Deep-Attention Text Classifier in Tensorflow

This repository contains code for text classification using attention mechanism in Tensorflow with tensorboard visualization.

Requirements

Python 3.6
Tensorflow 1.2.1
Numpy

Project Module

utility_dir: storage module for data, vocab files, saved models, tensorboard logs, outputs.
pre_processing_module: code for pre-processing text file which includes sampling infrequent words, creation of training vocab and classes in form of pickle dictionary.
implementation_module: code for model architecture, data reader, training pipeline and test pipeline.
settings_module: code to set directory paths (data path, vocab path, model path etc.), set model parameters (hidden dim, attention dim, regularization, dropout etc.), set vocab dictionary.
run_module: wrapper code to execute end-to-end train and test pipeline.
viz_module: code to generate embedding visualization via tensorboard.
utility_code: other utility codes

How to run

train: python -m global_module.run_module.run_train
test: python -m global_module.run_module.run_test
visualize tensorboard: tensorboard --logdir=PATH-TO-LOG-DIR

Data Sample

Utterance file
- it is hard to resist
- But something seems to be missing .
- A movie of technical skill and rare depth of intellect and feeling .
- Brosnan is more feral in this film than I 've seen him before
- . . .
Utterance label
- neg
- neg
- pos
- neg
- . . .

How to change model parameters

Go to set_params.py here.

Model Graph

Loss and Accuracy Plots

#Histogram

Embedding Visualization