- Update (Mar. 19, 2020) * I've added a starter Python script for today's 'AMP Challenge' along with training and validation sets. The final test set will be added later!
Example scripts for running deep neural networks using Keras and TensorFlow2 in Python and R.
Example files are located in: /home/bcbb_teaching_files/intro_deep_learning/
NOTE: I suggest you either clone this GitHub repo or copy the HPC files to a folder in your local home directory!
Activate the conda environment:
conda activate /home/bcbb_teaching_files/intro_deep_learning/envs
If that does not work try the following:
source activate /home/bcbb_teaching_files/intro_deep_learning/envs
This should make Tensorflow2 and other libraries available to you. At the moment, it appears an old version of conda is still installed causing the conda activate command to not work properly.
To run these you'll need python and the following packages installed. :
- numpy
- scikit-learn
- h5py
- Pillow
- matplotlib
- tensorflow (v2 now includes keras)
I recommend installing packages using a virtual environment. On a Linux machine, pip should work for the above packages but if you have Anaconda installed, you can easily use the deep_learning_environment.yml file to make a deep_learning environment via the command:
conda create -f deep_learning_environment.yml.
You can install to a specific directory using: conda create --prefix ./envs -f deep_learning_environment.yml where ./envs is the directory you want to install to.
Note For Mac Users! - I recommend installing tensorflow via Anaconda rather than pip (also applies to R users). You might also need to also install the nomkl package to prevent a multithreading bug in numpy.