lab1.md

Lab 1: Single-character prediction

Before you begin, make sure to set up!

Please complete Lab Setup before proceeding!

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

git pull
cd lab1/

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

- text_recognizer/networks/mlp.py
- text_recognizer/networks/lenet.py
- text_recognizer/models/base.py
- text_recognizer/models/character_model.py
- training/util.py

Train MLP and CNN

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

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:

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

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:

pytest -s text_recognizer/tests/test_character_predictor.py

Or, use the shorthand tasks/test_functionality.sh

Testing should finish quickly.