Exception that indicates that static shapes are not able to broadcast among each other during
arithmetic operations. Static shapes do not have unknown rank or any unknown dimensions hasUnknownDimension()
. The term broadcasting describes how TensorFlow treats arrays with
different shapes during arithmetic operations.
Broadcasting is the process of making arrays to have compatible shapes for arithmetic operations. Two shapes are compatible if for each dimension pair they are either equal or one of them is one. When trying to broadcast a Tensor to a shape, it starts with the trailing dimensions, and works its way forward.
See Also
Public Constructors
NotBroadcastableException(String message)
Creates a new NotBroadcastableException exception with the specified detail message
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NotBroadcastableException(String message, Throwable cause)
Creates a new NotBroadcastableException exception with the specified detail message
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Inherited Methods
Public Constructors
public NotBroadcastableException (String message)
Creates a new NotBroadcastableException exception with the specified detail message
Parameters
message | the detail message. |
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public NotBroadcastableException (String message, Throwable cause)
Creates a new NotBroadcastableException exception with the specified detail message
Parameters
message | the detail message. |
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cause | the cause |