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# Forward and Backward Propagation in a Neural Network

Unsolved###### Neural Networks

##### Problem reported in interviews at

Neural Networks are trained by a feedforward process to calculate node activations progressively and backpropagation to calculate the derivative of the loss function with respect to each weight.

In this problem, you are required to implement the **feed-forward** and **backpropagation** algorithms of a **custom-defined fully connected neural network**.

The activation function must be **sigmoid.**

The loss function must be binary cross-entropy loss.

**It is mandatory to initialize the weights and biases to zeros prior to training.**

**Input**

**X:** A vector with training data values

**y:** Labels of the training data

**l: **list of number of neurons in each hidden layer

**Output:**

After one forward and backward pass on the complete data, return the derivative values of:

**deriv_w**: list of derivatives of the loss function with respect to weights in each hidden layer

**deriv_b:** list of derivatives of the loss function with respect to bias in each hidden layer

**For example:**

X = [[2.55337307, 1.52481329], [0.95618789, 1.22932837]]

y = [0,1]

l = [2] (one hidden layers with 2 neurons)

The last hidden layer is not specified but has to specified by the user as number of classed in y.

**RESULT**

deriv_w = [array([[0., 0.], [0., 0.]]), array([[ 0.25, -0.25], [ 0.25, -0.25]])]

deriv_b = [array([[0., 0.]]), array([[ 0.5, -0.5]])]

I would recommend watching this video (https://www.youtube.com/watch?v=x_Eamf8MHwU) by Andrew Ng to understand how to calculate derivatives with respect to weights and biases using delta matrix.

You can also refer given Wikipedia link: https://en.wikipedia.org/wiki/Backpropagation for a better understanding of the algorithm.

##### Sample Input:

`X: [[2.55337307, 1.52481329], [0.95618789, 1.22932837], [0.75296472, 3.24716693], [-0.93797213, 1.26415069], [-0.39155179, 2.39860195], [0.71028504, 0.72796597], [-3.63427663, 1.35134052], [-0.24226226, -1.84743763], [2.96405404, -0.68521863], [-2.83764855, -1.61462203]]`

<class 'list'>

`y: [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0]`

<class 'list'>

`l: [2]`

##### Expected Output:

```
([array([[0., 0.],
[0., 0.]]), array([[0.05, 0.05],
[0.05, 0.05]])], [array([[0., 0.]]), array([[0.1, 0.1]])])
```

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Input Test Case

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