tf.compat.v1.train.AdadeltaOptimizer

Optimizer that implements the Adadelta algorithm.

Inherits From: Optimizer

tf.compat.v1.train.AdadeltaOptimizer(
    learning_rate=0.001,
    rho=0.95,
    epsilon=1e-08,
    use_locking=False,
    name='Adadelta'
)

Migrate to TF2

tf.compat.v1.train.AdadeltaOptimizer is compatible with eager mode and tf.function. When eager execution is enabled, learning_rate, rho, and epsilon can each be a callable that takes no arguments and returns the actual value to use. This can be useful for changing these values across different invocations of optimizer functions.

To switch to native TF2 style, use tf.keras.optimizers.Adadelta instead. Please notice that due to the implementation differences, tf.keras.optimizers.Adadelta and tf.compat.v1.train.AdadeltaOptimizer may have slight differences in floating point numerics even though the formula used for the variable updates still matches.

Structural mapping to native TF2

Before:

optimizer = tf.compat.v1.train.AdadeltaOptimizer(
  learning_rate=learning_rate,
  rho=rho,
  epsilon=epsilon)

After:

optimizer = tf.keras.optimizers.Adadelta(
  learning_rate=learning_rate,
  rho=rho,
  epsilon=epsilon)

How to map arguments

TF1 Arg Name	TF2 Arg Name	Note
`learning_rate`	`learning_rate`	Be careful of setting learning_rate tensor value computed from the global step. In TF1 this was usually meant to imply a dynamic learning rate and would recompute in each step. In TF2 (eager + function) it will treat it as a scalar value that only gets computed once instead of a symbolic placeholder to be computed each time.
`rho`	`rho`	-
`epsilon`	`epsilon`	Default value is 1e-08 in TF1, but 1e-07 in TF2.
`use_locking`	-	Not applicable in TF2.

Before & after usage example

Before:

x = tf.Variable([1,2,3], dtype=tf.float32)
grad = tf.constant([0.1, 0.2, 0.3])
optimizer = tf.compat.v1.train.AdadeltaOptimizer(learning_rate=0.001)
optimizer.apply_gradients(zip([grad], [x]))

After:

x = tf.Variable([1,2,3], dtype=tf.float32)
grad = tf.constant([0.1, 0.2, 0.3])
optimizer = tf.keras.optimizers.Adadelta(learning_rate=0.001)
optimizer.apply_gradients(zip([grad], [x]))

Description

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

Args
`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".

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.

@compatibility(TF2)

How to Map Arguments

TF1 Arg Name	TF2 Arg Name	Note
`grads_and_vars`	`grads_and_vars`	-
`global_step`	Not supported.	Use `optimizer.iterations`
`name`	`name.`	-

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.

Migrate to TF2

tf.keras.optimizers.Optimizer in TF2 does not provide a compute_gradients method, and you should use a tf.GradientTape to obtain the gradients:

@tf.function
def train step(inputs):
  batch_data, labels = inputs
  with tf.GradientTape() as tape:
    predictions = model(batch_data, training=True)
    loss = tf.keras.losses.CategoricalCrossentropy(
        reduction=tf.keras.losses.Reduction.NONE)(labels, predictions)
  gradients = tape.gradient(loss, model.trainable_variables)
  optimizer.apply_gradients(zip(gradients, model.trainable_variables))

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.

@compatibility(eager) When eager execution is enabled, gate_gradients, aggregation_method, and colocate_gradients_with_ops are ignored.

Description

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.

`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`

tf.compat.v1.train.AdadeltaOptimizer Stay organized with collections Save and categorize content based on your preferences.

Migrate to TF2

Structural mapping to native TF2

How to map arguments

Before & after usage example

Description

References

Args

Methods

apply_gradients

How to Map Arguments

compute_gradients

Migrate to TF2

Description

get_name

get_slot

get_slot_names

minimize

eager compatibility

variables

Class Variables

tf.compat.v1.train.AdadeltaOptimizer

`apply_gradients`

`compute_gradients`

`get_name`

`get_slot`

`get_slot_names`

`minimize`

`variables`