import torch.nn as nn net = nn.Sequential(nn.Linear(2, 1))

`import torch.nn as nn` 是用于导入 PyTorch 库中的神经网络模块的命令。PyTorch 是一个开源的深度学习框架，用于构建和训练神经网络。

`nn.Sequential` 是 PyTorch 中的一种容器，用于将多个神经网络层按照顺序排列在一起。在这个例子中，`nn.Sequential(nn.Linear(2, 1))` 创建了一个包含一个线性层的序列。这意味着输入有 2 个特征，经过这个线性层后，输出为 1 个特征。

所以，这段代码的意思是创建了一个简单的神经网络模型，该模型有一个输入层（具有 2 个特征），经过一个线性层后，输出只有一个特征。

注意：在实际使用中，你可能需要添加更多的层（如激活函数、池化层等）来构建更复杂的网络结构。此外，你可能还需要定义损失函数和优化器来训练这个模型。

相关问题

在这代码里面加入获取准确率和损失值并且绘制函数的代码import os import sys import json import torch import torch.nn as nn from torchvision import transforms, datasets, utils import matplotlib.pyplot as plt import numpy as np import torch.optim as optim from tqdm import tqdm from m

import os
import sys
import json
import torch
import torch.nn as nn
from torchvision import transforms, datasets, utils
import matplotlib.pyplot as plt
import numpy as np
import torch.optim as optim
from tqdm import tqdm

def train(model, device, train_loader, optimizer, epoch):
model.train()
train_loss = 0
correct = 0
total = 0
progress_bar = tqdm(train_loader)

for batch_idx, (data, target) in enumerate(progress_bar):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = nn.CrossEntropyLoss()(output, target)
loss.backward()
optimizer.step()

train_loss += loss.item()
_, predicted = output.max(1)
total += target.size(0)
correct += predicted.eq(target).sum().item()

progress_bar.set_description(
'Train Epoch: {} [{}/{} ({:.0f}%)] Loss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader),
loss.item() / len(data)))

accuracy = 100. * correct / total
train_loss /= len(train_loader.dataset)

return accuracy, train_loss

def test(model, device, test_loader):
model.eval()
test_loss = 0
correct = 0
total = 0

with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += nn.CrossEntropyLoss()(output, target).item()
_, predicted = output.max(1)
total += target.size(0)
correct += predicted.eq(target).sum().item()

test_loss /= len(test_loader.dataset)
accuracy = 100. * correct / total

return accuracy, test_loss

def main():
# set up device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("Using device:", device)

# set up hyperparameters
batch_size = 64
epochs = 10
lr = 0.01

# set up data loaders
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])

train_dataset = datasets.MNIST(
'../data', train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=batch_size, shuffle=True)

test_dataset = datasets.MNIST(
'../data', train=False, download=True, transform=transform)
test_loader = torch.utils.data.DataLoader(
test_dataset, batch_size=batch_size, shuffle=True)

# set up model
model = nn.Sequential(
nn.Conv2d(1, 10, kernel_size=5),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2),
nn.Conv2d(10, 20, kernel_size=5),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2),
nn.Flatten(),
nn.Linear(320, 50),
nn.ReLU(),
nn.Linear(50, 10),
nn.LogSoftmax(dim=1)
).to(device)

# set up optimizer
optimizer = optim.SGD(model.parameters(), lr=lr)

# train and test the model
train_accuracies = []
train_losses = []
test_accuracies = []
test_losses = []

for epoch in range(1, epochs + 1):
train_accuracy, train_loss = train(
model, device, train_loader, optimizer, epoch)
test_accuracy, test_loss = test(model, device, test_loader)

train_accuracies.append(train_accuracy)
train_losses.append(train_loss)
test_accuracies.append(test_accuracy)
test_losses.append(test_loss)

print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))

# plot accuracy and loss curves
plt.figure(figsize=(10, 5))
plt.subplot(1, 2, 1)
plt.plot(range(1, epochs + 1), train_accuracies, label='Train')
plt.plot(range(1, epochs + 1), test_accuracies, label='Test')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()

plt.subplot(1, 2, 2)
plt.plot(range(1, epochs + 1), train_losses, label='Train')
plt.plot(range(1, epochs + 1), test_losses, label='Test')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()

plt.show()

if __name__ == '__main__':
main()

import torch.nn as nn

The torch.nn module in PyTorch provides various classes and functions for building neural networks.

Some of the commonly used classes in torch.nn are:

1. nn.Module: This is the base class for all neural network modules in PyTorch. It provides methods for defining and initializing the various layers of a neural network.

2. nn.Linear: This class implements a linear transformation of the input data. It is often used as the first layer of a neural network, and takes in the input size and output size as arguments.

3. nn.Conv2d: This class implements a 2D convolutional layer for processing image data. It takes in the input channels, output channels, kernel size, stride, and padding as arguments.

4. nn.RNN: This class implements a recurrent neural network (RNN) layer for processing sequential data. It takes in the input size, hidden size, and number of layers as arguments.

5. nn.Dropout: This class implements a regularization technique called dropout, which randomly drops out some units during training to prevent overfitting.

These are just a few examples of the classes available in torch.nn. The module also provides various activation functions, loss functions, and optimization algorithms for training neural networks.