mlp_bp.py

# Implement this algorithm using pytorch

import torch

# Assuming we have a neural network model `model` and a loss function `criterion`
# Assuming we have input data `x` and target `y`

# Forward pass to compute output and loss
output = model(x)
loss = criterion(output, y)

# Initialize the gradient table (grad_table) as a list of tensors
grad_table = [None] * len(list(model.parameters()))

# Perform the backward pass to compute gradients
loss.backward()

# Fill the grad_table with the gradients of the loss with respect to each parameter
for i, param in enumerate(model.parameters()):
    grad_table[i] = param.grad

# The grad_table now contains the gradients dL/dw for each parameter w(i)