keras-sentiment-analysis-web-api：基于烧瓶的Web API，用于使用Word Embedding...

# keras-sentiment-analysis-web-api Web api built on flask for keras-based sentiment analysis using Word Embedding, RNN and CNN The implementation of the classifiers can be found in [keras_sentiment_analysis/library](keras_sentiment_analysis/library) * [cnn.py](keras_sentiment_analysis/library/cnn.py) * 1-D CNN with Word Embedding * Multi-Channel CNN with categorical cross-entropy loss function * [cnn_lstm.py](keras_sentiment_analysis/library/cnn_lstm.py) * 1-D CNN + LSTM with Word Embedding * [ffn.py](keras_sentiment_analysis/library/ffn.py) * Feedforward network with Glove Word Embedding * [lstm.py](keras_sentiment_analysis/library/lstm.py) * LSTM with binary or category cross-entropy loss function * Bi-directional LSTM/GRU with categorical cross-entropy loss function # Usage Run the following command to install the keras, flask and other dependency modules: ```bash sudo pip install -r requirements.txt ``` ## Training (Optional) As the trained models are already included in the [demo/models](demo/models) folder in the project, the training is not required. However, if you like to tune the parameters and retrain the models, you can use the following command to run the training: ```bash cd demo python wordvec_bidirectional_lstm_softmax_train.py ``` Below shows the sample code in [wordvec_bidirectional_lstm_softmax_train.py](demo/wordvec_bidirectional_lstm_softmax_train.py): ```python import numpy as np import os import sys def main(): random_state = 42 np.random.seed(random_state) current_dir = os.path.dirname(__file__) # this line ask sys to add the keras_sentiment_analysis module to system path sys.path.append(os.path.join(current_dir, '..')) current_dir = current_dir if current_dir is not '' else '.' output_dir_path = current_dir + '/models' data_file_path = current_dir + '/data/umich-sentiment-train.txt' from keras_sentiment_analysis.library.lstm import WordVecBidirectionalLstmSoftmax from keras_sentiment_analysis.library.utility.simple_data_loader import load_text_label_pairs from keras_sentiment_analysis.library.utility.text_fit import fit_text text_data_model = fit_text(data_file_path) text_label_pairs = load_text_label_pairs(data_file_path) classifier = WordVecBidirectionalLstmSoftmax() batch_size = 64 epochs = 20 history = classifier.fit(text_data_model=text_data_model, model_dir_path=output_dir_path, text_label_pairs=text_label_pairs, batch_size=batch_size, epochs=epochs, test_size=0.3, random_state=random_state) if __name__ == '__main__': main() ``` The above commands will train bidrectional lstm model with softmax activation on the "demo/data/umich-sentiment-train.txt" dataset and store the trained model in [demo/models/bidirectional_lstm_softmax_**](demo/models) If you like to train other models, you can use the same command above on another train python scripts: * [wordvec_cnn_lstm_train.py](demo/wordvec_cnn_lstm_train.py): cnn + lstm model with softmax and categorical crossentropy objective * [wordvec_lstm_softmax_train.py](demo/wordvec_lstm_softmax_train.py): lstm model with softmax and categorical crossentropy objective * [wordvec_lstm_sigmoid_train.py](demo/wordvec_lstm_sigmoid_train.py): lstm model with sigmoid and binary crossentropy objective * [wordvec_cnn_train.py](demo/wordvec_cnn_train.py): cnn model with softmax and categorical crossentropy objective * [wordvec_multi_channel_cnn_train.py](demo/wordvec_multi_channel_cnn_train.py): multi-channel cnn model with softmax and categorical crossentropy objective * [wordvec_glove_ffn_train.py](demo/wordvec_glove_ffn_train.py): glove word embedding layer with feed forward network model and categorical crossentropy objective The figure below compare the training accuracy and validation accuracy of various models using the script [wordvec_compare_models.py](demo/wordvec_compare_models.py):  ## Predict Sentiments With the trained models in [demo/models](demo/models), one can test the performance by running the predictors via the following command: ```bash cd demo python wordvec_bidirectional_lstm_softmax_predict.py ``` Below shows the sample code in [wordvec_bidirectional_lstm_softmax_predict.py](demo/wordvec_bidirectional_lstm_softmax_predict.py): ```python from random import shuffle import numpy as np import os import sys def main(): random_state = 42 np.random.seed(random_state) current_dir = os.path.dirname(__file__) # this line ask sys to add the keras_sentiment_analysis module to system path sys.path.append(os.path.join(current_dir, '..')) current_dir = current_dir if current_dir is not '' else '.' model_dir_path = current_dir + '/models' data_file_path = current_dir + '/data/umich-sentiment-train.txt' from keras_sentiment_analysis.library.lstm import WordVecBidirectionalLstmSoftmax from keras_sentiment_analysis.library.utility.simple_data_loader import load_text_label_pairs text_label_pairs = load_text_label_pairs(data_file_path) classifier = WordVecBidirectionalLstmSoftmax() classifier.load_model(model_dir_path=model_dir_path) shuffle(text_label_pairs) for i in range(20): text, label = text_label_pairs[i] print('Output: ', classifier.predict(sentence=text)) predicted_label = classifier.predict_class(text) print('Sentence: ', text) print('Predicted: ', predicted_label, 'Actual: ', label) if __name__ == '__main__': main() ``` Below is the console print out from running the prediction scripts above: ``` Output: [ 5.74236214e-09 1.00000000e+00] Sentence: by the way, the Da Vinci Code sucked, just letting you know... Predicted: 0 Actual: 0 Output: [ 2.50778981e-10 1.00000000e+00] Sentence: , she helped me bobbypin my insanely cool hat to my head, and she laughed at my stupid brokeback mountain cowboy jokes.. Predicted: 0 Actual: 0 Output: [ 1.00000000e+00 2.23617502e-09] Sentence: I love Harry Potter.. Predicted: 1 Actual: 1 Output: [ 5.44211538e-08 1.00000000e+00] Sentence: Oh, and Brokeback Mountain is a TERRIBLE movie... Predicted: 0 Actual: 0 Output: [ 1.00000000e+00 2.92380893e-08] Sentence: I love The Da Vinci Code... Predicted: 1 Actual: 1 Output: [ 1.00000000e+00 2.23617502e-09] Sentence: I love Harry Potter.. Predicted: 1 Actual: 1 Output: [ 1.53129953e-08 1.00000000e+00] Sentence: Is it just me, or does Harry Potter suck?... Predicted: 0 Actual: 0 Output: [ 1.00000000e+00 1.66275674e-10] Sentence: I am going to start reading the Harry Potter series again because that is one awesome story. Predicted: 1 Actual: 1 Output: [ 2.50778981e-10 1.00000000e+00] Sentence: , she helped me bobbypin my insanely cool hat to my head, and she laughed at my stupid brokeback mountain cowboy jokes.. Predicted: 0 Actual: 0 Output: [ 1.00000000e+00 1.62028765e-10] Sentence: So as felicia's mom is cleaning the table, felicia grabs my keys and we dash out like freakin mission impossible. Predicted: 1 Actual: 1 Output: [ 1.12964793e-08 1.00000000e+00] Sentence: friday hung out with kelsie and we went and saw The Da Vinci Code SUCKED!!!!! Predicted: 0 Actual: 0 Output: [ 1.00000000e+00 1.53758450e-10] Sentence: I want to be here because I love Harry Potter, and I really want a place where people take it serious, but it is still so much fun. Predicted: 1 Actual: 1 Output: [ 6.24366930e-06 9.99993801e-01] Sentence: Mission Impossible III-Sucks big-time!.. Predicted: 0 Actual: 0 Output: [ 1.00000000e+00 1.35581537e-08] Sentence: I, too, like Harry Potter.. Predicted: 1 Actual: 1 Output: [ 1.00000000e+00 9.90327020e-09] Sentence: