Train Model with TensorFlow/Keras/Xgboost in Python

This is my repo for local model development in python.

Follow the following steps to create virtual environment and install all dependencies:

virtualenv env
source env/bin/activate
pip3 install -r requirements.txt

If you use PyCharm for Python Dev (recommended), follow the instruction https://www.jetbrains.com/help/pycharm/creating-virtual-environment.html to run PyCharm on the virtualenv created in previous steps.

To run script in Jupyter Notebook

jupyter notebook
jupyter notebook --ip=0.0.0.0   (Run within EC2 such that the port can be accessed externally)

To show tensor board, go to the root directory that contains TensorFlow logs, within the virtual environment

tensorboard --logdir ./yourLogDirName

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
src		src
.gitignore		.gitignore
README.md		README.md
batch_score.ipynb		batch_score.ipynb
playground.ipynb		playground.ipynb
precision_recall_analysis.ipynb		precision_recall_analysis.ipynb
requirements.txt		requirements.txt
train_ar_tf_model.ipynb		train_ar_tf_model.ipynb
train_xgboost.ipynb		train_xgboost.ipynb
tran_ar_lstm_model.ipynb		tran_ar_lstm_model.ipynb

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