ShaneTian / Textcnn
Licence: gpl-3.0
TextCNN by TensorFlow 2.0.0 ( tf.keras mainly ).
Stars: ✭ 37
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TextCNN
TextCNN by TensorFlow 2.0.0 ( tf.keras mainly ).
Software environments
- tensorflow-gpu 2.0.0-alpha0
- python 3.6.7
- pandas 0.24.2
- numpy 1.16.2
Data
- Vocabulary size: 3407
- Number of classes: 18
- Train/Test split: 20351/2261
Model architecture
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_data (InputLayer) [(None, 128)] 0
__________________________________________________________________________________________________
embedding (Embedding) (None, 128, 512) 1744384 input_data[0][0]
__________________________________________________________________________________________________
add_channel (Reshape) (None, 128, 512, 1) 0 embedding[0][0]
__________________________________________________________________________________________________
convolution_3 (Conv2D) (None, 126, 1, 128) 196736 add_channel[0][0]
__________________________________________________________________________________________________
convolution_4 (Conv2D) (None, 125, 1, 128) 262272 add_channel[0][0]
__________________________________________________________________________________________________
convolution_5 (Conv2D) (None, 124, 1, 128) 327808 add_channel[0][0]
__________________________________________________________________________________________________
max_pooling_3 (MaxPooling2D) (None, 1, 1, 128) 0 convolution_3[0][0]
__________________________________________________________________________________________________
max_pooling_4 (MaxPooling2D) (None, 1, 1, 128) 0 convolution_4[0][0]
__________________________________________________________________________________________________
max_pooling_5 (MaxPooling2D) (None, 1, 1, 128) 0 convolution_5[0][0]
__________________________________________________________________________________________________
concatenate (Concatenate) (None, 1, 1, 384) 0 max_pooling_3[0][0]
max_pooling_4[0][0]
max_pooling_5[0][0]
__________________________________________________________________________________________________
flatten (Flatten) (None, 384) 0 concatenate[0][0]
__________________________________________________________________________________________________
dropout (Dropout) (None, 384) 0 flatten[0][0]
__________________________________________________________________________________________________
dense (Dense) (None, 18) 6930 dropout[0][0]
==================================================================================================
Total params: 2,538,130
Trainable params: 2,538,130
Non-trainable params: 0
__________________________________________________________________________________________________
Model parameters
- Padding size: 128
- Embedding size: 512
- Num channel: 1
- Filter size: [3, 4, 5]
- Num filters: 128
- Dropout rate: 0.5
- Regularizers lambda: 0.01
- Batch size: 64
- Epochs: 10
- Fraction validation: 0.05 (1018 samples)
- Total parameters: 2,538,130
Run
Train result
Use 20351 samples after 10 epochs:
| Loss | Accuracy | Val loss | Val accuracy |
|---|---|---|---|
| 0.1609 | 0.9683 | 0.3648 | 0.9185 |
Test result
Use 2261 samples:
| Accuracy | Macro-Precision | Macro-Recall | Macro-F1 |
|---|---|---|---|
| 0.9363 | 0.9428 | 0.9310 | 0.9360 |
Images
Accuracy
Loss
Confusion matrix
Usage
usage: train.py [-h] [-t TEST_SAMPLE_PERCENTAGE] [-p PADDING_SIZE]
[-e EMBED_SIZE] [-f FILTER_SIZES] [-n NUM_FILTERS]
[-d DROPOUT_RATE] [-c NUM_CLASSES] [-l REGULARIZERS_LAMBDA]
[-b BATCH_SIZE] [--epochs EPOCHS]
[--fraction_validation FRACTION_VALIDATION]
[--results_dir RESULTS_DIR]
This is the TextCNN train project.
optional arguments:
-h, --help show this help message and exit
-t TEST_SAMPLE_PERCENTAGE, --test_sample_percentage TEST_SAMPLE_PERCENTAGE
The fraction of test data.(default=0.1)
-p PADDING_SIZE, --padding_size PADDING_SIZE
Padding size of sentences.(default=128)
-e EMBED_SIZE, --embed_size EMBED_SIZE
Word embedding size.(default=512)
-f FILTER_SIZES, --filter_sizes FILTER_SIZES
Convolution kernel sizes.(default=3,4,5)
-n NUM_FILTERS, --num_filters NUM_FILTERS
Number of each convolution kernel.(default=128)
-d DROPOUT_RATE, --dropout_rate DROPOUT_RATE
Dropout rate in softmax layer.(default=0.5)
-c NUM_CLASSES, --num_classes NUM_CLASSES
Number of target classes.(default=18)
-l REGULARIZERS_LAMBDA, --regularizers_lambda REGULARIZERS_LAMBDA
L2 regulation parameter.(default=0.01)
-b BATCH_SIZE, --batch_size BATCH_SIZE
Mini-Batch size.(default=64)
--epochs EPOCHS Number of epochs.(default=10)
--fraction_validation FRACTION_VALIDATION
The fraction of validation.(default=0.05)
--results_dir RESULTS_DIR
The results dir including log, model, vocabulary and
some images.(default=./results/)
usage: test.py [-h] [-p PADDING_SIZE] [-c NUM_CLASSES] results_dir
This is the TextCNN test project.
positional arguments:
results_dir The results dir including log, model, vocabulary and
some images.
optional arguments:
-h, --help show this help message and exit
-p PADDING_SIZE, --padding_size PADDING_SIZE
Padding size of sentences.(default=128)
-c NUM_CLASSES, --num_classes NUM_CLASSES
Number of target classes.(default=18)
You need to know...
- You need to alter
load_data_and_write_to_filefunction indata_helper.pyto match you data file; - This code used single channel input, you can use two channels from embedding vector, one is static and the other is dynamic. Maybe it is greater;
- The model is saved by
hdf5file; - Tensorboard is available.
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