Perceptron Neural Network with one layer

We are going to implement stochastic, batch and mini batch gradient descent using basic data science libraries.

How to run?

Note: Please use Python3

Install the requirements:

pip install -r requirements.txt

Run the following command to run the code:

python main.py

For each gradient descent type, graphs for MSE per iteration will be stored in plots/ directory after running the code.

Directory Structure

• main.py - Entry level for the project
• layer.py - Contains layer class for each layer
• neural_network.py - Contains neural network class with added layers
• plots/ - Contains plots for MSEs

Assumptions

• We've divided data set in ration of train: test :: 8: 2.
• We're considering bias as a weight for a feature, instead of computing it individually. So, we've added 1.0 to the feature set along with the four feature. The weights for this data point will compensate for bias value.
• Mini batch size = 12
• Total epochs = 1000
• Using sigmoid as activation functon with threshold = 0.5
• Hyperparameters for mini-batch and stochastic:
  • t0 = 0.1
  • t1 = 5

Thank you :)