## digit-recognition ##
This is my attempt to create a digit recognition neural network from scratch (without machine learning frameworks, only numpy + math).
## Model info ##
- It consists of one input layer (for the 28 * 28 grayscale pixel values), 2 hidden layers (each with 10 nodes) and one output layer (which corresponds which digit is the most likely).
- It uses relu activation layers for the hidden layers and a softmax activation layer for the output layer.
- It uses the MSE as the loss function. The error in this case is the model output subtracted by a one hot vector of the expected output.
- It uses standard backpropagation with a constant learning rate and stochastic learning to update the weight matrix.
- The weights are initialized using a normal distribution, as described in the research paper of Le Cun et al.
- I could get it accurate up to 96%.
- It is licensed under the MIT license.
## Dependencies ##
- numpy
- matplotlib
## Training the model ##
To start training pleas use python3 main.py.
This will initialize the model and start training. Periodically a matplotlib
window will be opened showing a sample of test images and the model prediction
to visualize the model's accuracy.
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