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readme.md

Lab 2: Single-character prediction

Goal of the lab

Train a model to solve a simplified version of the line text recognition problem.

Outline

  • Intro to EMNIST, a character prediction dataset.
  • Explore the networks and training code.
  • Train simple MLP/CNN baselines to solve EMNIST.
  • Test your model.

Follow along

cd lab2_sln/

Intro to EMNIST

  • EMNIST = Extended Mini-NIST :)
  • All English letters and digits presented in the MNIST format.
  • Look at: notebooks/01-look-at-emnist.ipynb

Networks and training code

  • Look at text_recognizer/networks/mlp.py
  • Look at text_recognizer/networks/lenet.py
  • Look at text_recognizer/models/base.py
  • Look at text_recognizer/models/character_model.py
  • Look at training/util.py

Train MLP and CNN

You can run the shortcut command tasks/train_character_predictor.sh, which runs the following:

pipenv run training/run_experiment.py --save '{"dataset": "EmnistDataset", "model": "CharacterModel", "network": "mlp",  "train_args": {"batch_size": 256}}'

It will take a couple of minutes to train your model.

Just for fun, you could also try a larger MLP, with a smaller batch size:

pipenv run training/run_experiment.py '{"dataset": "EmnistDataset", "model": "CharacterModel", "network": "mlp", "network_args": {"num_layers": 8}, "train_args": {"batch_size": 128}}'

Let's also train a CNN on the same task.

pipenv run training/run_experiment.py '{"dataset": "EmnistDataset", "model": "CharacterModel", "network": "lenet", "train_args": {"epochs": 1}}'

Training the single epoch will take about 2 minutes (that's why we only do one epoch in this lab :)). Leave it running while we go on to the next part.

It is very useful to be able to subsample the dataset for quick experiments. This is possibe by passing subsample_fraction=0.1 (or some other fraction) at dataset initialization, or in dataset_args in the run_experiment.py dictionary, for example:

pipenv run training/run_experiment.py '{"dataset": "EmnistDataset", "dataset_args": {"subsample_fraction": 0.1}, "model": "CharacterModel", "network": "mlp"}'

Testing

First, let's take a look at how the test works at

text_recognizer/tests/test_character_predictor.py

Now let's see if it works by running:

pipenv run pytest -s text_recognizer/tests/test_character_predictor.py

Or, use the shorthand tasks/run_prediction_tests.sh

Testing should finish quickly.