If your code is taking too long to run, you will need to either reduce the complexity of your chosen RNN architecture or switch to running your code on a GPU. If you'd like to use a GPU, you have two options:
If you have access to a GPU, you should follow the Keras instructions for running Keras on GPU.
Instead of a local GPU, you could use Amazon Web Services to launch an EC2 GPU instance. (This costs money.)
| Criteria | Meets Specifications |
|---|---|
| Submission Files | RNN_project.ipynb, my_answers.py --> both the completed notebook RNN_project.ipynb as well as all completed python functions requested in the main notebook RNN_project.ipynb (TODO items) should be copied into this python script and submitted for grading. |
| Criteria | Meets Specifications |
|---|---|
| Comments | The submission includes comments that describe the functionality of the code. Every line of code is preceded by a meaningful comment. 1. describing input parameters to Keras module functions. 2. function calls 3. explaning thought process in common language |
| Criteria | Meets Specifications |
|---|---|
| Window time series data. | The submission returns the proper windowed version of input time series of proper dimension listed in the notebook. |
| Criteria | Meets Specifications |
|---|---|
| Build an RNN model to perform regression. | The submission constructs an RNN model in keras with LSTM module of dimension defined in the notebook. |
| Criteria | Meets Specifications |
|---|---|
| Find and remove all non-english or punctuation characters from input text data. The submission removes all non-english / non-punctuation characters. |
| Criteria | Meets Specifications |
|---|---|
| Implement a function to window input text data | The submission returns the proper windowed version of input text of proper dimension listed in the notebook. |
| Criteria | Meets Specifications |
|---|---|
| Build an RNN model to perform multiclass classification. | The submission constructs an RNN model in keras with LSTM module of dimension defined in the notebook. |
| Criteria | Meets Specifications |
|---|---|
| Generate text using a trained RNN classifier. | The submission presents examples of generated text from a trained RNN module. The majority of this generated text should consist of real english words. |