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Digit Recognizer

A digit recognizer using the MNIST Dataset built using TensorFlow.js and Keras.

Demo

Click here to check out the live version.

Architecture

The architecture of the model was taken from the tutorial on the website of TensorFlow.js, link to the tutorial can be found here.

To summarize, shown below is the architecture:

  • 2D Convolutional Layer
  • 2D Max Pooling Layer
  • 2D Convolutional Layer
  • 2D Max Pooling Layer
  • Flatten Layer
  • Dense Layer

The input given to the network is a Tensor of shape [28,28,1]. Since the MNIST Dataset originally stores the values in a linear array of depth 784, we need to preprocess our data to get in the desired format. To preprocess the data, I have used the data.js file link to which can be found here.

Model Options

There are 3 things the user can do with the model:

  1. Load Model: This loads a pre-trained model which was trained using Keras with an Accuracy of over 98%. The model is imported using tf.loadModel.
  2. Train Model: This trains the model using fit which is a function belonging to tf.Model for a set number of iterations given by the user in the Train Iterations input field. As the model is being trained, the results are plotted using Plotly.js library.
  3. Test Model: This just tests the model over the entire test set and gives out an Accuracy and a Loss value. The model is tested using evalute which is a function belonging to tf.Model.

Prediction

The user can draw a number on the canvas which was was created using the p5.js library. The user can then click on Predict which runs the predict function in the tf.Model and gives an output of the number. The results of all the class scores are plotted.

Helper Functions

There are 3 helper functions that were used:

  1. canvasToMnistData: This function uses the pixel array of the canvas and converts it into an array of 784 values. The size of the canvas was chosen to be a multiple of 28 to allow easy transformation. Since the canvas is larger than the size 28×28, Max Pooling was applied over the entire pixel array.
  2. mnistDataToCanvas: This function takes in an array of 784 values and draws it onto the canvas. The function uses the pixel array and the conversion from Grayscale to RGB was done by duplication of values across all the axes.
  3. RGB2GRAY: The MNIST images are of Grayscale color format and the canvas element is of type RGB color format. This function just converts an RGB color to a GRAYSCALE value using Relative Luminance link to the Wikipedia page can be found here.