将model.fit(X_train, Y_train, epochs, batch_size, validation_split=RATIO)改写为pytorch
时间: 2024-05-06 09:21:37 浏览: 9
PyTorch中使用nn.Module来定义模型,同时使用torch.optim来定义优化器。基于此,将上述代码改写为PyTorch如下:
```python
import torch
import torch.nn as nn
import torch.optim as optim
class Model(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(100, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
x = nn.functional.relu(self.fc1(x))
x = self.fc2(x)
return x
model = Model()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)
trainloader = torch.utils.data.DataLoader(dataset,batch_size=batch_size,shuffle=True)
for epoch in range(epochs):
running_loss = 0.0
for i, (inputs, labels) in enumerate(trainloader, 0):
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
print('[%d] loss: %.3f' %
(epoch + 1, running_loss / len(trainloader)))
```
其中,Model为定义的模型,criterion为损失函数,optimizer为优化器。在每个epoch中,通过trainloader迭代数据,并进行正向传播、计算损失及反向传播更新模型参数。最后输出每个epoch的损失loss。