Write pytorch-based Python code to implement a neural network that solves binary classification problem with an output layer of 3 weighted sub-networks. The first weight is Positive, the second is negative, which is random and take the opposite of the absolute value of the standard normal distribution, the third random weight obeys a standard normal distribution. The exact value of the 3 weights are obtained from the objective function by Back propagation. The activation function is set to tanh.
时间: 2024-04-08 13:30:17 浏览: 12
Sure! Here's the PyTorch-based Python code to implement a neural network that solves a binary classification problem with an output layer consisting of three weighted sub-networks:
```python
import torch
import torch.nn as nn
class BinaryClassifier(nn.Module):
def __init__(self, input_size, hidden_size):
super(BinaryClassifier, self).__init__()
self.hidden_layer = nn.Linear(input_size, hidden_size)
self.output_layer = nn.Linear(hidden_size, 3)
# Initialize the weights
self.output_layer.weight.data[0] = torch.randn(3)
self.output_layer.weight.data[1] = -torch.abs(torch.randn(3))
self.output_layer.weight.data[2] = torch.randn(3)
def forward(self, x):
x = torch.tanh(self.hidden_layer(x))
x = self.output_layer(x)
return x
# Example usage
input_size = 10
hidden_size = 20
model = BinaryClassifier(input_size, hidden_size)
# Generate a random input tensor
input_tensor = torch.randn(1, input_size)
# Forward pass
output = model(input_tensor)
print(output)
```
In this code, we define a `BinaryClassifier` class that inherits from `nn.Module` in PyTorch. The class has two layers: a hidden layer and an output layer. The hidden layer is a linear transformation with the input size and hidden size specified. The output layer is also a linear transformation that maps the hidden layer's output to three classes.
We initialize the weights of the output layer as described in the question - the first weight is sampled from a standard normal distribution, the second weight is the negative absolute value of a standard normal distribution, and the third weight is also sampled from a standard normal distribution.
The forward method performs the forward pass of the network. The input tensor is passed through the hidden layer, followed by a tanh activation function. The result is then passed through the output layer, resulting in the final output.
Finally, we create an instance of the `BinaryClassifier` class with the desired input and hidden sizes. We generate a random input tensor and pass it through the model to obtain the output.