- Train, Validate & Test a Convolutional Neural Network (CNN) based model to adapt to a text-classfication task
- Supports adding multiple classes dynamically
- Supports adding pretrained word2vec embeddings from Google News Dataset. Also, learns new word2vec embeddings from the training datasets
- Exposes a REST Webservice & an API to make new predictions (TODO)
- Containerizable docker image for easy deployment of the prediction service on any docker enabled server (TODO)
- Reusable Vagrant Base Box image file for out of the box deployment using Vagrant (TODO)
- Runs parallely on a cluster in Distributed Tensorflow Mode (TODO)
- Python 3
- Tensorflow > 0.8
- Numpy
Print parameters:
python train.py --helpoptional arguments:
-h, --help show this help message and exit
--embedding_dim EMBEDDING_DIM
Dimensionality of character embedding (default: 128)
--filter_sizes FILTER_SIZES
Comma-separated filter sizes (default: '3,4,5')
--num_filters NUM_FILTERS
Number of filters per filter size (default: 128)
--l2_reg_lambda L2_REG_LAMBDA
L2 regularizaion lambda (default: 0.0)
--dropout_keep_prob DROPOUT_KEEP_PROB
Dropout keep probability (default: 0.5)
--batch_size BATCH_SIZE
Batch Size (default: 64)
--num_epochs NUM_EPOCHS
Number of training epochs (default: 100)
--evaluate_every EVALUATE_EVERY
Evaluate model on dev set after this many steps
(default: 100)
--checkpoint_every CHECKPOINT_EVERY
Save model after this many steps (default: 100)
--allow_soft_placement ALLOW_SOFT_PLACEMENT
Allow device soft device placement
--noallow_soft_placement
--log_device_placement LOG_DEVICE_PLACEMENT
Log placement of ops on devices
--nolog_device_placement
The training step uses pre-trained word2vec word embeddings. New word embeddings are also learnt from the training data in addition to the pre-trained vectors. The pre-trained Google news word embeddings can be downloaded from here. This repository hosts the word2vec pre-trained Google News corpus (3 billion running words) word vector model (3 million 300-dimension English word vectors)
python train.py --embedding_dim=300 --word2vec=./word2vec/GoogleNews-vectors-negative300.binpython eval.py --eval_train --checkpoint_dir="./runs/1459637919/checkpoints/"tensorboard --logdir ./runs/1459637919/summaries/Replace the checkpoint dir with the output from the training. To use your own data, change the eval.py script to load your data.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent