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LICENSE (1068, 2019-02-01)
data (0, 2019-02-01)
data\test.csv (1857427, 2019-02-01)
data\train.csv (29470338, 2019-02-01)
src (0, 2019-02-01)
src\utils.py (1615, 2019-02-01)
src\very_deep_cnn.py (5426, 2019-02-01)
tensorboard (0, 2019-02-01)
tensorboard\vdcnn (0, 2019-02-01)
tensorboard\vdcnn\test (0, 2019-02-01)
tensorboard\vdcnn\test\events.out.tfevents.1547166611.viet-System-Product-Name (828, 2019-02-01)
tensorboard\vdcnn\train (0, 2019-02-01)
tensorboard\vdcnn\train\events.out.tfevents.1547166272.viet-System-Product-Name (16004427, 2019-02-01)
train.py (9033, 2019-02-01)
trained_models (0, 2019-02-01)
trained_models\logs.txt (2213, 2019-02-01)
visualization (0, 2019-02-01)
visualization\ag_news_depth_17.png (94286, 2019-02-01)
visualization\ag_news_depth_29.png (88420, 2019-02-01)
visualization\ag_news_depth_9.png (98186, 2019-02-01)
visualization\all.png (4328333, 2019-02-01)
visualization\amazon_review_full_depth_17.png (93386, 2019-02-01)
visualization\amazon_review_full_depth_29.png (95236, 2019-02-01)
visualization\amazon_review_full_depth_9.png (100577, 2019-02-01)
visualization\amazon_review_polarity_depth_17.png (97030, 2019-02-01)
visualization\amazon_review_polarity_depth_29.png (90918, 2019-02-01)
visualization\amazon_review_polarity_depth_9.png (98284, 2019-02-01)
visualization\dbpedia_depth_17.png (90408, 2019-02-01)
visualization\dbpedia_depth_29.png (96786, 2019-02-01)
visualization\dbpedia_depth_9.png (89699, 2019-02-01)
visualization\sogou_news_depth_17.png (99579, 2019-02-01)
visualization\sogou_news_depth_29.png (104265, 2019-02-01)
visualization\sogou_news_depth_9.png (93425, 2019-02-01)
visualization\yelp_review_full_depth_17.png (97309, 2019-02-01)
visualization\yelp_review_full_depth_29.png (94169, 2019-02-01)
visualization\yelp_review_full_depth_9.png (100682, 2019-02-01)
visualization\yelp_review_polarity_depth_17.png (97504, 2019-02-01)
visualization\yelp_review_polarity_depth_29.png (96504, 2019-02-01)
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# [TENSORFLOW] Very deep convolutional networks for Text Classification ## Introduction Here is my tensorflow implementation of the model described in the paper **Very deep convolutional networks for Text Classification** [paper](https://arxiv.org/abs/1606.01781). ## Datasets: Statistics of datasets I used for experiments. These datasets could be download from [link](https://drive.google.com/drive/u/0/folders/0Bz8a_Dbh9Qhbfll6bVpmNUtUcFdjYmF2SEpmZUZUcVNiMUw1TWN6RDV3a0JHT3kxLVhVR2M) | Dataset | Classes | Train samples | Test samples | |------------------------|:---------:|:---------------:|:--------------:| | AGs News | 4 | 120 000 | 7 600 | | Sogou News | 5 | 450 000 | 60 000 | | DBPedia | 14 | 560 000 | 70 000 | | Yelp Review Polarity | 2 | 560 000 | 38 000 | | Yelp Review Full | 5 | 650 000 | 50 000 | | Yahoo! Answers | 10 | 1 400 000 | 60 000 | | Amazon Review Full | 5 | 3 000 000 | 650 000 | | Amazon Review Polarity | 2 | 3 600 000 | 400 000 | ## Setting: I almost keep default setting as described in the paper. Additionally, in the original model, one epoch is seen as a loop over batch_size x num_batch records (128x5000 or 128x10000 or 128x30000), so it means that there are records used more than once for 1 epoch. In my model, 1 epoch is a complete loop over the whole dataset, where each record is used exactly once. ## Training After putting 2 files **train.csv** and **test.csv** from any dataset mentioned above into **data** folder, there are several options for training: If you want to train a model with common dataset and default parameters, you could run: - **python train.py** If you want to train a model with your preference parameters, like depth and learning rate, you could run: - **python train.py --depth 29 --lr learning_rate**: For example, python train.py --depth 29 --lr 0.001 If you want to train a model with your own dataset, you need to specify the paths to input (train and test files) and output folder: - **python train.py --train_set path/to/training/file --test_set path/to/test/file --saved_path path/to/output/folder** You could find all trained models I have trained in [link](https://drive.google.com/open?id=1DF9WXR5Et0aV3p725vEyxTTNUAqSdn0p) ## Experiments: I run experiments in 2 machines, one with NVIDIA TITAN X 12gb GPU and the other with NVIDIA quadro 6000 24gb GPU. Results for test set are presented as follows: A(B): - **A** is accuracy reproduced here. - **B** is accuracy reported in the paper. It should be noted that in experiments with depth is 49 layers, there is no accuracy reported in the paper. Therefore here I only show the results obtained from my experiments. | Depth | 9 | 17 | 29 | 49 | |:---------------:|:--------------:|:--------------:|:--------------:|:--------------:| | ag_news | 87.67(90.17) | 88.09(90.61) | 88.01(91.33) | 84.71 | | sogu_news | 95.67(9***2) | 95.89(9***9) | 95.73(96.82) | 95.35 | | db_pedia | ***.33(***.44) | ***.28(***.39) | ***.07(***.59) | 97.38 | | yelp_polarity | 94.57(94.73) | 95.20(94.95) | 94.95(95.37) | 95.08 | | yelp_review | 62.44(61.96) | 63.44(62.59) | 62.70(63.00) | 62.83 | | yahoo_answer | 69.57(71.76) | 70.03(71.75) | 70.34(72.84) | 69.16 | | amazon_review | 60.34(60.81) | 60.***(61.19) | 60.67(61.61) | 59.80 | |amazon_polarity| 94.30(94.31) | 94.60(94.57) | 94.53(95.06) | 94.10 | Below are the training/test loss/accuracy curves for each dataset's experiments (figures for 9, 17, 29-layer model are from left to right) : - **ag_news** - **sogou_news** - **db_pedia** - **yelp_polarity** - **yelp_review** - **amazon_review** - **amazon_polarity** You could find detail log of each experiment containing loss, accuracy and confusion matrix at the end of each epoch in **trained_models/logs.txt**.

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