A sentiment analyzer using deep rnns, built with TensorFlow
Switch branches/tags
Nothing to show
Clone or download
Latest commit dbbada9 May 14, 2018
Failed to load latest commit information.
README.md readme updates Jul 26, 2017



A sentiment analyzer using deep rnns, built with TensorFlow.

Installation & Dependency Instructions

  1. Python 2 or 3
  2. Install [TensorFlow 1.2]https://www.tensorflow.org/install/)
  3. $ pip install nltk

Next you will need to download the nltk tokenizer dataset:

  1. run a python session in terminal $ python
  2. >>> import nltk
  3. >>> nltk.download()
  4. type 'd', then hit enter
  5. type 'punkt' then hit enter

You can exit the python terminal once the download is done, your dependencies will be all setup.

Usage Instructions

To run with preset hyper-parameters just run:

$ python train.py

To try other hyper-parameters, you can change them in the config file. If you want to provide your own:

$ python train.py --config_file="path_to_config"

Descripton of hyper parameters:

Name Type Description
hidden_size int number of hidden units in hidden layers
num_layers int number of hidden layers
batch_size int size of batchs in training
max_epoch int max number of epochs to train for
learning_rate float beggining learning rate
steps_per_checkpoint int number of steps before running test set
lr_decay_factor float factor by which to decay learning rate
batch_size int size of batchs in training
max_seq_length int maximum length of input token sequence
checkpoint_dir string directory to store/restore checkpoints
dropout float probability of hidden inputs being removed
grad_clip int max gradient norm

When you first run train.py, it will download the data set, unzip, and process it. This will take several minutes. The training will begin automatically after this is done.

After your model is trained you can run:

$ python sample.py --text="Your text to sample here"

It will return 0 or 1. This number corresponds to a positive (1), or a negative (0) score.


The model is: embedding layer -> LSTMCells -> logistic regression output layer. I'll provide a picture in the future.

The hidden state averaged across all time steps is what is passed to the logistic regression layer.

Tensorboard Usage

I've begun to implement tensorboard variables and histograms.

You can access tensorboard with this application by using:

$ tensorboard --logdir=/tmp/tb_logs/

Then copy and paste the localhost url your terminal window gives you, into your browser of choice.

There isn't much to see right now except an overview of the graph. I will include screenshots of this down the road as I add more tensorboard functionality to this project.


Pre-trained model will be committed soon. Currently with defaults, it gets about 70%. I would like to get it to about 80-85% before committing the model.


Thanks to reddit user /u/LeavesBreathe for the help with getting the last hidden state

The dataset used was the Large Movie Review Dataset.

I also referred to this tutorial as a starting point