def extrac_subject(inputs):
"""根据subject_ids从output中取出subject的向量表征
"""
output, subject_ids = inputs
subject_ids = K.cast(subject_ids, 'int32')
start = batch_gather(output, subject_ids[:, :1])
end = batch_gather(output, subject_ids[:, 1:])
subject = K.concatenate([start, end], 2)
return subject[:, 0]
def extrac_subject(inputs):
"""根据subject_ids从output中取出subject的向量表征
"""
output, subject_ids = inputs
subject_ids = K.cast(
subject_ids,
'int32') # Tensor("lambda_2/Cast:0", shape=(?, 2), dtype=int32)
start = batch_gather(
output, subject_ids[:, :1]
) # 取出start的向量 subject_ids表示每个btz中只有一个数字 Tensor("lambda_2/Gather/Reshape_3:0", shape=(?, 1, 768), dtype=float32)
end = batch_gather(
output, subject_ids[:, 1:]
) # Tensor("lambda_2/Gather_1/Reshape_3:0", shape=(?, 1, 768), dtype=float32)
subject = K.concatenate(
[start, end],
2) # Tensor("lambda_2/concat:0", shape=(?, 1, 1536), dtype=float32)
return subject[:, 0]
def compute_seq2seq_loss(self, inputs, mask=None):
y_true, y_mask, _, y_pred, _ = inputs
y_true = y_true[:, 1:] # 目标token_ids
y_mask = y_mask[:, :-1] * y_mask[:, 1:] # segment_ids，刚好指示了要预测的部分
y_pred = y_pred[:, :-1] # 预测序列，错开一位
# 正loss
pos_loss = batch_gather(y_pred, y_true[..., None])[..., 0]
# 负loss
y_pred = tf.nn.top_k(y_pred, k=k_sparse)[0]
neg_loss = K.logsumexp(y_pred, axis=-1)
# 总loss
loss = neg_loss - pos_loss
loss = K.sum(loss * y_mask) / K.sum(y_mask)
return loss
def call(self, input):
input, output, label = input
output = batch_gather(output, label)
return K.gradients(output, [input])[0] * input
def call(self, input):
input, output, label = input
label = K.cast(label, 'int32')
output = batch_gather(output, label)
return K.gradients(output, [input])[0] * input
def extract_subject(inputs):
output, subject_ids = inputs
start = batch_gather(output, subject_ids[:, :1])
end = batch_gather(output, subject_ids[:, 1:])
subject = K.concatenate([start, end], 2)
return subject[:, 0]