def _build_network(self, vocab_size, maxlen, embedding_dimension=256, hidden_units=256, trainable=False):
    print('Build model...')
    model = Sequential()
    print('Reached here')

    model.add(Embedding(vocab_size, embedding_dimension, input_length=maxlen, embeddings_initializer='glorot_normal'))
    print('embedding done')

    model.add(Convolution1D(hidden_units, 3, kernel_initializer='he_normal', padding='valid', activation='sigmoid',
                            input_shape=(1, maxlen)))
    # model.add(MaxPooling1D(pool_size=3))
    model.add(Convolution1D(hidden_units, 3, kernel_initializer='he_normal', padding='valid', activation='sigmoid',
                            input_shape=(1, maxlen - 2)))
    print('conv1dcomplete')
    # model.add(MaxPooling1D(pool_size=3))

    # model.add(Dropout(0.25))

    model.add(LSTM(hidden_units, kernel_initializer='he_normal', activation='sigmoid', dropout=0.5, return_sequences=True))
    #print('Reached here')

    model.add(LSTM(hidden_units, kernel_initializer='he_normal', activation='sigmoid', dropout=0.5))

    model.add(Dense(hidden_units, kernel_initializer='he_normal', activation='sigmoid'))
    model.add(Dense(2))
    model.add(Activation('softmax'))
    adam = Adam(lr=0.0001)
    model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])
    print('No of parameter:', model.count_params())
    print(model.summary())
    return model