Title

Multi-layer perceptrons for classification - Implementing a classifier in TensorFlow.js

About the project

Implementation of a multi layer perceptron that can classify digits (hand written number) of the famous MNIST dataset right in your web browser.

Key things: transforming images to Tensors, and understanding the new outputs of the model.

To create and test a ML model

1. Import the training data (input and output)
2. Set our Input and output tensor (shuffle the training data if need be)
3. Create the model architecture:

• set the model to be sequential
• Add all the layers (neurons including hidden, output) wit their input shapes?, units "output" and activation function

4. Train the model by:

• Compile the model with optimizer, loss function, metrics
• Fit and get the result from the training which takes 3 parameters: Input_tensor, output_tensor and an object that contains shuffle, validationSplit, batchSize, epoch
• Dispose all the tensors created
• Evaluate the model