CODEWITHSUNDEEP
pythonneural-networkpytorch
Penguin
Penguin

Reputation: 2441

How can I get the in and out edges weights for each neuron in a neural network?

Say I have the following network

import torch
import torch.nn as nn

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.fc1 = nn.Linear(1, 2)
        self.fc2 = nn.Linear(2, 3)
        self.fc3 = nn.Linear(3, 1)

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

net = Model()

I know that I can access the weights (i.e edges) at each layer:

net.fc1.weight

However, I'm trying to create a function that randomly selects a neuron from the entire network and outputs it's in-connections (i.e the edges/weights that are attached to it from the previous layer) and its out-connections (i.e the edges/weights that are going out of it to the next layer).

pseudocode:

def get_neuron_in_out_edges(list_of_neurons):
    shuffled_list_of_neurons = shuffle(list_of_neurons)

    in_connections_list = []
    out_connections_list = []

    for neuron in shuffled_list_of_neurons:
        in_connections = get_in_connections(neuron) # a list of connections
        out_connections = get_out_connections(neuron) # a list of connections

        in_connections_list.append([neuron,in_connections])
        out_connections_list.append([neuron,out_connections])
    
    return in_connections_list, out_connections_list

The idea is that I can then access these values and say if they're smaller than 10, change them to 10 in the network. This is for a networks class where we're working on plotting different networks so this doesn't have to make much sense from a machine learning perspective

Upvotes: 1

Views: 53

Answers (1)

Shai
Shai

Reputation: 114826

Let's ignore biases for this discussion.

A linear layer computes the output y given weights w and inputs x as:
y_i = sum_j w_ij x_j

So, for neuron i all the incoming edges are the weights w_ij - that is the i-th row of the weight matrix W.

Similarly, for input neuron j it affects all y_i according to the j-th column of the weight matrix W.

Upvotes: 2

Related Questions