# The Maths behind Neural Networks

Alex Punnen

© All Rights Reserved

2019-2021

## Contents

- Chapter 1: Vectors, Dot Products and the Perceptron
- Chapter 2: Feature Vectors, Dot products and Perceptron Training
- Chapter 3: Gradient Descent, Gradient Vector and Loss Function
- Chapter 4: Activation functions, Cost functions and Back propagation
**Chapter 5: Back Propagation with Matrix Calulus**- Chapter 6: A Simple NeuralNet with above Equations

## Chapter 5: Back Propagation for a Two layered Neural Network (Matrix Calculus)

Let’s take the simple neural network and walk through the same, first going through the maths and then the implementation.

Let’s write the equation of the following neural network

```
x is the Input
y is the Output.
l is the number of layers of the Neural Network.
a is the activation function ,(we use sigmoid here)
```

Where the activation $a^l$ is \(a^{l} = \sigma(w^l a^{l-1}+b^l).\) http://neuralnetworksanddeeplearning.com/chap2.html#eqtn25

and \(a^{l} = \sigma(z^l) \quad where \quad z^l =w^l a^{l-1} +b^l\)

## A Two Layered Neural Network

Let’s start with a concrete case of a Neural network with two layers and derive the equations of back propagation for that first. Each of these are explained in more detail in the previous sections.

Our two layer neural network can be written as

\[\mathbf { a^0 \rightarrow a^{1} \rightarrow a^{2} \rightarrow y }\]($a^2$ does not denote the exponent but just that it is of layer 2)

Lets write down the derivative of Loss function wrto weight using chain rule

\[\mathbf { \frac {\partial C}{\partial w^l} = \frac {\partial a^l}{\partial w^l} . \frac {\partial C}{\partial a^l} }\]We will use the above equation as the basis for the rest of the chapter.

## Gradient Vector/Matrix/2D tensor of Loss function wrto Weight in last layer

\[C = \frac{1}{2} \sum_j (y_j-a^L_j)^2\]Assuming a neural net with 2 layers, we have the final Loss as

\[C = \frac{1}{2} \sum_j (y_j-a^2_j)^2\]Where

\[a^2 = \sigma(w^2.a^1)\]We can then write

\[C = \frac{1}{2} \sum_j v^2 \quad \rightarrow (Eq \;A)\]Where

\[v= y-a^2\]## Partial Derivative of Loss function wrto Weight

For the last layer, lets use Chain Rule to split like below

\[\frac {\partial C}{\partial w^2} = \frac{\partial v^2}{\partial v} * \frac{\partial v}{\partial w^2} \quad \rightarrow (Eq \;B)\] \[\frac{\partial v^2}{\partial v} =2v \quad \rightarrow (Eq \;B.1)\] \[\frac{\partial v}{\partial w^2}= \frac{\partial (y-a^2)}{\partial w^2} = 0-\frac{\partial a^2}{\partial w^2} \quad \rightarrow (Eq \;B.2)\]\(\frac {\partial C}{\partial w^2} = \frac{1}{2} *2v(0-\frac{\partial a^2}{\partial w^2}) \quad \rightarrow (Eq \;B)\)

### Now we need to find $\frac{\partial a^2}{\partial w^2}$

Let

\(a^2= \sigma(sum(w^2 \otimes a^1 )) = \sigma(z^2)\) \(z^2 = sum(w^2 \otimes a^1 )\)

\[z^2 = sum(k^2) \; \text {where} \; k^2=w^2 \otimes a^1\]We now need to derive an intermediate term which we will use later

\(\frac{\partial z^2}{\partial w^2} =\frac{\partial z^2}{\partial k^2}*\frac{\partial k^2}{\partial w^2}\) \(=\frac {\partial sum(k^2)}{\partial k^2}* \frac {\partial (w^2 \otimes a^1 )} {\partial w^2}\) \(\frac{\partial z^2}{\partial w^2} = (1^{\rightarrow})^T* diag(a^1) =(a^{1})^T \quad \rightarrow (Eq \;B.3)\) How the above is, you need to check this in https://explained.ai/matrix-calculus/#sec6.2

Basically though these are written like scalar here; actually all these are partial differention of vector by vector, or vector by scalar; and a set of vectors can be represented as the matrix here.

Note that the vector dot product $w.a$ when applied on matrices becomes the elementwise multiplication $w^2 \otimes a^1$ (also called Hadamard product)

Going back to $Eq \;(B.2)$

\[\frac {\partial a^2}{\partial w^2} = \frac{\partial a^2}{\partial z^2} * \frac{\partial z^2}{\partial w^2}\]Using $Eq \;(B.3)$ for the term in left

\[= \frac{\partial a^2}{\partial z^2} * (a^{1})^T\] \[= \frac{\partial \sigma(z^2)}{\partial z^2} * (a^{1})^T\] \[\frac {\partial a^2}{\partial w^2} = \sigma^{'}(z^2) * (a^{1})^T \quad \rightarrow (Eq \;B.4)\]Now lets got back to partial derivative of Loss function wrto to weight

\(\frac {\partial C}{\partial w^2} = \frac {1}{2}*2v(0-\frac{\partial a^2}{\partial w^2}) \quad \rightarrow (Eq \;B)\) Using $Eq \;(B.4)$ to substitute in the last term

\[= v(0- \sigma^{'}(z^2) * (a^{1})^T)\] \[= v*-1*\sigma^{'}(z^2) * (a^{1})^T\] \[= (y-a^2)*-1*\sigma^{'}(z^2) * (a^{1})^T\]\(\frac {\partial C}{\partial w^2}= (a^2-y)*\sigma^{'}(z^2) * (a^{1})^T \quad \rightarrow \mathbb Eq \; (3)\)

## Gradient Vector of Loss function in Inner Layer

Now let’s do the same for the inner layer. This is bit more tricky and we use the Chain rule to derive this

\[\frac {\partial C}{\partial w^1} = \frac {\partial a^1}{\partial w^1} . \frac {\partial C}{\partial a^1} \quad \rightarrow (4.0)\]

We can calculate the first part of this from $Eq\; (B.4)$ that we derived above

\[\frac {\partial a^2}{\partial w^2} = \sigma^{'}(z^2) * (a^{1})^T \quad \rightarrow (Eq \;B.4)\] \[\frac {\partial a^1}{\partial w^1} = \sigma'(z^1) * (a^{0})^T \quad \rightarrow (4.1)\]For the second part, we use Chain Rule to split like below, the first part of which we calculated in the earlier step.

\[\frac{\partial C}{\partial(a^1)} = \frac{\partial C}{\partial(a^2)}.\frac{\partial(a^2)}{\partial(a^1)}\] \[{ \frac{\partial C}{\partial(a^2)} = \frac {\partial({\frac{1}{2} \|y-a^2\|^2)}}{\partial(a^2)} = \frac{1}{2}*2*(a^2-y) =(a^2-y) = \delta^{2} }\] \[\begin{aligned} \frac {\partial C}{\partial(a^2)} =\delta^{2} \rightarrow (4.2)\\ \\ \text {Now to calculate} \quad \frac{\partial(a^2)}{\partial(a^1)} \quad where \quad a^{2} = \sigma(w^2 a^{1}+b^2) \\ \\ \frac{\partial(a^2)}{\partial(a^1)} = \frac{\partial(\sigma(w^2 a^{1}+b^2))}{\partial(a^1)} = w^2.\sigma'(w^2 a^{1}+b^2) = w^2.\sigma'(z^2)\rightarrow (4.3)* \end{aligned}\]*https://math.stackexchange.com/a/4065766/284422

Putting (4.1) (4.2) and (4.3) together

## Final Equations

\[\mathbf{ \frac {\partial C}{\partial w^1} = \sigma'(z^1) * (a^{0})^T*\delta^{2}*w^2.\sigma'(z^2) \quad \rightarrow \mathbb Eq \; (5) }\] \[\delta^2 = (a^2-y)\]Adding also the partial derivate of loss funciton with respect to weight in the final layer

\[\mathbf{ \frac {\partial C}{\partial w^2}= \delta^{2}*\sigma^{'}(z^2) * (a^{1})^T \quad \rightarrow \mathbb Eq \; (3) }\]And that’s that. You can see that the inner layer derivative have terms from the outer layer. So if we store and use the result; this is like dynamic program; maybe the backprogation algorithm is the most elegant dynamic programming till date.

\[\mathbf{ \frac {\partial C}{\partial w^1} = \delta^{2}*\sigma'(z^2)*(a^{0})^T*w^2.\sigma'(z^1) \quad \rightarrow \mathbb Eq \; (5) }\]

## Using Gradient Descent to find the optimal weights to reduce the Loss function

With equations (3) and (5) we can calculate the gradient of the Loss function with respect to weights in any layel - in this example

\[\frac {\partial C}{\partial w^1},\frac {\partial C}{\partial w^2}\]

We now need to adjust the previous weight, by gradient descent.

So using the above gradients we get the new weights iteratively like below. If you notice this is exactly what is happening in gradient descent as well; only chain rule is used to calculate the gradients here. Backpropagation is the algorithm that helps calculate the gradients for each layer.

\[\mathbf { W^{l-1}_{new} = W^{l-1}_{old} - learningRate* \delta C_0/ \delta w^{l-1} }\]

That’s it

Reference

- https://cedar.buffalo.edu/~srihari/CSE574/Chap5/Chap5.3-BackProp.pdf
- http://neuralnetworksanddeeplearning.com/chap2.html

## Contents

- Chapter 1: Vectors, Dot Products and the Perceptron
- Chapter 2: Feature Vectors, Dot products and Perceptron Training
- Chapter 3: Gradient Descent, Gradient Vector and Loss Function
- Chapter 4: Activation functions, Cost functions and Back propagation
**Chapter 5: Implementing a Neural Network using Chain Rule and Back Propagation**- Chapter 6: A Simple NeuralNet with above Equations