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cnn-text-classification-tf
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# cnn-text-classification-tf The base project is **[dennybritz/cnn-text-classification-tf](https://github.com/dennybritz/cnn-text-classification-tf)**. It can classify the positive and negative movie reviews. I modified it to have 5 labels(0,1,2,3,4). It is slightly simplified implementation of Kim's [Convolutional Neural Networks for Sentence Classification](http://arxiv.org/abs/1408.5882) paper in Tensorflow. ## Requirements - Python 3 - Tensorflow > 0.12 - Numpy ## Install tensorflow Please see the [install guide](https://www.tensorflow.org/install/) in tensorflow website. ## Training Print parameters: ```bash ./train.py --help ``` ``` optional arguments: -h, --help show this help message and exit --data_file DATA_FILE File for training or evaluation (default: /data/train.txt for training, /data/dev.txt for evaluation) --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 ``` Train: ```bash ./train.py ``` Train, including the Kaggle dataset: ```bash ./train.py --data_file ./data/train-all.txt ``` ## Evaluating ```bash ./eval.py --checkpoint_dir="./runs/1459637919/checkpoints/" ``` Replace the checkpoint dir with the output from the training. To use your own data, change the `eval.py` script to load your data. ## References - [Convolutional Neural Networks for Sentence Classification](http://arxiv.org/abs/1408.5882) - [A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification](http://arxiv.org/abs/1510.03820)
