标签:实战 loss 时装 nn train torch FashionMNIST model data
import os import numpy as np import pandas as pd import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import Dataset, DataLoader # 设置环境和超参数 ## 方案一:使用os.environ # os.environ['CUDA_VISIBLE_DEVICES']='0' ## 方案二:使用“device”,后续对要使用GPU的变量用.to(device)即可 device = torch.device('cuda:1' if torch.cuda is_available() else 'cpu') ## 配置其他超参数,如batch_size, num_workers, learning rate, 以及总的epochs batch_size = 256 num_workers = 4 # 对于Windows用户,这里应设置为0,否则会出现多线程错误 lr = 1e-4 epochs = 20 # 设置数据变换 from torchvision import transforms image_size = 28 data_transform = transform.Compose([ transform.ToPILImage(), # 这一步取决于后续的数据读取方式,如果使用内置数据集读取方式则不需要 transform.Resize(image_size), transform.ToTensor()]) ## 读取方式一:使用torchvision自带数据集,下载可能需要一段时间 from torchvision import datasets train_data = datasets.FashionMNIST(root='./', train=True, download=True, transform=data_transform) test_data = datasets.FashionMNIST(root='./', train=False, download=True, transform=data_transform) # 定义DataLoader类,加载数据 # drop_last对最后无法满足 batch_size大小的皮数据予以丢弃 train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=num_workers, drop_last=True) test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=False, num_workers=num_workers) # 据可视化操作,验证读入的数据是否正确 import matplotlib.pyplot as plt image, label = next(iter(train_loader)) print(image.shape, label.shape) plt.imshow(image[0][0], cmap="gray") # 模型设计 class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv = nn.Sequential( nn.Conv2d(1, 32, 5), nn.ReLU(), nn.MaxPool2d(2, stride = 2), nn.Dropout(0.3), nn.Conv2d(32, 64, 5), nn.ReLU(), nn.MaxPool2d(2, stride=2), nn.Dropout(0.3)) self.fc = nn.Sequential( nn.Linear(64*4*4, 512), nn.ReLU(), nn.Linear(512, 10)) def forward(self, x): x = self.conv(x) x = x.view(-1, 64*4*4) x = self.fc(x) return x model = Net() model = model.cuda() # model = nn.DataParallel(model).cuda() # 多卡训练时的写法 ## 设定损失函数 # 使用CrossEntropy损失会,自动把整数型的label转为one-hot型,用于计算CE loss criterion = nn.CrossEntropyLoss() ## 设置优化器 optimizer = optim.Adam(model.parameters(), lr=0.001) ## 训练和测试 def train(epoch): model.train() train_loss = 0 for data, label in train_loader: data, label = data.cuda(), label.cuda() optimizer = optimizer.zero_grad() output = model(data) loss = criterion(output, label) loss.backward() optimizer.step() train_loss += loss.item()*data.size(0) train_loss = train_loss/len(train_loader.dataset) print('Epoch: {} \tTraining Loss: {:.6f}'.format(epoch, train_loss)) def val(epoch): model.eval() val_loss = 0 gt_labels = [] pred_labels = [] with torch.no_grad(): for data, label in test_loader: data, label = data.cuda(), label.cuda() output = model(data) preds = torch.argmax(output, 1) gt_labels.append(label.cpu().data.numpy()) pred_labels.append(preds.cpu().data.numpy()) loss = criterion(output, label) val_loss += loss.item()*data.size(0) val_loss = val_loss/len(test_loader.dataset) gt_labels, pred_labels = np.concatenate(gt_labels), np.concatenate(pred_labels) acc = np.sum(gt_labels==pred_labels)/len(pred_labels) print('Epoch: {} \tValidation Loss: {:.6f}, Accuracy: {:6f}'.format(epoch, val_loss, acc)) ## 训练与测试 for epoch in range(1, epochs+1): train(epoch) val(epoch)
模型保存
save_path = './FahionModel.pkl' torch.save(model, save_path)
加载模型
model = torch.load('model.pkl')
注意:将模型保存成何种格式文件无所谓(比如pkl,pth等)。
保存与加载模型参数
torch.save(model.state_dict(), 'model_params.pth') model.load(torch.load( 'model_params.pth'))
标签:实战,loss,时装,nn,train,torch,FashionMNIST,model,data 来源: https://www.cnblogs.com/5466a/p/16611426.html
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