Comments (6)
就是这种写法不支持windows的意思~~
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就是这种写法不支持windows的意思~~
秒回啊。。。
好吧,那不用windows了。。。对了,我还有个问题,seq2seq
时,用tfrecord
,指定了fixed length
的类型后,是不是不需要再用sequence__padding
了?因为 FixedLenFeature = tf.io.FixedLenFeature([sequence_length], tf.int64)
已经指定了sequence_length
?
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1、parallel_apply那里,如果非得用windows的话,可以考虑在parallel_apply中传入参数dummy=True,这样应该能跑,但是会慢一些;
2、你就算用FixedLenFeature,好像也得自己把单个样本向量padding好吧?(我对tfrecord其实不熟悉)。
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1、好吧,那不纠结parallel_apply了
2、对单个样本向量padding的话会不会对准确率有影响啊,因为之前没用tfrecord的时候,我们用的是sequence_padding,是取一个batch里最大的长度padding。。。
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padding多少个0都不会有影响的呀,模型会自动排除padding部分的影响,最终等价于没有padding。
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哦哦,这样啊,谢谢大佬~
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