loss_fn = torch.nn.CrossEntropyLoss() dummy_outputs = torch.rand(4, 10) dummy_labels = torch.tensor([1, 5, 3, 7])

这段代码定义了一个交叉熵损失函数 `CrossEntropyLoss`，并且定义了一个大小为 $4 \times 10$ 的虚拟输出 `dummy_outputs` 和一个大小为 $4$ 的虚拟标签 `dummy_labels`。在实际应用中，你需要将 `dummy_outputs` 替换为你的模型的输出，将 `dummy_labels` 替换为你的样本的真实标签。然后将二者输入到 `CrossEntropyLoss` 中计算损失。交叉熵损失函数常用于多分类问题中计算损失。

相关问题

Write pytorch-based Python code to implement a neural network that solves solving binary classification problem with an output layer of a positively weighted sub-network plus a negatively weighted sub-network， where the negative weights are random and take the opposite of the absolute value of the standard normal distribution, the exact value of the two weights are obtained from the objective function by Back propagation.

Sure! Here's an example of Python code using PyTorch to implement a neural network for binary classification with a positively weighted sub-network and a negatively weighted sub-network:

import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable

# Define the neural network model
class BinaryClassifier(nn.Module):
    def __init__(self, input_size):
        super(BinaryClassifier, self).__init__()
        self.positive_network = nn.Linear(input_size, 1)
        self.negative_network = nn.Linear(input_size, 1)

    def forward(self, x):
        positive_output = self.positive_network(x)
        negative_output = self.negative_network(x)
        output = positive_output - negative_output
        return output

# Generate random negative weights from the standard normal distribution
def generate_negative_weights(model):
    for param in model.negative_network.parameters():
        param.data = torch.abs(torch.randn(param.size())) * -1

# Define the objective function
def objective_function(outputs, labels):
    criterion = nn.BCEWithLogitsLoss()
    loss = criterion(outputs, labels)
    return loss

# Generate some dummy data
input_size = 10
num_samples = 100
input_data = torch.randn(num_samples, input_size)
labels = torch.randint(0, 2, (num_samples,))

# Create the model and initialize random negative weights
model = BinaryClassifier(input_size)
generate_negative_weights(model)

# Training loop
num_epochs = 10
learning_rate = 0.001
optimizer = optim.SGD(model.parameters(), lr=learning_rate)

for epoch in range(num_epochs):
    model.train()
    optimizer.zero_grad()
    inputs = Variable(input_data)
    targets = Variable(labels.float())

    # Forward pass
    outputs = model(inputs)
    loss = objective_function(outputs, targets)

    # Backward pass and optimization
    loss.backward()
    optimizer.step()

    # Print loss for tracking progress
    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

In this code, we define a `BinaryClassifier` class that inherits from `nn.Module` and contains two linear layers: `positive_network` and `negative_network`. The `forward` method computes the output by subtracting the negative output from the positive output.

To generate random negative weights, we define the `generate_negative_weights` function, which iterates over the parameters of the negative network and assigns random negative values drawn from the standard normal distribution.

The objective function is defined using the `nn.BCEWithLogitsLoss` loss function, which combines a sigmoid activation function and a binary cross-entropy loss.

Finally, we define the training loop with a fixed number of epochs, a learning rate, and an optimizer (in this case, stochastic gradient descent). The loop performs a forward pass, computes the loss, performs a backward pass, and updates the model parameters.

Please note that this code is a basic implementation and may need modifications depending on your specific requirements.