my-course-work / linearsmiles

Smile detectors based on linear regression

linearSmile

Smile detectors based on linear regression

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What's inside?

• Analytical Smile Detector
• Gradient Descent Smile Detector

Analytical Weight

analytical_weight_detector.py computes the optimal weights w and bias term b for the linear regression model by deriving the expression for the gradient of the cost function w.r.t. w and b, setting it to 0, and then solving.

The weight is computed as

with the cost function of

Gradient Descent

The algorithm picks a random starting value for w and b and a small learning rate (e.g., ε = .001). Then, using the expression for the gradient of the cost function, iteratively update w,b to reduce the cost. Stop when the difference between costs over successive training rounds is below some “tolerance” (e.g., δ = 0.000001). This method of optimizing model weights is much more general than analytically computing the optimal weights, at the expense of requiring some additional optimization hyperparameters (e.g., learning rate, tolerance).

The gradient of error w.r.t weight and w.r.t bias are computed as

and

individually, where

Gradient Descent with Regulation

In machine learning, low training error and high testing error imply overfitting. One of the simplest and oldest regularization techniques is to panelize large weights in the cost function. The L2 regularized error and the regularized gradient of error w.r.t weight are computed as

and

seperately.

Getting Started

Prerequisite

• Python v3.6.4
• NumPy v1.14.0

Running

Analytical Weight Detector

To test out the analytical weight detector, run the following command in the terminal:

python3 analytical_weight_detector.py

Here is an example output:

Training MSE: 0.049402
Testing MSE: 0.092220

Training Accuracy: 0.895000
Testing Accuracy: 0.767505

Gradient Descent Detector

To test out the gradient descent detector, run the following command in the terminal:

python3 gradient_descent_detector.py

Here is an example output:

Start training the model
Training MSE: 0.052163
Testing MSE: 0.086731

Training Accuracy: 0.882500
Testing Accuracy: 0.774617

Gradient Descent with Regulation Detector

To test out the gradient descent with regulation detector, run the following command in the terminal:

python3 gradient_descent_regulation_detector.py

Here is an example output:

Start training the model
Unregulated Training MSE: 0.066756
Unregulated Testing MSE: 0.073758

Training Accuracy: 0.841000
Testing Accuracy: 0.811269

Authors

• Ben Hylak - Initial work - bhylak

• Yang Liu - Initial work - byliuyang

License

This repo is maintained under MIT license.