DAY 39 图像数据与显存

1. 图像数据的格式：灰度和彩色数据
2. 模型的定义
3. 显存占用的4种地方
   1. 模型参数+梯度参数
   2. 优化器参数
   3. 数据批量所占显存
   4. 神经元输出中间状态
4. batchisize和训练的关系

5. import torch
   import torch.nn as nn
   import torch.optim as optim
   from torch.utils.data import DataLoader , Dataset
   from torchvision import datasets, transforms
   import matplotlib.pyplot as plttorch.manual_seed(42)
   transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.1307,), (0.3081,))
   ])
   train_dataset = datasets.MNIST(root='./data',train=True,download=True,transform=transform
   )test_dataset = datasets.MNIST(root='./data',train=False,transform=transform
   )
   sample_idx = torch.randint(0, len(train_dataset), size=(1,)).item() 
   image, label = train_dataset[sample_idx] 
   def imshow(img):img = img* 0.3081 + 0.1307npimg= img.numpy()plt.imshow(npimg[0], cmap='gray')plt.show()
   print(f'Label:{label}')
   imshow(image)
   import torch
   import torchvision
   import torchvision.transforms as transforms
   import matplotlib.pyplot as plt
   import numpy as nptorch.manual_seed(42)
   transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))  
   ])trainset = torchvision.datasets.CIFAR10(root='./data',train=True,download=True,transform=transform
   )trainloader = torch.utils.data.DataLoader(trainset,batch_size=4,shuffle=True
   )classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')sample_idx = torch.randint(0, len(trainset), size=(1,)).item()
   image, label = trainset[sample_idx]print(f"图像形状: {image.shape}") 
   print(f"图像类别: {classes[label]}")def imshow(img):img = img / 2 + 0.5 npimg = img.numpy()plt.imshow(np.transpose(npimg, (1, 2, 0)))  plt.axis('off') plt.show()imshow(image)
   transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) 
   ])
   import matplotlib.pyplot as plttrain_dataset = datasets.MNIST(root='./data',train=True,download=True,transform=transform
   )test_dataset = datasets.MNIST(root='./data',train=False,transform=transform
   )
   class MLP(nn.Module):def __init__(self):super(MLP, self).__init__()self.flatten = nn.Flatten()  self.layer1 = nn.Linear(784, 128) self.relu = nn.ReLU()  self.layer2 = nn.Linear(128, 10)  def forward(self, x):x = self.flatten(x)  x = self.layer1(x)   x = self.relu(x)    x = self.layer2(x) return xmodel = MLP()device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
   model = model.to(device)  from torchsummary import summary  
   print("\n模型结构信息：")
   summary(model, input_size=(1, 28, 28)) 
   class MLP(nn.Module):def __init__(self, input_size=3072, hidden_size=128, num_classes=10):super(MLP, self).__init__()self.flatten = nn.Flatten()self.fc1 = nn.Linear(input_size, hidden_size) self.relu = nn.ReLU()self.fc2 = nn.Linear(hidden_size, num_classes) def forward(self, x):x = self.flatten(x)  x = self.fc1(x)    x = self.relu(x)     x = self.fc2(x)     return xmodel = MLP()device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
   model = model.to(device) 
   from torchsummary import summary 
   print("\n模型结构信息：")
   summary(model, input_size=(3, 32, 32))  class MLP(nn.Module):def __init__(self):super().__init__()self.flatten = nn.Flatten() self.layer1 = nn.Linear(784, 128)self.relu = nn.ReLU()self.layer2 = nn.Linear(128, 10)def forward(self, x):x = self.flatten(x) x = self.layer1(x) x = self.relu(x)x = self.layer2(x)   return x
   from torch.utils.data import DataLoadertrain_loader = DataLoader(dataset=train_dataset,  batch_size=64,          shuffle=True           
   )test_loader = DataLoader(dataset=test_dataset,batch_size=1000,shuffle=False
   )