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| # -*- coding: utf-8 -*-
"""
@date: 2020/3/1 下午2:38
@file: svm.py
@author: zj
@description:
"""
import time
import copy
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
import torchvision.transforms as transforms
from torchvision.datasets import MNIST
def load_data():
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
data_loaders = {}
data_sizes = {}
for name in ['train', 'val']:
data_set = MNIST('./data', download=True, transform=transform)
# 测试
# img, target = data_set.__getitem__(0)
# print(img.shape)
# print(target)
# exit(0)
data_loader = DataLoader(data_set, shuffle=True, batch_size=128, num_workers=8)
data_loaders[name] = data_loader
data_sizes[name] = len(data_set)
return data_loaders, data_sizes
def hinge_loss(outputs, labels):
"""
折页损失计算
:param outputs: 大小为(N, num_classes)
:param labels: 大小为(N)
:return: 损失值
"""
num_labels = len(labels)
corrects = outputs[range(num_labels), labels].unsqueeze(0).T
# 最大间隔
margin = 1.0
margins = outputs - corrects + margin
loss = torch.sum(torch.max(margins, 1)[0]) / len(labels)
# # 正则化强度
# reg = 1e-3
# loss += reg * torch.sum(weight ** 2)
return loss
def train_model(data_loaders, model, criterion, optimizer, lr_scheduler, num_epochs=25, device=None):
since = time.time()
best_model_weights = copy.deepcopy(model.state_dict())
best_acc = 0.0
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
# Each epoch has a training and validation phase
for phase in ['train', 'val']:
if phase == 'train':
model.train() # Set model to training mode
else:
model.eval() # Set model to evaluate mode
running_loss = 0.0
running_corrects = 0
# Iterate over data.
for inputs, labels in data_loaders[phase]:
# print(inputs.shape)
# print(labels.shape)
inputs = inputs.reshape(-1, 28 * 28)
inputs = inputs.to(device)
labels = labels.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward
# track history if only in train
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs)
# print(outputs.shape)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
# backward + optimize only if in training phase
if phase == 'train':
loss.backward()
optimizer.step()
# statistics
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
if phase == 'train':
lr_scheduler.step()
epoch_loss = running_loss / data_sizes[phase]
epoch_acc = running_corrects.double() / data_sizes[phase]
print('{} Loss: {:.4f} Acc: {:.4f}'.format(
phase, epoch_loss, epoch_acc))
# deep copy the model
if phase == 'val' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model_weights = copy.deepcopy(model.state_dict())
print()
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))
# load best model weights
model.load_state_dict(best_model_weights)
return model
if __name__ == '__main__':
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
data_loaders, data_sizes = load_data()
# print(data_loaders)
# print(data_sizes)
model = nn.Linear(28 * 28, 10).to(device)
# criterion = nn.CrossEntropyLoss()
criterion = hinge_loss
optimizer = optim.SGD(model.parameters(), lr=1e-3, momentum=0.9)
lr_schduler = optim.lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)
train_model(data_loaders, model, criterion, optimizer, lr_schduler, num_epochs=25, device=device)
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