def build_model(self):
input_word = Input(shape=(int(self.max_len / 5),))
x_word = Embedding(len(self.embeddings),
300,
weights=[self.embeddings],
trainable=False)(input_word)
x_word = Bidirectional(LSTM(128, return_sequences=True))(x_word)
x_word = VanillaRNNAttention(256)(x_word)
model_word = Model(input_word, x_word)
# Sentence part
inputs = Input(shape=(self.max_len,)) # (5, self.max_len) ：(篇章最多包含的句子，每句包含的最大词数)
reshape = Reshape((5, int(self.max_len / 5)))(inputs)
x_sentence = TimeDistributed(model_word)(reshape)
x_sentence = Bidirectional(LSTM(128, return_sequences=True))(x_sentence)
x_sentence = VanillaRNNAttention(256)(x_sentence)
output = Dense(1, activation='sigmoid')(x_sentence)
model = Model(inputs=inputs, outputs=output)
model.compile('adam', 'binary_crossentropy', metrics=['accuracy'])
return model
def build_model(self):
inputs = Input(shape=(self.max_len,))
output = Embedding(len(self.embeddings),
300,
weights=[self.embeddings],
trainable=False)(inputs)
output = Bidirectional(LSTM(150, return_sequences=True, dropout=0.25, recurrent_dropout=0.25))(output)
output = VanillaRNNAttention(300)(output)
output = Dense(128, activation="relu")(output)
output = Dropout(0.25)(output)
output = Dense(1, activation="sigmoid")(output)
model = Model(inputs=inputs, outputs=output)
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.summary()
return model