Write pytorch-based Python code to implement a neural network that solves binary classification problem with an output layer of a positively weighted sub-network plus a negatively weighted sub-network，where the negative weight is 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. The activation function is set to tanh.

Sure! Here's a PyTorch-based Python code that implements a neural network for binary classification with the specified architecture and requirements:

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

# Define the neural network architecture
class BinaryClassifier(nn.Module):
    def __init__(self, input_size):
        super(BinaryClassifier, self).__init__()
        self.hidden_size = 16
        self.output_size = 1

        # Positive sub-network
        self.positive_weights = nn.Linear(input_size, self.hidden_size)
        self.positive_output = nn.Linear(self.hidden_size, self.output_size)

        # Negative sub-network
        self.negative_weights = nn.Linear(input_size, self.hidden_size)
        self.negative_output = nn.Linear(self.hidden_size, self.output_size)

        # Activation function
        self.activation = nn.Tanh()

    def forward(self, inputs):
        positive_hidden = self.activation(self.positive_weights(inputs))
        positive_output = self.positive_output(positive_hidden)

        negative_hidden = self.activation(self.negative_weights(inputs))
        negative_output = self.negative_output(negative_hidden)

        output = positive_output - negative_output
        return output

# Create the binary classifier model
input_size = 10  # Change this according to your input size
model = BinaryClassifier(input_size)

# Define the objective function and optimizer
criterion = nn.BCEWithLogitsLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# Generate random weights for the negative sub-network
with torch.no_grad():
    model.negative_weights.weight.data.normal_(0, 1)
    model.negative_weights.weight.data = -torch.abs(model.negative_weights.weight.data)

# Training loop (assuming you have train_loader and test_loader)
epochs = 10  # Change this according to the desired number of epochs
for epoch in range(epochs):
    for inputs, labels in train_loader:
        # Zero the gradients
        optimizer.zero_grad()

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

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

    # Print the loss for every epoch
    print(f"Epoch {epoch+1}/{epochs}, Loss: {loss.item()}")

# Evaluate the model on the test set
with torch.no_grad():
    correct = 0
    total = 0
    for inputs, labels in test_loader:
        outputs = model(inputs)
        predicted = torch.round(torch.sigmoid(outputs))
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    accuracy = correct / total
    print(f"Test Accuracy: {accuracy}")

Make sure to replace `input_size` with the appropriate value for your input size. Also, modify the training and test data loaders according to your dataset.

Note: This code assumes a binary classification problem with a sigmoid activation function at the output layer. The loss function used is `BCEWithLogitsLoss`, which combines a sigmoid activation and binary cross-entropy loss.

I hope this helps! Let me know if you have any further questions.