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This article mainly introduces "how to use overloaded operation in Tensorflow". In daily operation, I believe many people have doubts about how to use overloaded operation in Tensorflow. The editor consulted all kinds of materials and sorted out simple and easy-to-use operation methods. I hope it will be helpful to answer the doubts about "how to use overloaded operation in Tensorflow". Next, please follow the editor to study!

Like Numpy, Tensorflow overloads many Python operators to make building diagrams easier and code more readable.

1. Overload slicing operation

The slicing operation is one of the overloaded operators where the index tensor is very easy.

Although this operation is convenient, be careful when using it. Slicing is very inefficient and it is usually best to avoid it, especially when the number of slices is very high.

2. Overload arithmetic and logical operators

Tensorflow also overloads a series of arithmetic and logical operators.

Other unsupported operators: equal to (= =) and not equal to (! The =) operator, which can be overloaded in Numpy but not in Tensorflow, is implemented in tf.equal and tf.not_equal.

At this point, the study on "how Tensorflow uses overloaded operations" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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