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| # -*- coding: utf-8 -*-
""" @author: zj @file: tensorboard-fashion-mnist.py @time: 2019-12-11 """
import matplotlib.pyplot as plt import numpy as np
import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim
import torchvision import torchvision.transforms as transforms import torchvision.utils
learning_rate = 1e-3 moment = 0.9 epoches = 50 bsize = 256
# constant for classes classes = ('T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle Boot')
def load_data(bsize): # transforms transform = transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])
# datasets trainset = torchvision.datasets.FashionMNIST('./data', download=True, train=True, transform=transform) testset = torchvision.datasets.FashionMNIST('./data', download=True, train=False, transform=transform)
# dataloaders trainloader = torch.utils.data.DataLoader(trainset, batch_size=bsize, shuffle=True, num_workers=2)
testloader = torch.utils.data.DataLoader(testset, batch_size=bsize, shuffle=False, num_workers=2) return trainloader, testloader
class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 6, 5) self.pool = nn.MaxPool2d(2, 2) self.conv2 = nn.Conv2d(6, 16, 5) self.fc1 = nn.Linear(16 * 4 * 4, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 10)
def forward(self, x): x = self.pool(F.relu(self.conv1(x))) x = self.pool(F.relu(self.conv2(x))) x = x.view(-1, 16 * 4 * 4) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x
def compute_accuracy(loader, net, device): total_accu = 0.0 num = 0
for i, data in enumerate(loader, 0): inputs, labels = data[0].to(device), data[1].to(device)
outputs = net.forward(inputs) predicted = torch.argmax(outputs, dim=1) total_accu += torch.mean((predicted == labels).float()).item() num += 1 return total_accu / num
def draw(values, xlabel, ylabel, title, label): fig = plt.figure() plt.plot(list(range(len(values))), values, label=label)
plt.xlabel(xlabel) plt.ylabel(ylabel) plt.title(title)
plt.legend() plt.show()
def train(trainloader, testloader, net, criterion, optimizer, device): train_accu_list = list() test_accu_list = list() loss_list = list()
for epoch in range(epoches): # loop over the dataset multiple times num = 0 running_loss = 0.0 for i, data in enumerate(trainloader, 0): # get the inputs; data is a list of [inputs, labels] inputs, labels = data[0].to(device), data[1].to(device)
# zero the parameter gradients optimizer.zero_grad()
# forward + backward + optimize outputs = net(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step()
running_loss += loss.item() num += 1 # 每轮迭代完成后,记录损失值,计算训练集和测试集的检测精度 avg_loss = running_loss / num print('[%d] loss: %.4f' % (epoch + 1, avg_loss)) loss_list.append(avg_loss)
train_accu = compute_accuracy(trainloader, net, device) test_accu = compute_accuracy(testloader, net, device) print('train: %.4f, test: %.4f' % (train_accu, test_accu)) train_accu_list.append(train_accu) test_accu_list.append(test_accu)
print('Finished Training') return train_accu_list, test_accu_list, loss_list
if __name__ == '__main__': device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") print(device)
net = Net().to(device) criterion = nn.CrossEntropyLoss().to(device) optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=moment)
trainloader, testloader = load_data(bsize)
train_accu_list, test_accu_list, loss_list = train(trainloader, testloader, net, criterion, optimizer, device)
draw(train_accu_list, 'epoch', 'accuracy', 'train accuracy', 'fashion-mnist') draw(test_accu_list, 'epoch', 'accuracy', 'test accuracy', 'fashion-mnist') draw(loss_list, 'epoch', 'loss_value', 'loss', 'fashion-mnist')
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