Now train a OneFCNet using your Gradient Descent optimizer, the data loader which iterates over all samples in one batch with a Cross Entropy loss (hint: there is an implementation of PyTorch for this loss). For testing the optimizers we are not yet interested in the validation loss. So no need to provide a validation loader. 帮我按照上面的要求补充下面的代码 net = ... epochs = 10 optimizer = GD(net.parameters(), 0.002) loss_fn = ... losses, states = ... fig = plot_losses(losses) iplot(fig)
时间: 2024-03-25 16:40:46 浏览: 68
Gradient-Descent-The-Ultimate-Optimizer:梯度下降的纸张和代码
Sure, here's the code snippet that satisfies your requirements:
```
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from tqdm import tqdm
import matplotlib.pyplot as plt
from IPython.display import display, clear_output
# Define your OneFCNet model
class OneFCNet(nn.Module):
def __init__(self):
super().__init__()
self.fc = nn.Linear(100, 10)
def forward(self, x):
x = self.fc(x)
return x
# Define your Gradient Descent optimizer
class GD():
def __init__(self, params, lr):
self.params = list(params)
self.lr = lr
def step(self):
with torch.no_grad():
for p in self.params:
p -= self.lr * p.grad
p.grad.zero_()
# Define your training function
def train(net, loader, optimizer, loss_fn):
net.train()
losses = []
states = []
for epoch in range(epochs):
running_loss = 0.0
for i, data in tqdm(enumerate(loader)):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = loss_fn(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
epoch_loss = running_loss / len(loader)
losses.append(epoch_loss)
states.append([p.detach().clone() for p in net.parameters()])
clear_output(wait=True)
fig = plot_losses(losses)
display(fig)
return losses, states
# Define your data loader
class MyDataset(torch.utils.data.Dataset):
def __init__(self, data, labels):
self.data = data
self.labels = labels
def __getitem__(self, index):
x = self.data[index]
y = self.labels[index]
return x, y
def __len__(self):
return len(self.data)
# Create your OneFCNet object
net = OneFCNet()
# Define number of epochs, learning rate, Gradient Descent optimizer, and Cross Entropy loss
epochs = 10
lr = 0.002
optimizer = GD(net.parameters(), lr)
loss_fn = nn.CrossEntropyLoss()
# Create your data and labels tensors
data = torch.randn(1000, 100)
labels = torch.randint(0, 10, (1000,))
# Create your data loader
dataset = MyDataset(data, labels)
loader = DataLoader(dataset, batch_size=32, shuffle=True)
# Call your train function to train the model
losses, states = train(net, loader, optimizer, loss_fn)
```
Note that I assumed that you already have a `plot_losses` function that plots the training loss over time. If you don't have one already, you can define it as follows:
```
def plot_losses(losses):
fig, ax = plt.subplots(figsize=(8, 6))
ax.plot(losses)
ax.set_xlabel('Epochs')
ax.set_ylabel('Loss')
ax.set_title('Training Loss')
plt.show()
return fig
```
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