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Dropout Regularization

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Neural Networks

Difficulty: 4 | Problem written by zeyad_omar
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Dropout regularization is a technique that is used to avoid overfitting by dropping weights randomly. In this problem, you are required to implement dropout regularization given the weights (2D array) of a neural networkand the probability of dropping the weights out.

Inputs:

p: probability of dropping out

weights: a 2D matrix containg the weights of a neural net where each column represents the weights of a layer. For example, if we have a simple neural net which has two inputs X1 and X2 and two outputs Y1 and Y2, then:

Y1 would be X1 * W11 + X2 * W12

Y2 would be X2 * W21 + X2 * W22

The weights matrix will be:

[ [W11  W21],

[W12  W22]]

Make sure you use random.seed(10) to get the same output as the test cases.

To determine whether a weight will be dropped, only set a weight to 0 if its corresponding position in the below matrix is less than 1-p:

np.random.seed(10)
prob_matrix = np.random.random( (np.array(weights).shape) )

Sample Input:
<class 'list'>
weights: [[2.2, 5.7], [6.8, 0.8]]
<class 'float'>
p: 0.4

Expected Output:
<class 'numpy.ndarray'>
[[1.69690542 0. ] [4.308808 0.59904311]]

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numpy has been already imported as np (import numpy as np)