Optimizer that implements the Adadelta algorithm.

Inherits From: Optimizer


ADADELTA - An Adaptive Learning Rate Method: Zeiler, 2012 (pdf)

learning_rate A Tensor or a floating point value. The learning rate. To match the exact form in the original paper use 1.0.
rho A Tensor or a floating point value. The decay rate.
epsilon A Tensor or a floating point value. A constant epsilon used to better conditioning the grad update.
use_locking If True use locks for update operations.
name Optional name prefix for the operations created when applying gradients. Defaults to "Adadelta".



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Apply gradients to variables.

This is the second part of minimize(). It returns an Operation that applies gradients.

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.

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

TypeError If grads_and_vars is malformed.
ValueError If none of the variables have gradients.
RuntimeError If you should use _distributed_apply() instead.


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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.

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.

A list of (gradient, variable) pairs. Variable is always present, but gradient can be None.