怎么拿这个数据放到模型里进行预测’
时间: 2024-02-11 12:09:43 浏览: 19
在PyTorch中,你需要使用`DataLoader`将数据集转换为可迭代的小批量数据集,然后将小批量数据集传入模型进行预测。以下是一个简单的示例代码:
```python
import torch
import torchvision
import torch.nn as nn
from torchvision import transforms
# 定义数据预处理
transform = transforms.Compose([
transforms.ToTensor(), # 将图片转换为PyTorch张量
transforms.Normalize((0.1307,), (0.3081,)) # 归一化像素值到-1到1之间
])
# 加载MNIST数据集
trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)
# 创建数据加载器
batch_size = 32
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=2)
# 定义模型
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(torch.relu(self.conv1(x)))
x = self.pool(torch.relu(self.conv2(x)))
x = x.view(-1, 16 * 4 * 4)
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = self.fc3(x)
return x
# 创建模型实例
net = Net()
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
# 训练模型
for epoch in range(2): # 迭代2次
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 100 == 99: # 每100个小批量打印一次损失值
print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
running_loss = 0.0
print('Finished Training')
# 获取前5张训练图片
few_train_images = [trainset[i][0] for i in range(5)]
# 对前5张训练图片进行预测
for i, image in enumerate(few_train_images):
output = net(image.unsqueeze(0)) # 将图片张量转换为一批大小为1的小批量
_, predicted = torch.max(output, 1)
print('Predicted for image %d: %d' % (i + 1, predicted.item()))
```
在上面的代码中,我们首先定义了数据预处理和模型。然后我们使用`DataLoader`将数据集转换为可迭代的小批量数据集。接着我们定义损失函数和优化器,并使用训练集进行模型训练。最后,我们获取前5张训练图片进行预测,将每张图片转换为一批大小为1的小批量,然后将小批量输入模型进行预测,输出预测值。