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Text classification using different neural networks (CNN, LSTM, Bi-LSTM, C-LSTM).

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# 334,482
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Multi-class Text Classification

Implement four neural networks in Tensorflow for multi-class text classification problem.


  • A LSTM classifier. See
  • A Bidirectional LSTM classifier. See
  • A CNN classifier. See Reference: Implementing a CNN for Text Classification in Tensorflow.
  • A C-LSTM classifier. See Reference: A C-LSTM Neural Network for Text Classification. ## Requirements
  • Python 3.x
  • Tensorflow > 1.5
  • Sklearn > 0.19.0
    ## Data Format Training data should be stored in csv file. The first line of the file should be ["label", "content"] or ["content", "label"]. ## Train Run to train the models. Parameters:
    optional arguments:
    --clf CLF             Type of classifiers. Default: cnn. You have four
                        choices: [cnn, lstm, blstm, clstm]
    --data_file DATA_FILE
                        Data file path
    --stop_word_file STOP_WORD_FILE
                        Stop word file path
    --language LANGUAGE   Language of the data file. You have two choices: [ch,
    --min_frequency MIN_FREQUENCY
                        Minimal word frequency
    --num_classes NUM_CLASSES
                        Number of classes
    --max_length MAX_LENGTH
                        Max document length
    --vocab_size VOCAB_SIZE
                        Vocabulary size
    --test_size TEST_SIZE
                        Cross validation test size
    --embedding_size EMBEDDING_SIZE
                        Word embedding size. For CNN, C-LSTM.
    --filter_sizes FILTER_SIZES
                        CNN filter sizes. For CNN, C-LSTM.
    --num_filters NUM_FILTERS
                        Number of filters per filter size. For CNN, C-LSTM.
    --hidden_size HIDDEN_SIZE
                        Number of hidden units in the LSTM cell. For LSTM, Bi-
    --num_layers NUM_LAYERS
                        Number of the LSTM cells. For LSTM, Bi-LSTM, C-LSTM
    --keep_prob KEEP_PROB
                        Dropout keep probability
    --learning_rate LEARNING_RATE
                        Learning rate
    --l2_reg_lambda L2_REG_LAMBDA
                        L2 regularization lambda
    --batch_size BATCH_SIZE
                        Batch size
    --num_epochs NUM_EPOCHS
                        Number of epochs
    --decay_rate DECAY_RATE
                        Learning rate decay rate. Range: (0, 1]
    --decay_steps DECAY_STEPS
                        Learning rate decay steps.
    --evaluate_every_steps EVALUATE_EVERY_STEPS
                        Evaluate the model on validation set after this many
    --save_every_steps SAVE_EVERY_STEPS
                        Save the model after this many steps
    --num_checkpoint NUM_CHECKPOINT
                        Number of models to store
    You could run to start training. For example:
    python --data_file=./data/data.csv --clf=lstm

After the training is done, you can use tensorboard to see the visualizations of the graph, losses and evaluation metrics:

tensorboard --logdir=./runs/1111111111/summaries


Run to evaluate the trained model

optional arguments:
  --test_data_file TEST_DATA_FILE
                        Test data file path
  --run_dir RUN_DIR     Restore the model from this run
  --checkpoint CHECKPOINT
                        Restore the graph from this checkpoint
  --batch_size BATCH_SIZE
                        Test batch size
You could run to start evaluation. For example:
python --test_data_file=./data/data.csv --run_dir=./runs/1111111111 --checkpoint=clf-10000

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