feature vector of NN

Illustration
ritasingh - 2021-04-16T14:48:28+00:00
Question: feature vector of NN

how to find the feature vector of NN in the attached results obtained from the nprtool (which is app for develop pattern recognition appalication of Neural network).  

Related Questions

  • How to Plot data in excel sheet using MATLAB ?
  • How can I use LSTM networks to solve a time series regression problem?
  • How to create an attention layer for deep learning networks?
  • Nvidia NVLink in MATALB using multiple Geforce 2080 ti cards
  • How do I build TensorFlow Lite for Deep Learning C++ code generation and deployment?

    • Expert Answer

    Profile picture of Prashant Kumar Prashant Kumar answered . 2025-11-20

    You can extract the network and inputs from the 'results' struct using..
     
     
    net = results.net
inputs = results.inputs
    I found 2 ways to extract activations from intermediate layers in a 'network' object :-
     
    Approach 1
    You may modify the architecture to specify the required layer as an outputLayer..
     
     
    % Say 'net' is the network. Assume you want the output of layer 'i'.
net.outputConnect(i) = 1 % Modifies the architecture to set layer 'i' as an additional output.
out = sim(net, inputs) % the activations of i-th layer are now included in the output vector.
    Approach 2
     
    Manually run the neural network by executing it's underlying maths.
     
    Suppose you're trying to get the outputs of layer 3...
     
    X = inputs; % Initialize 'X' as inputs. Each layer's values will be computer into 'X'. 

% Layer 1
X = net.IW{1,1} * X + net.b{1}; % Compute the values of layer 1. Use 'net.IW' (input weights)
X = tansig(X) % apply whichever is the activation function of layer 1.

% Layer 2
X = net.LW{2,1} * X + net.b{2}; % compute the values of layer 2. Use 'net.LW' (layer weights)
X = logsig(X) % apply whichever is the activation function of layer 2.

% layer 3
X = net.LW{3,2} * X + net.b{3} % same of for layer 2
X = softmax(X) % apply whichever is the activation function of layer 3.

% 'X' now contains the activations of the third layer in the network.
    The manual computation might require tinkering with the 'network' object's many properties. You may follow the attached documentation to get a better understanding of these properties and how to manipulate them.
     
    https://www.matlabsolutions.com/documentation/deep-learning.php
     


    Not satisfied with the answer ?? ASK NOW

    Get a Free Consultation or a Sample Assignment Review!

    Upload Assignment Talk to Expert