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.

Certainly! Here's an example of PyTorch-based Python code that implements a neural network for binary classification with an output layer of 3 weighted sub-networks using the tanh activation function:

import torch
import torch.nn as nn
import torch.optim as optim

class NeuralNetwork(nn.Module):
    def __init__(self, input_size):
        super(NeuralNetwork, self).__init__()
        self.fc1 = nn.Linear(input_size, 10)
        self.fc2 = nn.Linear(10, 10)
        self.fc3 = nn.Linear(10, 3)

    def forward(self, x):
        x = torch.tanh(self.fc1(x))
        x = torch.tanh(self.fc2(x))
        x = self.fc3(x)
        return x

# Generate random weights for the second sub-network
negative_weights = -torch.abs(torch.randn(10))

# Create an instance of the neural network
input_size = 10  # Replace with the actual input size
model = NeuralNetwork(input_size)

# Define the loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.1)

# Training loop
for epoch in range(100):
    # Forward pass
    outputs = model(inputs)  # Replace inputs with your actual input data
    loss = criterion(outputs, labels)  # Replace labels with your actual labels

    # Backward and optimize
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    # Update the second sub-network's weight using backpropagation
    model.fc2.weight.data = negative_weights

    # Print the loss during training
    print(f"Epoch {epoch+1}/{num_epochs}, Loss: {loss.item():.4f}")

In this code, we define a `NeuralNetwork` class that inherits from `nn.Module` and implements the forward pass. The network consists of three fully connected layers (`nn.Linear`) with tanh activation for the hidden layers. The second sub-network's weight is initialized with random negative values obtained from the standard normal distribution.

During the training loop, we compute the forward pass, calculate the loss with the `nn.CrossEntropyLoss` function, perform backpropagation, and update the weights using the optimizer (`optim.SGD`). The second sub-network's weight is updated after each backpropagation step.

Please replace `input_size`, `inputs`, and `labels` with your actual input size, input data, and labels, respectively. Adjust the hyperparameters and the number of training epochs according to your specific problem.

Note: This code is a basic example and may need modifications to fit your specific use case.