def regression_evaluate(y_test, predict, msg='None'): # msg: Valence or Arousal return continuous_metrics(y_test, predict, 'prediction result:')
print('Predict value: %s' % str(predict)) submit_predict = model.predict(X_test, batch_size=batch_size).reshape( (1, len(X_test)))[0] pickle.dump((test, submit_predict), open("./tmp/submit_cnn_lstm.p", 'wb')) exit() print("Saving model and weights...") json_string = model.to_json() open('./tmp/keras_model_architecture.json', 'w').write(json_string) model.save_weights('./tmp/keras_model_weights.h5', overwrite=True) print("Model saved.") from metrics import continuous_metrics continuous_metrics(Y_test, predict, 'prediction result:') # visualization from visualize import draw_linear_regression X = range(50, 100) # or range(len(y_test)) draw_linear_regression(X, np.array(Y_test)[X], np.array(predict)[X], 'Sentence Number', "Sentiment scores", 'Comparison of predicted and true scores') from visualize import plot_keras, draw_hist plot_keras(result, x_labels='Epoch', y_labels='MAE Loss') draw_hist(np.array(Y_test) - np.array(predict),
# experiment evaluated by multiple metrics predict = model.predict(X_test, batch_size=batch_size).reshape((1, len(X_test)))[0] print('Y_test: %s' % str(Y_test)) print('Predict value: %s' % str(predict)) submit_predict = model.predict(X_test, batch_size=batch_size).reshape((1, len(X_test)))[0] pickle.dump((test, submit_predict), open("./tmp/submit_cnn_lstm.p", 'wb')) exit() print("Saving model and weights...") json_string = model.to_json() open('./tmp/keras_model_architecture.json', 'w').write(json_string) model.save_weights('./tmp/keras_model_weights.h5', overwrite=True) print("Model saved.") from metrics import continuous_metrics continuous_metrics(Y_test, predict, 'prediction result:') # visualization from visualize import draw_linear_regression X = range(50, 100) # or range(len(y_test)) draw_linear_regression(X, np.array(Y_test)[X], np.array(predict)[X], 'Sentence Number', "Sentiment scores", 'Comparison of predicted and true scores') from visualize import plot_keras, draw_hist plot_keras(result, x_labels='Epoch', y_labels='MAE Loss') draw_hist(np.array(Y_test) - np.array(predict), title='Histogram of sentiment scores prediction: ')