purpose of this assignment is to test concepts on building and training LSTM models.

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CS 6304 Deep Learning [50 points]

Purpose: The purpose of this assignment is to test concepts on building and training LSTM models.

 

What to submit: Submit Jupiter notebook that contains code and proper comments. You do not need to submit the data sets.

Refer to the Time Series Classification.ipynb jupyter notebook under week 4. This notebook illustrates how to build and train 1D CNN models to recognize 6 different kinds of human activity based on time series data of 9 features. In this assignment, you will use the same data sets used in the same notebook.

Your goal is to build four models that match the same architecture as the following models in TextClassification_LSTM.ipynb but on the human activity recognition data set. TextClassification_LSTM.ipynb jupyter notebook is under week 5 which illustrates text classification using different LSTM Models.

- layer1_lstm_model

- layer2_lstm_model

- cnn_lstm_model

- bi_lstm_model

 

For training parameters such as batch size, optimization algorithm etc, you can use the same provided in the notebook TextClassification_LSTM.ipynb. Use early stopping criterion and save the best model. Report the accuracies on the test set.

 

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