umbertogriffo / minimalistic-multiple-layer-neural-network-from-scratch-in-python

Minimalistic Multiple Layer Neural Network from Scratch in Python.

Minimalistic Multiple Layer Neural Network from Scratch in Python

• Author: Umberto Griffo

Inspired by [1] and [2] I implemented a Minimalistic Multiple Layer Neural Network from Scratch in Python. You can use It to better understand the core concepts of Neural Networks.

Software Environment

• Python 3.0 - 3.5

Features

• Backpropagation Algorithm With Stochastic Gradient Descent. During training we are using single training examples for one forward/backward pass.
• Supporting multiple hidden layers.
• Classification (MultilayerNnClassifier.py).
• Regression (MultilayerNnRegressor.py).
• Activation Function: Linear, ReLU, Sigmoid, Tanh.
• Classification Evaluator: Accuracy.
• Regression Evaluator: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Coefficient of Determination (R^2).

Demo

If you run Test.py you can see the following textual menu:

Please enter one of following numbers: 
 0 - Classification on Seed Dataset
 1 - Classification on Wine Red Dataset
 2 - Classification on Pokemon Dataset
 3 - Regression on Wine White Dataset
 4 - Regression on Wine Red Dataset 

If you choose 2 will be performed a classification task on Pokemon Dataset:

2
You entered 2
>epoch=0, lrate=0.100, error=0.396
>epoch=100, lrate=0.100, error=0.087
>epoch=200, lrate=0.100, error=0.083
>epoch=300, lrate=0.100, error=0.081
>epoch=400, lrate=0.100, error=0.081
>epoch=500, lrate=0.100, error=0.080
>accuracy=95.450
>epoch=0, lrate=0.100, error=0.353
>epoch=100, lrate=0.100, error=0.092
>epoch=200, lrate=0.100, error=0.085
>epoch=300, lrate=0.100, error=0.083
>epoch=400, lrate=0.100, error=0.082
>epoch=500, lrate=0.100, error=0.081
>accuracy=95.400
>epoch=0, lrate=0.100, error=0.415
>epoch=100, lrate=0.100, error=0.087
>epoch=200, lrate=0.100, error=0.083
>epoch=300, lrate=0.100, error=0.082
>epoch=400, lrate=0.100, error=0.081
>epoch=500, lrate=0.100, error=0.080
>accuracy=95.520
>epoch=0, lrate=0.100, error=0.401
>epoch=100, lrate=0.100, error=0.089
>epoch=200, lrate=0.100, error=0.084
>epoch=300, lrate=0.100, error=0.083
>epoch=400, lrate=0.100, error=0.082
>epoch=500, lrate=0.100, error=0.081
>accuracy=95.280
>epoch=0, lrate=0.100, error=0.395
>epoch=100, lrate=0.100, error=0.093
>epoch=200, lrate=0.100, error=0.087
>epoch=300, lrate=0.100, error=0.085
>epoch=400, lrate=0.100, error=0.084
>epoch=500, lrate=0.100, error=0.083
>accuracy=94.900
Scores: [95.45, 95.39999999999999, 95.52000000000001, 95.28, 94.89999999999999]
Mean Accuracy: 95.310%

Possible Extensions:

• Early stopping.
• Experiment with different weight initialization techniques (such as small random numbers).
• Batch Gradient Descent. Change the training procedure from online to batch gradient descent and update the weights only at the end of each epoch.
• Mini-Batch Gradient Descent. More info here.
• Momentum. More info here.
• Annealing the learning rate. More info here.
• Dropout Regularization, Batch Normalization. More info here.
• Model Ensembles. More info here.

References:

• [1] How to Implement Backpropagation Algorithm from scratch in Python here.
• [2] Implementing Multiple Layer Neural Network from Scratch here.
• [3] Andrew Ng Lecture on Gradient Descent here.
• [4] Andrew Ng Lecture on Backpropagation Algorithm here.
• [5] (P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.) here
• [6] seeds Data Set here