The repository provides an implementation of Recurrent Neural Network based on tutorial http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-2-implementing-a-language-model-rnn-with-python-numpy-and-theano/.
Relevant files are:
- RNN.py
- RNN_impl.py
- rnn_utils.py
The code has been written in the form of the package that can be used with import
command.
Since I am using Ubuntu 18.04, I will install appropriate version of CUDA for my Ubuntu 18.04 system. Depending on your system, you can choose write CUDA package for your system from http://developer.download.nvidia.com/compute/cuda/repos/
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.1.168-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1804_10.1.168-1_amd64.deb
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
sudo apt-get update
sudo apt-get install -y cuda
At this point, you will need to restart your computer. Then set the environment variable by including them in your .bashrc
file.
# Set Environment variables
export CUDA_ROOT=/usr/local/cuda-10.1
export PATH=$PATH:$CUDA_ROOT/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_ROOT/lib64
export THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32
# For profiling only
export CUDA_LAUNCH_BLOCKING=1
An example code is given to predict crypto currency exchange rate using LSTM.
Relevant file(s): crypt_lstm.py
A Juypter Notebook PyTorch_1.ipynb
follows the tutorial from YouTube tutorial Applied Deep Learning with PyTorch to demonstrate the use of PyTorch to implement Seq2Seq Model.