Adversarial Text Classification

Code for Adversarial Training Methods for Semi-Supervised Text Classification and Semi-Supervised Sequence Learning.

Source Paper: Semi-Supervised Sequence Learning

Source Repository: tensorflow/models/research/adversarial_text

Requirements

TensorFlow = v1.15.5
Current VM configuration, Current configuration: AWS Ubuntu server 18.04 LTS 64 bit (x86), t2.micro, 30GB SSD.

End-to-end IMDB Sentiment Classification Replication

Fetch data

$ wget http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz \
    -O /tmp/imdb.tar.gz
$ tar -xf /tmp/imdb.tar.gz -C /tmp

The directory /tmp/aclImdb contains the raw IMDB data.

Generate vocabulary

$ IMDB_DATA_DIR=/tmp/imdb

Assigning the folder imdb in tmp to IMDB_DATA_DIR

$ python gen_vocab.py \
    --output_dir=$IMDB_DATA_DIR \
    --dataset=imdb \
    --imdb_input_dir=/tmp/aclImdb \
    --lowercase=False

Head to the models/research/adversarial_text in the cloned repository. Execute the above command. Vocabulary and frequency files will be generated in $IMDB_DATA_DIR. the input files will be taken from /tmp/aclImdb

Scaling down the file

Head to the tmp/aclimdb filtering the 10% of data for further processing as alternative solution against the memory allocation issue.

mv neg neg_original
mv pos pos_original
#create a new repository
$mkdir neg
$mkdir pos
mv neg_orginal/9*.txt neg #filtering ~2k files from 25k 
mv pos_orginal/9*.txt pos
#The same need to be performed on the train data, test data , unsup files (unlablled data)

Generate training, validation, and test data

$ python gen_data.py \
    --output_dir=$IMDB_DATA_DIR \
    --dataset=imdb \
    --imdb_input_dir=/tmp/aclImdb \
    --lowercase=False \
    --label_gain=False

$IMDB_DATA_DIR contains TFRecords files.

Pretrain IMDB Language Model

$ PRETRAIN_DIR=/tmp/models/imdb_pretrain
$ python pretrain.py \
    --train_dir=$PRETRAIN_DIR \
    --data_dir=$IMDB_DATA_DIR \
    --vocab_size=87007 \
    --embedding_dims=256 \
    --rnn_cell_size=64 \
    --num_candidate_samples=64 \
    --batch_size=1 \
    --learning_rate=0.001 \
    --learning_rate_decay_factor=0.9999 \
    --max_steps=100000 \
    --max_grad_norm=1.0 \
    --num_timesteps=400 \
    --keep_prob_emb=0.5 \
    --normalize_embeddings

$PRETRAIN_DIR contains checkpoints of the pretrained language model.

Train classifier

$ TRAIN_DIR=/tmp/models/imdb_classify
$ python train_classifier.py \
    --train_dir=$TRAIN_DIR \
    --pretrained_model_dir=$PRETRAIN_DIR \
    --data_dir=$IMDB_DATA_DIR \
    --vocab_size=87007 \
    --embedding_dims=256 \
    --rnn_cell_size=1024 \
    --cl_num_layers=1 \
    --cl_hidden_size=30 \
    --batch_size=64 \
    --learning_rate=0.0005 \
    --learning_rate_decay_factor=0.9998 \
    --max_steps=15000 \
    --max_grad_norm=1.0 \
    --num_timesteps=400 \
    --keep_prob_emb=0.5 \
    --normalize_embeddings \
    --adv_training_method=vat \
    --perturb_norm_length=5.0

Evaluate on test data

$ EVAL_DIR=/tmp/models/imdb_eval
$ python evaluate.py \
    --eval_dir=$EVAL_DIR \
    --checkpoint_dir=$TRAIN_DIR \
    --eval_data=test \
    --run_once \
    --num_examples=25000 \
    --data_dir=$IMDB_DATA_DIR \
    --vocab_size=87007 \
    --embedding_dims=256 \
    --rnn_cell_size=1024 \
    --batch_size=256 \
    --num_timesteps=400 \
    --normalize_embeddings

Head to cd /tmp/models/imdb_pretrain, You get a graph.pbtxt file on the file.

Copy the folders to the main directory

cp /tmp/models/imdb_pretrain/* ~/imdb_pretrain

Data Generation

Vocabulary generation: gen_vocab.py
Data generation: gen_data.py

Command-line flags defined in document_generators.py control which dataset is processed and how.

New Data Generation (IMDB, AMAZON)

IMDB Web_Scrapping: imdb_scrapping.ipynb
Amazon new data generation: amazon_scrapping.py
SVM on Amazon listing: amazon_listing.ipynb

Replicated by

@sanjay-kv, @mona-piya, @Mohammed-Rizwan-Amanullah, @Sabihabit
Mail to: sanjay, Mona, Rizwan
Takeru Miyato, @takerum (Original implementation), Andrew M.Dai adai@google.com (Tensorflow/Models)

sanjay-kv / Semi-supervised-sequence-learning-Project