tf.mixed_precision.experimental.FixedLossScale Loss scale with a fixed value. Inherits From: LossScale  View aliases  Main aliases 
tf.train.experimental.FixedLossScale Compat aliases for migration See Migration guide for more details. tf.compat.v1.mixed_precision.FixedLossScale, tf.compat.v1.mixed_precision.experimental.FixedLossScale, tf.compat.v1.train.experimental.FixedLossScale  
tf.mixed_precision.experimental.FixedLossScale(
    loss_scale_value
)
 Warning: This class is deprecated and will be unexposed from the TF 2 namespace starting in TensorFlow 2.5. In TensorFlow 2.5, this class will only be accessible as tf.compat.v1.mixed_precision.FixedLossScale. Additionally in 2.5, you will no longer be able to pass a FixedLossScale to a tf.keras.mixed_precision.Policy. All the functionality in this class has been merged into tf.keras.mixed_precision.LossScaleOptimizer, so this class is no longer needed. The loss scale is not updated for the lifetime of instances of this class. A given instance of this class always returns the same number when called.
 


 Args
  loss_scale_value   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_value is less than 1.    Methods from_config View source 
@classmethod
from_config(
    config
)
 Creates the LossScale from its config. get_config View source 
get_config()
 Returns the config of this loss scale. update View source 
update(
    grads
)
 Updates the value of the loss scale. The loss scale will be potentially updated, based on the value of grads. The tensor returned by calling this class is only updated when this function is evaluated. In eager mode, this directly updates the loss scale, so that calling __call__ will return the newly updated loss scale. In graph mode, this returns an op that, when evaluated, updates the loss scale. This function also returns a should_apply_gradients bool. If False, gradients should not be applied to the variables that step, as nonfinite gradients were found, and the loss scale has been be updated to reduce the chance of finding nonfinite gradients in the next step. Some loss scale classes will always return True, as they cannot adjust themselves in response to nonfinite gradients. When a DistributionStrategy is used, this function may only be called in a cross-replica context.
 


 Args
  grads   A nested structure of unscaled gradients, each which is the gradient of the loss with respect to a weight. The gradients should have already been divided by the loss scale being before passed to this function. 'None' gradients are accepted, and are ignored.   
 


 Returns
  update_op   In eager mode, None. In graph mode, an op to update the loss scale.  
  should_apply_gradients   Either a bool or a scalar boolean tensor. If False, the caller should skip applying grads to the variables this step.    __call__ View source 
__call__()
 Returns the current loss scale as a scalar float32 tensor.