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Preparation

Extract data.zip to the same folder with scripts.

Trainig

To run training with cuda:

python train.py cuda

To run training with cpu:

python train.py

During the run program outputs some information about current hyperparameters and losses, accuracy. \

Program will save the current best performing network and overwrite the previous one during all the run.
As a result, after program finishes we have the best network saved in 'model_state_dict' file.

Testing

To run testing with cuda:

python test.py cuda

To run testing with cpu:

python test.py

Testing should be done only after training program completed! Because it needs to first load saved best network.

Additional scripts

  • Sanity check:

python sanity_check.py

  • Plot training and validation losses of best network (should run training first):

python plot_best.py

About

Implementation of ANN (Artificial Neural Network) to classify the 2-digit numbers on the dataset created using MNIST Dataset.

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