tf.compat.v1.mixed_precision.MixedPrecisionLossScaleOptimizer An optimizer that applies loss scaling. Inherits From: Optimizer  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.train.experimental.MixedPrecisionLossScaleOptimizer  
tf.compat.v1.mixed_precision.MixedPrecisionLossScaleOptimizer(
    opt, loss_scale
)
 Loss scaling is a process that multiplies the loss by a multiplier called the loss scale, and divides each gradient by the same multiplier. The pseudocode for this process is: loss = ...
loss *= loss_scale
grads = gradients(loss, vars)
grads /= loss_scale
 Mathematically, loss scaling has no effect, but can help avoid numerical underflow in intermediate gradients when float16 tensors are used for mixed precision training. By multiplying the loss, each intermediate gradient will have the same multiplier applied. The loss scale can either be a fixed constant, chosen by the user, or be dynamically determined. Dynamically determining the loss scale is convenient as a loss scale does not have to be explicitly chosen. However it reduces performance. This optimizer wraps another optimizer and applies loss scaling to it via a LossScale. Loss scaling is applied whenever gradients are computed, such as through minimize().
 


 Args
  use_locking   Bool. If True apply use locks to prevent concurrent updates to variables.  
  name   A non-empty string. The name to use for accumulators created for the optimizer.   
 


 Raises
  ValueError   If name is malformed.    Methods apply_gradients View source 
apply_gradients(
    grads_and_vars, global_step=None, name=None
)
 Apply gradients to variables. This is the second part of minimize(). It returns an Operation that conditionally applies gradients if all gradient values are finite. Otherwise no update is performed (nor is global_step incremented).
 


 Args
  grads_and_vars   List of (gradient, variable) pairs as returned by compute_gradients().  
  global_step   Optional Variable to increment by one after the variables have been updated.  
  name   Optional name for the returned operation. Default to the name passed to the Optimizer constructor.   
 


 Returns   An Operation that conditionally applies the specified gradients. If global_step was not None, that operation also increments global_step.  

 


 Raises
  RuntimeError   If you should use _distributed_apply() instead.    compute_gradients View source 
compute_gradients(
    loss, var_list=None, gate_gradients=optimizer.Optimizer.GATE_OP,
    aggregation_method=None, colocate_gradients_with_ops=False, grad_loss=None
)
 Compute gradients of loss for the variables in var_list. This adjusts the dynamic range of the gradient evaluation by scaling up the loss value. The gradient values are then scaled back down by the reciprocal of the loss scale. This is useful in reduced precision training where small gradient values would otherwise underflow the representable range.
 


 Args
  loss   A Tensor containing the value to minimize or a callable taking no arguments which returns the value to minimize. When eager execution is enabled it must be a callable.  
  var_list   Optional list or tuple of tf.Variable to update to minimize loss. Defaults to the list of variables collected in the graph under the key GraphKeys.TRAINABLE_VARIABLES.  
  gate_gradients   How to gate the computation of gradients. Can be GATE_NONE, GATE_OP, or GATE_GRAPH.  
  aggregation_method   Specifies the method used to combine gradient terms. Valid values are defined in the class AggregationMethod.  
  colocate_gradients_with_ops   If True, try colocating gradients with the corresponding op.  
  grad_loss   Optional. A Tensor holding the gradient computed for loss.   
 


 Returns   A list of (gradient, variable) pairs. Variable is always present, but gradient can be None.  
 get_name View source 
get_name()
 get_slot View source 
get_slot(
    var, name
)
 Return a slot named name created for var by the Optimizer. Some Optimizer subclasses use additional variables. For example Momentum and Adagrad use variables to accumulate updates. This method gives access to these Variable objects if for some reason you need them. Use get_slot_names() to get the list of slot names created by the Optimizer.
 


 Args
  var   A variable passed to minimize() or apply_gradients().  
  name   A string.   
 


 Returns   The Variable for the slot if it was created, None otherwise.  
 get_slot_names View source 
get_slot_names()
 Return a list of the names of slots created by the Optimizer. See get_slot().
 


 Returns   A list of strings.  
 minimize View source 
minimize(
    loss, global_step=None, var_list=None, gate_gradients=GATE_OP,
    aggregation_method=None, colocate_gradients_with_ops=False, name=None,
    grad_loss=None
)
 Add operations to minimize loss by updating var_list. This method simply combines calls compute_gradients() and apply_gradients(). If you want to process the gradient before applying them call compute_gradients() and apply_gradients() explicitly instead of using this function.
 


 Args
  loss   A Tensor containing the value to minimize.  
  global_step   Optional Variable to increment by one after the variables have been updated.  
  var_list   Optional list or tuple of Variable objects to update to minimize loss. Defaults to the list of variables collected in the graph under the key GraphKeys.TRAINABLE_VARIABLES.  
  gate_gradients   How to gate the computation of gradients. Can be GATE_NONE, GATE_OP, or GATE_GRAPH.  
  aggregation_method   Specifies the method used to combine gradient terms. Valid values are defined in the class AggregationMethod.  
  colocate_gradients_with_ops   If True, try colocating gradients with the corresponding op.  
  name   Optional name for the returned operation.  
  grad_loss   Optional. A Tensor holding the gradient computed for loss.   
 


 Returns   An Operation that updates the variables in var_list. If global_step was not None, that operation also increments global_step.  

 


 Raises
  ValueError   If some of the variables are not Variable objects.    Eager Compatibility When eager execution is enabled, loss should be a Python function that takes no arguments and computes the value to be minimized. Minimization (and gradient computation) is done with respect to the elements of var_list if not None, else with respect to any trainable variables created during the execution of the loss function. gate_gradients, aggregation_method, colocate_gradients_with_ops and grad_loss are ignored when eager execution is enabled. variables View source 
variables()
 Returns the variables of the Optimizer.
 


 Class Variables
  GATE_GRAPH   2  
  GATE_NONE   0  
  GATE_OP   1