tf.compat.v1.SparseConditionalAccumulator A conditional accumulator for aggregating sparse gradients. Inherits From: ConditionalAccumulatorBase  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.sparse.SparseConditionalAccumulator  
tf.compat.v1.SparseConditionalAccumulator(
    dtype, shape=None, shared_name=None,
    name='sparse_conditional_accumulator', reduction_type='MEAN'
)
 Sparse gradients are represented by IndexedSlices. Up-to-date gradients (i.e., time step at which gradient was computed is equal to the accumulator's time step) are added to the accumulator. Extraction of the average gradient is blocked until the required number of gradients has been accumulated.
 


 Args
  dtype   Datatype of the accumulated gradients.  
  shape   Shape of the accumulated gradients.  
  shared_name   Optional. If non-empty, this accumulator will be shared under the given name across multiple sessions.  
  name   Optional name for the accumulator.  
  reduction_type   Reduction type to use when taking the gradient.   
 


 Attributes
  accumulator_ref   The underlying accumulator reference.  
  dtype   The datatype of the gradients accumulated by this accumulator.  
  name   The name of the underlying accumulator.    Methods apply_grad View source 
apply_grad(
    grad_indices, grad_values, grad_shape=None, local_step=0, name=None
)
 Attempts to apply a sparse gradient to the accumulator. The attempt is silently dropped if the gradient is stale, i.e., local_step is less than the accumulator's global time step. A sparse gradient is represented by its indices, values and possibly empty or None shape. Indices must be a vector representing the locations of non-zero entries in the tensor. Values are the non-zero slices of the gradient, and must have the same first dimension as indices, i.e., the nnz represented by indices and values must be consistent. Shape, if not empty or None, must be consistent with the accumulator's shape (if also provided). Example: A tensor [[0, 0], [0, 1], [2, 3]] can be represented indices: [1,2] values: [[0,1],[2,3]] shape: [3, 2]
 


 Args
  grad_indices   Indices of the sparse gradient to be applied.  
  grad_values   Values of the sparse gradient to be applied.  
  grad_shape   Shape of the sparse gradient to be applied.  
  local_step   Time step at which the gradient was computed.  
  name   Optional name for the operation.   
 


 Returns   The operation that (conditionally) applies a gradient to the accumulator.  

 


 Raises
  InvalidArgumentError   If grad is of the wrong shape    apply_indexed_slices_grad View source 
apply_indexed_slices_grad(
    grad, local_step=0, name=None
)
 Attempts to apply a gradient to the accumulator. The attempt is silently dropped if the gradient is stale, i.e., local_step is less than the accumulator's global time step.
 


 Args
  grad   The gradient IndexedSlices to be applied.  
  local_step   Time step at which the gradient was computed.  
  name   Optional name for the operation.   
 


 Returns   The operation that (conditionally) applies a gradient to the accumulator.  

 


 Raises
  InvalidArgumentError   If grad is of the wrong shape    num_accumulated View source 
num_accumulated(
    name=None
)
 Number of gradients that have currently been aggregated in accumulator.
 


 Args
  name   Optional name for the operation.   
 


 Returns   Number of accumulated gradients currently in accumulator.  
 set_global_step View source 
set_global_step(
    new_global_step, name=None
)
 Sets the global time step of the accumulator. The operation logs a warning if we attempt to set to a time step that is lower than the accumulator's own time step.
 


 Args
  new_global_step   Value of new time step. Can be a variable or a constant  
  name   Optional name for the operation.   
 


 Returns   Operation that sets the accumulator's time step.  
 take_grad View source 
take_grad(
    num_required, name=None
)
 Attempts to extract the average gradient from the accumulator. The operation blocks until sufficient number of gradients have been successfully applied to the accumulator. Once successful, the following actions are also triggered:  Counter of accumulated gradients is reset to 0. Aggregated gradient is reset to 0 tensor. Accumulator's internal time step is incremented by 1. 
 


 Args
  num_required   Number of gradients that needs to have been aggregated  
  name   Optional name for the operation   
 


 Returns   A tuple of indices, values, and shape representing the average gradient.  

 


 Raises
  InvalidArgumentError   If num_required < 1    take_indexed_slices_grad View source 
take_indexed_slices_grad(
    num_required, name=None
)
 Attempts to extract the average gradient from the accumulator. The operation blocks until sufficient number of gradients have been successfully applied to the accumulator. Once successful, the following actions are also triggered:  Counter of accumulated gradients is reset to 0. Aggregated gradient is reset to 0 tensor. Accumulator's internal time step is incremented by 1. 
 


 Args
  num_required   Number of gradients that needs to have been aggregated  
  name   Optional name for the operation   
 


 Returns   An IndexedSlices holding the value of the average gradient.  

 


 Raises
  InvalidArgumentError   If num_required < 1