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2025-01-16 Update From: SLTechnology News&Howtos shulou NAV: SLTechnology News&Howtos > Development >
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This article will explain in detail the example analysis of data filling and replication in TensorFlow artificial intelligence learning. The editor thinks it is very practical, so I share it with you as a reference. I hope you can get something after reading this article.
1.tf.pad ()
This method can fill 0 around the data, and the parameters are: the data to be filled + the position of 0.
The position of 0 is in the form of an array, corresponding to the following: [[upstream, downlink], [left column, right column]]. Specific examples are as follows:
The more commonly used are the upper and lower left and right lines.
When you padding a picture, the dimension of the data is usually [brecrine hrecoverw.c], so if you add two rows or two columns, you will increase the h and w in the middle:
2.tf.tile ()
This method can copy data, the parameters to be filled in: data, dimension + the corresponding number of copies.
Broadcast_to = expand_dims + tile, usually, as long as the operation conforms to the broadcast principle, and even the broadcast_to operation is not needed, it will be automatically extended and copied for operation.
This is the end of the article on "sample Analysis of filling and replication of TensorFlow artificial Intelligence Learning data". I hope the above content can be of some help to you, so that you can learn more knowledge. if you think the article is good, please share it out for more people to see.
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