outputs, labels)
loss.backward()
optimizer.step()
print(loss)
_, id = torch.max(outputs.data, 1)
sum_loss += loss.data
train_correct += torch.sum(id == labels.data)
#print('[%d,%d] loss:%.03f' % (k + 1, k, sum_loss / len(train_loader)))
print(' correct:%.03f%%' % (100 * train_correct / len(train_dataset)))
torch.save(net.state_dict(), 'model/fasion_BL.pt')
本文地址：https://blog.csdn.net/qq_23144435/article/details/107430946
