def get_model():
sentence_inp = Input(shape=(SENT_LENGTH, ))
emb = Embedding(nb_words,
EMBEDDING_DIM,
input_length=SENT_LENGTH,
weights=[embedding_matrix],
trainable=False)(sentence_inp)
bigru = Bidirectional(CuDNNGRU(128, return_sequences=True))(emb)
bigru = TimeDistributed(Dense(256))(bigru)
bigru = AttentivePoolingLayer()(bigru)
sentEncoder = Model(sentence_inp, bigru)
review_input = Input(shape=(MAX_SENTS, SENT_LENGTH))
review_encoder = TimeDistributed(sentEncoder)(review_input)
bigru_sent = Bidirectional(CuDNNGRU(128,
return_sequences=True))(review_encoder)
bigru_sent = TimeDistributed(Dense(256))(bigru_sent)
bigru_sent = AttentivePoolingLayer()(bigru_sent)
fc1 = Dense(256, activation='relu')(bigru_sent)
fc2 = Dense(128, activation='relu')(fc1)
fc2 = BatchNormalization()(fc2)
output = Dense(19, activation="softmax")(fc2)
model = Model(inputs=review_input, outputs=output)
model.compile(optimizer="adam",
loss="categorical_crossentropy",
metrics=['accuracy'])
model.summary()
return model
def HAN(MAX_SENT_LENGTH, MAX_SENTS, max_features, embedding_dim, vecs):
embedding_layer = Embedding(
max_features,
embedding_dim,
weights=[vecs],
input_length=MAX_SENT_LENGTH,
trainable=True,
)
sentence_input = Input(shape=(MAX_SENT_LENGTH, ), dtype='int32')
embedded_sequences = embedding_layer(sentence_input)
# embedded_sequences =Masking()(embedded_sequences)
l_lstm = Bidirectional(LSTM(150,
return_sequences=True))(embedded_sequences)
l_lstm = AttentivePoolingLayer()(l_lstm)
sentEncoder = Model(sentence_input, l_lstm)
review_input = Input(shape=(MAX_SENTS, MAX_SENT_LENGTH), dtype='int32')
review_encoder = TimeDistributed(sentEncoder)(review_input)
# review_encoder =Masking()(review_encoder)
l_lstm_sent = Bidirectional(LSTM(50,
return_sequences=True))(review_encoder)
l_lstm_sent = AttentivePoolingLayer()(l_lstm_sent)
preds = Dense(2, activation='softmax')(l_lstm_sent)
model = Model(review_input, preds)
return model
def get_model():
inp = Input(shape=(MAX_SEQUENCE_LENGTH,))
emb = Embedding(nb_words, EMBEDDING_DIM, input_length=MAX_SEQUENCE_LENGTH, weights=[embedding_matrix],
trainable=False)(inp)
gru=Bidirectional(CuDNNGRU(256,return_sequences=True))(emb)
gru=AttentivePoolingLayer()(gru)
fc1 = Dense(256, activation='relu')(gru)
fc2 = Dense(128, activation='relu')(fc1)
fc2 = BatchNormalization()(fc2)
output = Dense(19, activation="softmax")(fc2)
model = Model(inputs=inp, outputs=output)
model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=['accuracy'])
model.summary()
return model
def get_attention_cv_model(MAX_SEQUENCE_LENGTH, nb_words, EMBEDDING_DIM,
embedding_matrix):
inp = Input(shape=(MAX_SEQUENCE_LENGTH, ))
emb = Embedding(nb_words,
EMBEDDING_DIM,
input_length=MAX_SEQUENCE_LENGTH,
weights=[embedding_matrix],
trainable=True,
dropout=0.1)(inp)
att = AttentivePoolingLayer()(emb)
fc1 = Dense(256, activation='relu')(att)
fc2 = Dense(64, activation='relu')(fc1)
fc2 = BatchNormalization()(fc2)
output = Dense(2, activation="softmax")(fc2)
model = Model(inputs=inp, outputs=output)
adam = Adam(lr=1e-3)
model.compile(optimizer=adam,
loss="categorical_crossentropy",
metrics=['accuracy'])
model.summary()
return model