This application is built by replicating work done in this paper here.
- Tensorflow
- Keras (Well, I wanted to avoid it entirely but had to use it for some data preprocessing :P)
- Matplotlib
- NumPy
usage: train.py [-h] [--src SRC] [--e_src E_SRC] [--ckpt_path CKPT_PATH]
[--s_len S_LEN] [--dim DIM] [--nb_words NB_WORDS]
[--model_type MODEL_TYPE] [-h_file H_FILE]
optional arguments:
-h, --help show this help message and exit
--src SRC Path to directory containing text file and label file
dataset. Default - './datasets/'
--e_src E_SRC File containing embedding vectors. Default -
'./embeddings/e_vectors.txt'
--ckpt_path CKPT_PATH
Path to directory where the checkpoint of the model
should be stored. If the directory doesn't exist, it
will be created. Default - './checkpoint/'
--s_len S_LEN Maximum length of the input sequence. Default - 64
--dim DIM Dimensions of the embedding vector space to be
utilized. Default - 100
--nb_words NB_WORDS Numbers of words to keep from the dataset.
--model_type MODEL_TYPE
Name of the model.Possible values - 'rand', 'static',
'non-static'Check CNN sentence classifier paper here
for details -> https://arxiv.org/abs/1408.5882.
Default - 'rand'
-h_file H_FILE In case of default model type a tunning_params.txt
file is required. The file should contain
hyperparameters in following order each on new line.
Learning Rate, Num Epochs, Mini-batch size, Device -
CPU/GPU. Default - 0.001, 10, 32, GPU.
