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image_process.py = takes in directories of raw screenshots from plays and outputs numpy arrays of RGB
- Key parameters = size of output numpy array for images
- Key input = raw screenshots, coverage array for screenshots that contains time of play in game, teams
- Output = numpy array of image data along with time of play in games, teams
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merge_data.py = merge image data with Kaggle data on time of play in game, teams
- Key parameters = output directory for merged data
- Key input = numpy array from Kaggle, numpy array of image data
- Output = merged numpy array
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pre_process_all.py = one-hot encode kaggle features, clean data for usable plays, get labels
- Key parameters = output directory for pre-processed data
- Key input = merged numpy array
- Output = X, Y
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ml_features.py = benchmark models with Kaggle data
- Key parameters = train/dev/test split, which y to predict
- Key input = pre-processed non-image X, Y
- Output = tuning parameters and model error
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shallow_cnn.py / shallow_cnn_classify.py = shallow cnn model on image and non-image data (yards / play classification)
- Key parameters = hyperparameters (epochs, learning rate, mini-batch, num hidden units, filter size, L2 parameter)
- Key input = X, Y
- Output = model error
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transfer_learn.py / transfer_learn_classify.py = VGG19 transfer learning model on image and non-image data (yards / play classification)
- Key parameters = hyperparameters (epochs, learning rate, mini-batch, num hidden units, num layers to take from VGG19, L2 parameter)
- Key input = X, Y
- Output = model error
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transfer_learn_classify_tune.py = VGG19 transfer learning model on image and non-image data w/ more systematic tuning performance and logging built in (yards / play classification)
- Key parameters = hyperparameters (epochs, learning rate, mini-batch, num hidden units, num layers to take from VGG19, L2 parameter)
- Key input = X, Y
- Output = log of model error and hyperparameters