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

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".

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

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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

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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

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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.

GATE_GRAPH 2
GATE_NONE 0
GATE_OP 1