Numpy Mnist Classifier

This project was done for learning purpose. The goal was to implement a deep neural network to do supervised learning. The MNIST data set was used due to its small size, making the learning process fast enough on a personal laptop.

To try the project you first need to install the dependencies, note that python3 is required.

pip install -r requirements/basic.txt

Usage

It only takes a small amount of code to test some models :

    from classifier import nn, training
    from data import mnist

    # The MNIST data set will be automatically downloaded and cached.
    training_data, validation_data, test_data = mnist.load()

    # Create a Neural Network with one hidden layer.
    model = nn.NeuralNetwork([784, 30, 10], learning_rate=0.02, batch_size=50)

    # Train the model with early stopping regularization.
    model_training = training.EarlyStoppingRegularization(model,
                                                          training_data,
                                                          validation_data,
                                                          test_data,
                                                          max_steps_without_progression=2)
    model_training.train()

    # It is possible to save the result which serializes the model and create a report.
    result.save('models/mnist-example')

    # It is possible to load the trained model for futur uses.
    model_trained = nn.load('models/mnist-example/model.pkl)

Report Example

Model

• Layers : [784, 30, 10]
• Activation : sigmoid
• Learning Rate : 0.02
• Batch Size : 50

Training

• Method : early stopping regularization
• Epochs : 69

Data

Size :

• Training : 50000
• Test : 10000
• Validation : 10000

Sample

Accuracy and Loss

	Training	Test
Accuracy	97.392%	95.430%
Loss	0.046	0.081