import torch.optim as optim net = Net() criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(net.parameters(), lr=3e-4)

这段代码是用PyTorch库创建一个神经网络模型，并定义了交叉熵损失函数和Adam优化器。其中，net为一个自定义的神经网络模型，criterion为交叉熵损失函数，optimizer为Adam优化器，lr为学习率，设定为3e-4。Adam是一种常用的优化算法，可以用来优化神经网络的权重和偏置参数，从而提高模型的准确性。

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optimizer=torch.optim.SGD(net.parameters(), lr=0.5)

这行代码创建了一个名为 "optimizer" 的优化器对象，使用的是随机梯度下降（Stochastic Gradient Descent，SGD）算法，用于更新神经网络模型中的参数。

具体来说，这里使用了 torch.optim.SGD 类来创建优化器对象。它接受两个参数：net.parameters() 用于指定需要被优化的参数，lr=0.5 用于设置学习率（learning rate）为 0.5。

下面是一个示例代码，展示了如何使用 SGD 优化器来更新神经网络模型的参数：

import torch
import torch.optim as optim

# 创建神经网络模型
net = Net(dim_in=1, dim_out=10, class_number=1)

# 创建优化器对象
optimizer = optim.SGD(net.parameters(), lr=0.5)

# 在训练过程中使用优化器更新模型参数
for inputs, labels in dataloader:
    # 前向传播和计算损失
    outputs = net(inputs)
    loss = criterion(outputs, labels)
    
    # 梯度清零
    optimizer.zero_grad()
    
    # 反向传播和更新参数
    loss.backward()
    optimizer.step()

在训练过程中，首先进行前向传播计算输出和损失，然后使用 optimizer.zero_grad() 将梯度清零，再进行反向传播计算梯度，并使用 optimizer.step() 来更新参数。通过迭代训练数据，不断更新参数，最终实现模型的优化。

import torch.optim as optim

from typing import List,Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
def __init__(self, input_size:int, hidden_size:List[int], output_size:int, dropout:float):
super(Net, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.dropout = dropout

# Construct the hidden layers
self.hidden_layers = nn.ModuleList()
for i in range(len(hidden_size)):
if i == 0:
self.hidden_layers.append(nn.Linear(input_size, hidden_size[i]))
else:
self.hidden_layers.append(nn.Linear(hidden_size[i-1], hidden_size[i]))

# Construct the output layer
self.output_layer = nn.Linear(hidden_size[-1], output_size)

# Set up the dropout layer
self.dropout_layer = nn.Dropout(p=dropout)

def forward(self, x:torch.Tensor) -> torch.Tensor:
# Pass the input through the hidden layers
for layer in self.hidden_layers:
x = F.relu(layer(x))
x = self.dropout_layer(x)

# Pass the output from the last hidden layer through the output layer
x = self.output_layer(x)

return x

def train_model(model:Net, train_data:Tuple[torch.Tensor, torch.Tensor],
test_data:Tuple[torch.Tensor, torch.Tensor],
batch_size:int, num_epochs:int, learning_rate:float):

# Extract the inputs and labels from the training data
train_inputs, train_labels = train_data

# Define the loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# Loop over the training data for the specified number of epochs
for epoch in range(num_epochs):
# Shuffle the training data
perm = torch.randperm(train_inputs.size(0))
train_inputs = train_inputs[perm]
train_labels = train_labels[perm]

# Loop over the training data in batches
for i in range(0, train_inputs.size(0), batch_size):
# Extract the current batch of data
inputs = train_inputs[i:i+batch_size]
labels = train_labels[i:i+batch_size]

# Zero the gradients
optimizer.zero_grad()

# Forward pass
outputs = model(inputs)
loss = criterion(outputs, labels)

# Backward pass and update parameters
loss.backward()
optimizer.step()

# Evaluate the model on the test data
test_inputs, test_labels = test_data
test_outputs = model(test_inputs)
test_loss = criterion(test_outputs, test_labels)
test_accuracy = accuracy(test_outputs, test_labels)

# Print the epoch number, training loss, and test accuracy
print(f"Epoch {epoch+1}/{num_epochs}: Train loss={loss:.4f}, Test loss={test_loss:.4f}, Test accuracy={test_accuracy:.4f}")

def accuracy(outputs:torch.Tensor, labels:torch.Tensor) -> float:
predictions = torch.argmax(outputs, dim=1)
correct_predictions = torch.sum(predictions == labels)
accuracy = correct_predictions.float() / labels.size(0)
return accuracy.item()