## Wednesday, February 14, 2018

### Neural Network weight optimization algorithms

In this post, I write about different ways of updating neural network weights.

I’m writing this post out of the notes I took for the class “Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization”.

Specifically, I discuss about optimizing the neural network weights using non-traditional gradient descent algorithms.

Applying proper optimization techniques have direct effect on the performance of the neural network.

Required pre-requisites to understand this content:
1. Neural Networks
3. Back Propagation
4. Cost functions

To explain the following, I’m assuming neural network weights θ to be 1D/2D vector.
The different ways to update the neural network weights are described as follows:

1. It is prone to larger oscillations during its descent to global/local optimum while optimizing the cost function. This affects performance.
2. To reduce the oscillations, we use (exponentially weighted) moving average like approaches to dampen out the effects of these oscillations.
3. This ensures smoother transition to the global/local optimum while optimizing the cost function.

## Exponentially weighted Moving averages:

Assuming we have a 1D vector V, that changes over time (like temperature). It is prone to large local variations.

To correct for that (quick and abrupt) change, we perform the following operations

Vcorrected  = (β* Vt-1) + (1 - β) * Voriginal

In other words, Vcorrected ≈ Approximately average over (1.0 / (1.0 - β)) observations

For example,

If β = 0.9 ≈ approximately last 10 observations average.

If β = 0.98 ≈ approximately last 50 observations average.

1. Larger value of β gives smoother curves (as opposed to zig-zag/abrupt movement as observed in pure gradient descent).
2. Bias correction in exponential weighted moving averages applied to gradient descent like algorithms don't affect them significantly. They can be implemented if needed. This is required in ADAptive momentum algorithm.

## Momentum

Since, we understand basics of exponentially weighted moving averages, we can apply that in weight update step in neural network optimization.

We update the weights by performing the following steps:

Vdw = (β * Vdw) + ((1- β)* Vdw)
Vdb = (β * Vdb) + ((1- β)* Vdb)

Weight update step:

W = W - (α * Vdw) instead of W = W - (α * dw)
b = b - (α * Vdb) instead of b = b - (α * dw)
Usually Beta = 0.9

1. This weight update procedure gives us smoother convergence to global/local minimum.
2. The Vdw, Vdb terms are derived from the exponentially weighted moving average equations.

## RMSProp

Root Mean Squared Propagation. Interestingly this was proposed in the Coursera course by Geoffrey Hinton back in 2011-2012.

While applying gradient descent, we update the weights by performing the following:

Sdw = (β * Sdw) + ((1- β)* (dw)2 )

Sdb = (β * Sdb) + ((1- β)* (db)2 )

Weight update step:
W = W - (α * (dw/ (sqrt(Sdw) +ε))) instead of W = W - (α * dw)
b = b - (α * (db/ sqrt(Sdb)+ε))) instead of W = W - (α * db).

The Sdw, Sdb terms are derived from the exponentially weighted moving average equations.

This is the most commonly used/popular optimization algorithm in the computer vision community.

We have slightly changed update algorithm compared to Momentum.

While applying gradient descent, we update the weights by performing the following:

From Momentum we have:

Vdw = (β1 * Vdw) + ((1.0 - β1) * Vdw)
Vdb = (β1 * Vdb) + ((1.0 - β1) * Vdb)

You have to perform bias correction:

Vdw_corrected = Vdw /(1.0 - ( β1)t)
Vdb_corrected = Vdb /(1.0 - ( β1)t)

From RMSProp we have

Sdw = (β2 * Sdw) + ((1.0 - β2)* (dw)2 )
Sdb = (β2 * Sdb) + ((1.0 - β2)* (db)2 )

You have to perform bias correction:

Sdw_corrected = Sdw /(1.0 - ( β2)t)
Sdb_corrected = Sdb /(1.0 - ( β2)t)

Finally, we perform weight updates:

W = W - (α * (Vdw/ (sqrt(Sdw)+ε))) instead of W = W - (α * dw).
b = b - (α * (Vdb/ (sqrt(Sdb)+ε))) instead of b = b - (α * db).

All of this reminds me of Kalman filters, where the observed signal value is not exactly correct, we observe some process noise and observation noise and we try to incorporate that to have a updated/corrected signal value from the sensor.