Neural Network Weight Initialization

Neural Networks

Difficulty: 4 | Problem written by hemdan219@gmail.com
Problem reported in interviews at


 You are given the number of nodes in input layer x_nodes, a list of the number of nodes in each of two hidden layers h_nodes, and finally the number of nodes in the output layer y_nodes.

Your task is to initialize weight matrices for the three layers depending on the nodes of previous layer and the nodes of current layer. Don't worry about initializing a bias vector. The size of the 3 weight matrices in this small neural network is given by the following formula, where \(n^{[l]}\) is the number of nodes in layer \(l\).

\( Size \,of \,{w^{[l]}}={({n^{[l]}},{n^{[l-1]}})}\),


Use np.random.randn to initialize the weights.

Return a tuple containing the three weight matrices W1, W2, and W3.

Use np.random.seed(3) to initialize the random number generator to get the same expected output as our test cases.



Sample Input:
<class 'int'>
x_nodes: 3
<class 'list'>
h_nodes: [7, 4]
<class 'int'>
y_nodes: 1

Expected Output:
<class 'tuple'>
(array([[ 1.78862847, 0.43650985, 0.09649747], [-1.8634927 , -0.2773882 , -0.35475898], [-0.08274148, -0.62700068, -0.04381817], [-0.47721803, -1.31386475, 0.88462238], [ 0.88131804, 1.70957306, 0.05003364], [-0.40467741, -0.54535995, -1.54647732], [ 0.98236743, -1.10106763, -1.18504653]]), array([[-0.2056499 , 1.48614836, 0.23671627, -1.02378514, -0.7129932 , 0.62524497, -0.16051336], [-0.76883635, -0.23003072, 0.74505627, 1.97611078, -1.24412333, -0.62641691, -0.80376609], [-2.41908317, -0.92379202, -1.02387576, 1.12397796, -0.13191423, -1.62328545, 0.64667545], [-0.35627076, -1.74314104, -0.59664964, -0.58859438, -0.8738823 , 0.02971382, -2.24825777]]), array([[-0.26776186, 1.01318344, 0.85279784, 1.1081875 ]]))

This is a premium problem, to view more details of this problem please sign up for MLPro Premium. MLPro premium offers access to actual machine learning and data science interview questions and coding challenges commonly asked at tech companies all over the world

MLPro Premium also allows you to access all our high quality MCQs which are not available on the free tier.

Not able to solve a problem? MLPro premium brings you access to solutions for all problems available on MLPro

Get access to Premium only exclusive educational content available to only Premium users.

Have an issue, the MLPro support team is available 24X7 to Premium users.

This is a premium feature.
To access this and other such features, click on upgrade below.

Log in to post a comment

Jump to comment-110
uozcan12 • 5 months, 3 weeks ago


I did this but it did not pass all test cases. What should I do?


import numpy as np

# Please do not change the below function name and parameters
def initial_weights(x_nodes, h_nodes, y_nodes):
    layers_dims = []
    layers_dims += h_nodes
    np.random.seed(3) # This seed makes sure your "random" numbers will be the as ours
    parameters = {}
    L = len(layers_dims) # integer representing the number of layers
    for l in range(1, L):
        parameters['W' + str(l)] = np.random.randn(layers_dims[l], layers_dims[l-1])
    return tuple(parameters.values())


Jump to comment-148
sapna_sharma • 3 months ago


Same for me only 1 test case is passing.


Input Test Case

Please enter only one test case at a time
numpy has been already imported as np (import numpy as np)