标签:kernel nn 05 torch stride PyTorch train VGGNet size
使用数据集猫狗大战
import time import torch import torchvision from torchvision import datasets, transforms import os import matplotlib.pyplot as plt from torch.autograd import Variable os.environ["CUDA_VISIBLE_DEVICES"] = "0" # 使用GPU 0 data_dir = "DogsVsCats" # 设置数据格式 data_transform = {x: transforms.Compose([transforms.Scale([64, 64]), # scale类将原始图缩放至64*64 transforms.ToTensor()]) for x in ["train", "valid"]} # 加载数据 image_datasets = {x: datasets.ImageFolder(root=os.path.join(data_dir, x), transform=data_transform[x]) for x in ["train", "valid"]} # 数据加载器,结合了数据集和取样器,并且可以提供多个线程处理数据集。 # 在训练模型时使用到此函数,用来把训练数据分成多个小组,此函数每次抛出一组数据。直至把所有的数据都抛出。就是做一个数据的初始化。 dataloader = {x: torch.utils.data.DataLoader(dataset=image_datasets[x], batch_size=16, shuffle=True) for x in ["train", "valid"]} # 获取一个批次的装载数据 x_example(16,3,64,64) y_example 进行了独热编码,里面全为0和1 x_example, y_example = next(iter(dataloader["train"])) # index_classes的 输出结果为{'cat':0,'dog',1} index_classes = image_datasets["train"].class_to_idx #将原始标签的结果存在example_clasees中 {'cat','dog'} example_clasees = image_datasets["train"].classes # 做成网格数据 img = torchvision.utils.make_grid(x_example) img = img.numpy().transpose([1, 2, 0]) # 转换维度 # print([example_clasees[i] for i in y_example]) # plt.imshow(img) # plt.show() # VGGNet模型 class Models(torch.nn.Module): def __init__(self): super(Models,self).__init__() self.Conv = torch.nn.Sequential( torch.nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1), torch.nn.ReLU(), torch.nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1), torch.nn.ReLU(), torch.nn.MaxPool2d(kernel_size=2, stride=2), torch.nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1), torch.nn.ReLU(), torch.nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1), torch.nn.ReLU(), torch.nn.MaxPool2d(kernel_size=2, stride=2), torch.nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1), torch.nn.ReLU(), torch.nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1), torch.nn.ReLU(), torch.nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1), torch.nn.ReLU(), torch.nn.MaxPool2d(kernel_size=2, stride=2), torch.nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1), torch.nn.ReLU(), torch.nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1), torch.nn.ReLU(), torch.nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1), torch.nn.ReLU(), torch.nn.MaxPool2d(kernel_size=2, stride=2), ) self.Classes = torch.nn.Sequential( torch.nn.Linear(4*4*512, 1024), torch.nn.ReLU(), torch.nn.Dropout(p=0.5), torch.nn.Linear(1024, 1024), torch.nn.Dropout(p=0.5), torch.nn.Linear(1024, 2) ) def forward(self, input): x = self.Conv(input) x = x.view(-1, 4*4*512) x = self.Classes(x) return x model = Models() # print(model) loss_f = torch.nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(),lr=0.00001) Use_gpu = torch.cuda.is_available() # 判断是否存在cuda if Use_gpu: model = model.cuda() # *********************************************************** epoch_n = 10 time_open = time.time() for epoch in range(epoch_n): print("Epoch{}/{}".format(epoch,epoch_n-1)) print("-"*10) for phase in ["train", "valid"]: if phase == "train": print("Training...") model.train(True) else: print("Validing...") model.train(False) running_loss = 0.0 running_corrects = 0 for batch, data in enumerate(dataloader[phase], 1): # enumerate() 函数用于将一个可遍历的数据对象(如列表、元组或字符串)组合为一个索引序列,同时列出数据和数据下标, X, y = data if Use_gpu: X, y =Variable(X.cuda()),Variable(y.cuda()) # ************************************** else: X, y = Variable(X), Variable(y) y_pred = model(X) # 得到预测值 _,pred =torch.max(y_pred,1) optimizer.zero_grad() # 清空梯度 loss = loss_f(y_pred, y) # 定义损失函数 if phase == "train": loss.backward() # 如果是训练,进行反向传播 optimizer.step() # 更新各节点的梯度 running_loss += loss.item() running_corrects += torch.sum(pred == y.data) if batch%500 == 0 and phase == "train": print("Batch{},TrainLoss:{:.4f},Train ACC:{:.4f}".format( batch,running_loss/batch, 100*running_corrects/(16*batch))) epocn_loss = running_loss*16/len(image_datasets[phase]) epoch_acc = 100*running_corrects/len(image_datasets[phase]) print("{} Loss:{:.4f} Acc:{:4f}%".format(phase, epocn_loss, epoch_acc)) time_end = time.time()-time_open print(time_end)
标签:kernel,nn,05,torch,stride,PyTorch,train,VGGNet,size 来源: https://www.cnblogs.com/zuhaoran/p/11502551.html
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