tf.contrib.mixed_precision.FixedLossScaleManager
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Loss scale manager with a fixed loss scale.
Inherits From: LossScaleManager
tf.contrib.mixed_precision.FixedLossScaleManager(
loss_scale
)
The loss scale is not updated for the lifetime of the class.
Args |
loss_scale
|
A Python float. Its ideal value varies depending on models to
run. Choosing a too small loss_scale might affect model quality; a too
big loss_scale might cause inf or nan. There is no single right
loss_scale to apply. There is no harm choosing a relatively big number
as long as no nan or inf is encountered in training.
|
Raises |
ValueError
|
If loss_scale is less than 1.
|
Methods
get_loss_scale
View source
get_loss_scale()
Returns the loss scale as a scalar float32
tensor.
update_loss_scale
View source
update_loss_scale(
finite_grads
)
Updates loss scale based on if gradients are finite in current step.
Args |
finite_grads
|
bool scalar tensor indicating if all gradients are
finite (i.e., not inf or nan).
|
Returns |
An op, when executed updates the loss scale. If eager execution is
enabled, does not return anything.
|
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Last updated 2020-10-01 UTC.
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