Text convolution-deconvolution auto-encoder and classification model in PyTorch.
PyTorch implementation of Deconvolutional Paragraph Representation Learning described in NIPS 2017.
This repository is still developing.
- Python 3
- PyTorch >= 0.3
- numpy
Download data. Hotel reviews
Then, run following command.
$ python main_reconstruction.py -data_path=path/to/hotel_reviews.p Specify download data path by -data_path.
About other parameters.
usage: main_reconstruction.py [-h] [-lr LR] [-epochs EPOCHS]
[-batch_size BATCH_SIZE]
[-lr_decay_interval LR_DECAY_INTERVAL]
[-log_interval LOG_INTERVAL]
[-test_interval TEST_INTERVAL]
[-save_interval SAVE_INTERVAL]
[-save_dir SAVE_DIR] [-data_path DATA_PATH]
[-shuffle SHUFFLE] [-sentence_len SENTENCE_LEN]
[-embed_dim EMBED_DIM]
[-kernel_sizes KERNEL_SIZES] [-tau TAU]
[-use_cuda] [-enc_snapshot ENC_SNAPSHOT]
[-dec_snapshot DEC_SNAPSHOT]
text convolution-deconvolution auto-encoder model
optional arguments:
-h, --help show this help message and exit
-lr LR initial learning rate
-epochs EPOCHS number of epochs for train
-batch_size BATCH_SIZE
batch size for training
-lr_decay_interval LR_DECAY_INTERVAL
how many epochs to wait before decrease learning rate
-log_interval LOG_INTERVAL
how many steps to wait before logging training status
-test_interval TEST_INTERVAL
how many epochs to wait before testing
-save_interval SAVE_INTERVAL
how many epochs to wait before saving
-save_dir SAVE_DIR where to save the snapshot
-data_path DATA_PATH data path
-shuffle SHUFFLE shuffle data every epoch
-sentence_len SENTENCE_LEN
how many tokens in a sentence
-embed_dim EMBED_DIM number of embedding dimension
-kernel_sizes KERNEL_SIZES
kernel size to use for convolution
-tau TAU temperature parameter
-use_cuda whether using cuda
-enc_snapshot ENC_SNAPSHOT
filename of encoder snapshot
-dec_snapshot DEC_SNAPSHOT
filename of decoder snapshot
Run follow command.
$ python main.py -data_path=path/to/trainingdata -label_path=path/to/labeldataSpecify training data and label data by -data_path and -label_data arguments.
Both data must have same lines and training data must be separated by blank.
About other parameters.
usage: main_classification.py [-h] [-lr LR] [-epochs EPOCHS]
[-batch_size BATCH_SIZE]
[-lr_decay_interval LR_DECAY_INTERVAL]
[-log_interval LOG_INTERVAL]
[-test_interval TEST_INTERVAL]
[-save_interval SAVE_INTERVAL]
[-save_dir SAVE_DIR] [-data_path DATA_PATH]
[-label_path LABEL_PATH] [-separated SEPARATED]
[-shuffle SHUFFLE] [-sentence_len SENTENCE_LEN]
[-mlp_out MLP_OUT] [-dropout DROPOUT]
[-embed_dim EMBED_DIM]
[-kernel_sizes KERNEL_SIZES] [-tau TAU]
[-use_cuda] [-enc_snapshot ENC_SNAPSHOT]
[-dec_snapshot DEC_SNAPSHOT]
[-mlp_snapshot MLP_SNAPSHOT]
text convolution-deconvolution auto-encoder model
optional arguments:
-h, --help show this help message and exit
-lr LR initial learning rate
-epochs EPOCHS number of epochs for train
-batch_size BATCH_SIZE
batch size for training
-lr_decay_interval LR_DECAY_INTERVAL
how many epochs to wait before decrease learning rate
-log_interval LOG_INTERVAL
how many steps to wait before logging training status
-test_interval TEST_INTERVAL
how many steps to wait before testing
-save_interval SAVE_INTERVAL
how many epochs to wait before saving
-save_dir SAVE_DIR where to save the snapshot
-data_path DATA_PATH data path
-label_path LABEL_PATH
label path
-separated SEPARATED how separated text data is
-shuffle SHUFFLE shuffle the data every epoch
-sentence_len SENTENCE_LEN
how many tokens in a sentence
-mlp_out MLP_OUT number of classes
-dropout DROPOUT the probability for dropout
-embed_dim EMBED_DIM number of embedding dimension
-kernel_sizes KERNEL_SIZES
kernel size to use for convolution
-tau TAU temperature parameter
-use_cuda whether using cuda
-enc_snapshot ENC_SNAPSHOT
filename of encoder snapshot
-dec_snapshot DEC_SNAPSHOT
filename of decoder snapshot
-mlp_snapshot MLP_SNAPSHOT
filename of mlp classifier snapshot
Deconvolutional Paragraph Representation Learning
Yizhe Zhang, Dinghan Shen, Guoyin Wang, Zhe Gan, Ricardo Henao, Lawrence Carin
arXiv:1708.04729 [cs.CL]
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