from tensorflow.keras.layers import Activation
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Embedding
from tensorflow.keras.layers import Input
from tensorflow.keras.layers import LSTM
from tensorflow.keras.models import Sequential

def Network(max_seq_len, vocab_size):
    model = Sequential()
    model.add(Embedding(vocab_size, 64, input_length=max_seq_len))
    model.add(LSTM(64, return_sequences = True))
    model.add(Dropout(0.5))
    model.add(LSTM(64))
    model.add(Dense(256, name='FC1'))
    model.add(Dropout(0.5))
    model.add(Dense(1, name='out_layer'))
    model.add(Activation('sigmoid'))
    return model

from tensorflow.keras.layers import Activation
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Embedding
from tensorflow.keras.layers import Input
from tensorflow.keras.layers import LSTM
from tensorflow.keras.models import Sequential

def Network(max_seq_len, vocab_size):
    model = Sequential()
    model.add(Embedding(vocab_size, 64, input_length=max_seq_len))
    model.add(LSTM(64, return_sequences = True))
    model.add(Dropout(0.5))
    model.add(LSTM(64))
    model.add(Dense(256, name='FC1'))
    model.add(Dropout(0.5))
    model.add(Dense(1, name='out_layer'))
    model.add(Activation('sigmoid'))
    return model
