tf.compat.v1.train.AdagradDAOptimizer Adagrad Dual Averaging algorithm for sparse linear models. Inherits From: Optimizer 
tf.compat.v1.train.AdagradDAOptimizer(
    learning_rate, global_step, initial_gradient_squared_accumulator_value=0.1,
    l1_regularization_strength=0.0, l2_regularization_strength=0.0,
    use_locking=False, name='AdagradDA'
)
 This optimizer takes care of regularization of unseen features in a mini batch by updating them when they are seen with a closed form update rule that is equivalent to having updated them on every mini-batch. AdagradDA is typically used when there is a need for large sparsity in the trained model. This optimizer only guarantees sparsity for linear models. Be careful when using AdagradDA for deep networks as it will require careful initialization of the gradient accumulators for it to train. References: Adaptive Subgradient Methods for Online Learning and Stochastic Optimization :Duchi et al., 2011 (pdf)
 


 Args
  learning_rate   A Tensor or a floating point value. The learning rate.  
  global_step   A Tensor containing the current training step number.  
  initial_gradient_squared_accumulator_value   A floating point value. Starting value for the accumulators, must be positive.  
  l1_regularization_strength   A float value, must be greater than or equal to zero.  
  l2_regularization_strength   A float value, must be greater than or equal to zero.  
  use_locking   If True use locks for update operations.  
  name   Optional name prefix for the operations created when applying gradients. Defaults to "AdagradDA".   
 


 Raises
  ValueError   If the initial_gradient_squared_accumulator_value is invalid.    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 applies gradients.
 


 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 applies the specified gradients. If global_step was not None, that operation also increments global_step.  

 


 Raises
  TypeError   If grads_and_vars is malformed.  
  ValueError   If none of the variables have gradients.  
  RuntimeError   If you should use _distributed_apply() instead.    compute_gradients View source 
compute_gradients(
    loss, var_list=None, gate_gradients=GATE_OP, aggregation_method=None,
    colocate_gradients_with_ops=False, grad_loss=None
)
 Compute gradients of loss for the variables in var_list. This is the first part of minimize(). It returns a list of (gradient, variable) pairs where "gradient" is the gradient for "variable". Note that "gradient" can be a Tensor, an IndexedSlices, or None if there is no gradient for the given variable.
 


 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.  

 


 Raises
  TypeError   If var_list contains anything else than Variable objects.  
  ValueError   If some arguments are invalid.  
  RuntimeError   If called with eager execution enabled and loss is not callable.    Eager Compatibility When eager execution is enabled, gate_gradients, aggregation_method, and colocate_gradients_with_ops are ignored. 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()
 A list of variables which encode the current state of Optimizer. Includes slot variables and additional global variables created by the optimizer in the current default graph.
 


 Returns   A list of variables.  

 


 Class Variables
  GATE_GRAPH   2  
  GATE_NONE   0  
  GATE_OP   1