def build_model(max_len=200, aspect_max_len=9, embedding_matrix=[],
position_embedding_matrix=[], class_num=3, num_words=10000):
MAX_LEN = max_len
# ============================= Input =================================================
sentence_input = Input(shape=(max_len,), dtype='int32', name='sentence_input') # (?, 78)
position_input = Input(shape=(max_len,), dtype='int32', name='position_input') # (?, 78)
aspect_input = Input(shape=(aspect_max_len,), dtype='int32', name='aspect_input') # (?, 78)
sentence_embedding_layer = Embedding(num_words + 1, EMBEDDING_DIM, weights=[embedding_matrix],
input_length=max_len, trainable=False, mask_zero=True)
sentence_embedding = sentence_embedding_layer(sentence_input) # (?, 78, 300)
position_embedding = Embedding(max_len * 2, POSITION_EMBEDDING_DIM, weights=[position_embedding_matrix],
input_length=max_len, trainable=True, mask_zero=True)(position_input) # (?, 78, 50)
aspect_embedding_layer = Embedding(num_words + 1, EMBEDDING_DIM, weights=[embedding_matrix],
input_length=aspect_max_len, trainable=False, mask_zero=True)
aspect_embedding = aspect_embedding_layer(aspect_input) # (?, 9, 300)
# ============================= Input =================================================
# =========================== GRU Layer ===============================================
input_embedding = keras.layers.concatenate([sentence_embedding, position_embedding]) # (?, 78, 350)
encode_x = Bidirectional(GRU(300, activation="relu", return_sequences=True, recurrent_dropout=0.5, dropout=0.5))(input_embedding) # (?, 78, 600)
# =========================== GRU Layer ===============================================
# =========================== attention ===============================================
# --------------------------- source2aspect attention ----------------------------------------
aspect_attention = TimeDistributed(Dense(1, activation='tanh'))(aspect_embedding) # (?, 9, 1)
aspect_attention = Lambda(reduce_dimension,
output_shape=reduce_dimension_output_shape,
arguments={'length': aspect_max_len},
mask=aspect_embedding_layer.get_output_mask_at(0),
name='aspect_attention')(aspect_attention) # (?, 9)
aspect_embedding = Lambda(attention,
output_shape=attention_output_shape,
arguments={'dim': 300})([aspect_attention, aspect_embedding]) # (?, 300)
# --------------------------- aspect attention ----------------------------------------
aspect_embedding = Lambda(liter,
output_shape=liter_output_shape,
arguments={'length': max_len})(aspect_embedding) # (?, 82, 300)
x = keras.layers.concatenate([aspect_embedding, encode_x]) # (?, 82, 900)
x = TimeDistributed(Dense(300, activation='tanh'))(x) # (?, 82, 300)
x = keras.layers.concatenate([x, encode_x]) # (?, 82, 900)
# --------------------------- source2token attention ----------------------------------------
x = TimeDistributed(Dense(1, activation='tanh'))(x) # (?, 78, 1)
attention_x = Lambda(reduce_dimension,
output_shape=reduce_dimension_output_shape,
arguments={'length': max_len},
mask=sentence_embedding_layer.get_output_mask_at(0),
name='attention_x')(x) # (?, 78)
# --------------------------- source2token attention ----------------------------------
# =========================== attention ===============================================
x = Lambda(attention, output_shape=attention_output_shape, arguments={'dim': 600})([attention_x, encode_x]) # (?, 600)
x = Dropout(rate=0.5)(x)
predictions = Dense(class_num, activation='softmax')(x) # (?, 3)
model = Model(inputs=[sentence_input, position_input, aspect_input], outputs=predictions)
model.compile(loss=['categorical_crossentropy'], optimizer='rmsprop', metrics=['accuracy'])
print(model.summary())
return model
