keras_bilstm.py

# BiDirectional LSTM
def model_lstm_du(embedding_matrix):
    inp = Input(shape=(maxlen,))
    x = Embedding(max_features, embed_size, weights=[embedding_matrix])(inp)
    '''
    Here 64 is the size(dim) of the hidden state vector as well as the output vector. Keeping return_sequence we want the output for the entire sequence. So what is the dimension of output for this layer?
        64*70(maxlen)*2(bidirection concat)
    CuDNNLSTM is fast implementation of LSTM layer in Keras which only runs on GPU
    '''
    x = Bidirectional(CuDNNLSTM(64, return_sequences=True))(x)
    avg_pool = GlobalAveragePooling1D()(x)
    max_pool = GlobalMaxPooling1D()(x)
    conc = concatenate([avg_pool, max_pool])
    conc = Dense(64, activation="relu")(conc)
    conc = Dropout(0.1)(conc)
    outp = Dense(1, activation="sigmoid")(conc)
    model = Model(inputs=inp, outputs=outp)
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model