[Simple keras BiLSTM Attention Model Pipeline] #python #keras

BiLSTM_Attention_Keras.py

from keras import Sequential
from keras.preprocessing.sequence import pad_sequences
from sklearn.model_selection import train_test_split
from keras.models import Sequential,Model
from keras.layers import LSTM, Dense, Bidirectional, Input,Dropout,BatchNormalization
from keras import backend as K
from keras.engine.topology import Layer
from keras import initializers, regularizers, constraints

# Definition of model
model = Sequential()
model.add(BatchNormalization(input_shape=(10, 128)))
model.add(Bidirectional(LSTM(256, dropout=0.4, recurrent_dropout=0.4, activation='relu', return_sequences=True)))
model.add(Attention(10))
model.add(Dense(1,activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())

#fit on a portion of the training data, and validate on the rest
model.fit(x_train, y_train,
          batch_size=300,
          epochs=50,
          validation_data=(x_val, y_val))

# Evaluation
score, acc = model.evaluate(x_val, y_val, batch_size=256)
print('Test accuracy:', acc)

# Prediction
test_data = test['audio_embedding'].tolist()
submission = model.predict(pad_sequences(test_data))