文件列表:

cnn-text-classification-tf (0, 2017-09-28)
cnn-text-classification-tf\train.py (8209, 2017-09-28)
cnn-text-classification-tf\text_cnn.py (3776, 2017-09-28)
cnn-text-classification-tf\eval.py (3738, 2017-09-28)
cnn-text-classification-tf\data_helpers.py (2485, 2017-09-28)
cnn-text-classification-tf\LICENSE (11357, 2017-09-28)
cnn-text-classification-tf\data (0, 2017-09-28)
cnn-text-classification-tf\data\rt-polaritydata (0, 2017-09-28)
cnn-text-classification-tf\data\rt-polaritydata\rt-polarity.pos (626395, 2017-09-28)
cnn-text-classification-tf\data\rt-polaritydata\rt-polarity.neg (612506, 2017-09-28)

**[This code belongs to the "Implementing a CNN for Text Classification in Tensorflow" blog post.](http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/)** 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 ## Training Print parameters: ```bash ./train.py --help ``` ``` optional 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: ***) --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 ``` ## Evaluating ```bash ./eval.py --eval_train --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)

近期下载者：

相关文件：

评论：[我要评论] [举报此文件]

收藏者：