def predict(self, left, right):
"""
Forward network
"""
# embedding layer
emb_layer = layers.EmbeddingLayer(self.dict_size, self.emb_dim, "emb")
left_emb = emb_layer.ops(left)
right_emb = emb_layer.ops(right)
# Presentation context
lstm_layer = layers.DynamicLSTMLayer(self.lstm_dim, "lstm")
left_lstm = lstm_layer.ops(left_emb)
right_lstm = lstm_layer.ops(right_emb)
last_layer = layers.SequenceLastStepLayer()
left_last = last_layer.ops(left_lstm)
right_last = last_layer.ops(right_lstm)
# matching layer
if self.task_mode == "pairwise":
relu_layer = layers.FCLayer(self.hidden_dim, "relu", "relu")
left_relu = relu_layer.ops(left_last)
right_relu = relu_layer.ops(right_last)
cos_sim_layer = layers.CosSimLayer()
pred = cos_sim_layer.ops(left_relu, right_relu)
return left_relu, pred
else:
concat_layer = layers.ConcatLayer(1)
concat = concat_layer.ops([left_last, right_last])
relu_layer = layers.FCLayer(self.hidden_dim, "relu", "relu")
concat_fc = relu_layer.ops(concat)
softmax_layer = layers.FCLayer(2, "softmax", "cos_sim")
pred = softmax_layer.ops(concat_fc)
return left_last, pred
def predict(self, left, right):
"""
Forward network
"""
# embedding layer
emb_layer = layers.EmbeddingLayer(self.dict_size, self.emb_dim, "emb")
left_emb = emb_layer.ops(left)
right_emb = emb_layer.ops(right)
# Presentation context
gru_layer = layers.DynamicGRULayer(self.gru_dim, "gru")
left_gru = gru_layer.ops(left_emb)
right_gru = gru_layer.ops(right_emb)
last_layer = layers.SequenceLastStepLayer()
left_last = last_layer.ops(left_gru)
right_last = last_layer.ops(right_gru)
tanh_layer = layers.FCLayer(self.hidden_dim, "tanh", "tanh")
left_tanh = tanh_layer.ops(left_last)
right_tanh = tanh_layer.ops(right_last)
# matching layer
if self.task_mode == "pairwise":
cos_sim_layer = layers.CosSimLayer()
pred = cos_sim_layer.ops(left_tanh, right_tanh)
else:
concat_layer = layers.ConcatLayer(1)
concat = concat_layer.ops([left_tanh, right_tanh])
softmax_layer = layers.FCLayer(2, "softmax", "cos_sim")
pred = softmax_layer.ops(concat)
return left_tanh, pred
def predict(self, left, right):
"""
Forward network
"""
# embedding layer
emb_layer = layers.EmbeddingLayer(self.dict_size, self.emb_dim, "emb")
left_emb = emb_layer.ops(left)
right_emb = emb_layer.ops(right)
# Presentation context
pool_layer = layers.SequencePoolLayer("sum")
left_pool = pool_layer.ops(left_emb)
right_pool = pool_layer.ops(right_emb)
softsign_layer = layers.SoftsignLayer()
left_soft = softsign_layer.ops(left_pool)
right_soft = softsign_layer.ops(right_pool)
bow_layer = layers.FCLayer(self.bow_dim, None, "tanh")
left_bow = bow_layer.ops(left_soft)
right_bow = bow_layer.ops(right_soft)
# matching layer
if self.task_mode == "pairwise":
cos_sim_layer = layers.CosSimLayer()
pred = cos_sim_layer.ops(left_bow, right_bow)
else:
concat_layer = layers.ConcatLayer(1)
concat = concat_layer.ops([left_bow, right_bow])
softmax_layer = layers.FCLayer(2, "softmax", "cos_sim")
pred = softmax_layer.ops(concat)
return left_bow, pred
def predict(self, left, right):
"""
Forward network
"""
# embedding layer
emb_layer = layers.EmbeddingLayer(self.dict_size, self.emb_dim, "emb")
left_emb = emb_layer.ops(left)
right_emb = emb_layer.ops(right)
# Presentation context
cnn_layer = layers.SequenceConvPoolLayer(
self.filter_size, self.num_filters, "conv")
left_cnn = cnn_layer.ops(left_emb)
right_cnn = cnn_layer.ops(right_emb)
# matching layer
if self.task_mode == "pairwise":
relu_layer = layers.FCLayer(self.hidden_dim, "relu", "relu")
left_relu = relu_layer.ops(left_cnn)
right_relu = relu_layer.ops(right_cnn)
cos_sim_layer = layers.CosSimLayer()
pred = cos_sim_layer.ops(left_relu, right_relu)
return left_relu, pred
else:
concat_layer = layers.ConcatLayer(1)
concat = concat_layer.ops([left_cnn, right_cnn])
relu_layer = layers.FCLayer(self.hidden_dim, "relu", "relu")
concat_fc = relu_layer.ops(concat)
softmax_layer = layers.FCLayer(2, "softmax", "cos_sim")
pred = softmax_layer.ops(concat_fc)
return left_cnn, pred
