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TypeError: float(Tensor) is not supported in static graph mode. Because it's value is not available during the static mode.If you haven't call float(Tensor) explicitly, it's usually triggered by the logging implicitly, for example: >>> logging.info("The value of x is: {float(x)}") ^ `x` is Tensor, `float(x)` triggers float(Tensor) There are two common workarounds available: If you are logging Tensor values, then consider logging only at dynamic graphs, for example: Modify the following code >>> logging.info("The value of x is: {float(x)}") to >>> if paddle.in_dynamic_mode(): ... logging.info("The value of x is: {float(x)}") If you need to convert the Tensor type, for example: Modify the following code >>> x = float(x) to >>> x = x.astype("float64")
新的 int(Value) 报错信息
TypeError: int(Tensor) is not supported in static graph mode. Because it's value is not available during the static mode.If you haven't call int(Tensor) explicitly, it's usually triggered by the logging implicitly, for example: >>> logging.info("The value of x is: {int(x)}") ^ `x` is Tensor, `int(x)` triggers int(Tensor) There are two common workarounds available: If you are logging Tensor values, then consider logging only at dynamic graphs, for example: Modify the following code >>> logging.info("The value of x is: {int(x)}") to >>> if paddle.in_dynamic_mode(): ... logging.info("The value of x is: {int(x)}") If you need to convert the Tensor type, for example: Modify the following code >>> x = int(x) to >>> x = x.astype("int64")
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PR Category
Execute Infrastructure
PR Types
Improvements
Description
新的
float(Value)
报错信息新的
int(Value)
报错信息