tf.compat.v1.train.Optimizer

Base class for optimizers.

tf.compat.v1.train.Optimizer(
    use_locking, name
)

Migrate to TF2

Caution: This API was designed for TensorFlow v1. Continue reading for details on how to migrate from this API to a native TensorFlow v2 equivalent. See the TensorFlow v1 to TensorFlow v2 migration guide for instructions on how to migrate the rest of your code.

tf.compat.v1.train.Optimizer can be used in eager mode and tf.function, but it is not recommended. Please use the subclasses of tf.keras.optimizers.Optimizer instead in TF2. Please see Basic training loops or Writing a training loop from scratch for examples.

If your TF1 code contains a tf.compat.v1.train.Optimizer symbol, you cannot simply replace that with the corresponding tf.keras.optimizers.Optimizers. To migrate to TF2, it is advised the whole training program to be migrated to Keras Model.fit based, or TF2 custom training loops.

Structural Mapping to Native TF2

Before:

sgd_op = tf.compat.v1.train.GradientDescentOptimizer(3.0)
opt_op = sgd_op.minimize(cost, global_step, [var0, var1])
opt_op.run(session=session)

After:

sgd = tf.keras.optimizers.SGD(3.0)
sgd.minimize(cost_fn, [var0, var1])

How to Map Arguments

TF1 Arg Name	TF2 Arg Name	Note
use_locking	Not supported	-
name	name.	-

Before & After Usage Example

Before:

g = tf.compat.v1.Graph()
with g.as_default():
  var0 = tf.compat.v1.Variable([1.0, 2.0])
  var1 = tf.compat.v1.Variable([3.0, 4.0])
  cost = 5 * var0 + 3 * var1
  global_step = tf.compat.v1.Variable(
      tf.compat.v1.zeros([], tf.compat.v1.int64), name='global_step')
  init_op = tf.compat.v1.initialize_all_variables()
  sgd_op = tf.compat.v1.train.GradientDescentOptimizer(3.0)
  opt_op = sgd_op.minimize(cost, global_step, [var0, var1])
session = tf.compat.v1.Session(graph=g)
session.run(init_op)
opt_op.run(session=session)
print(session.run(var0))
[-14. -13.]

After:

>>> var0 = tf.Variable([1.0, 2.0])
>>> var1 = tf.Variable([3.0, 4.0])
>>> cost_fn = lambda: 5 * var0 + 3 * var1
>>> sgd = tf.keras.optimizers.SGD(3.0)
>>> sgd.minimize(cost_fn, [var0, var1])
>>> print(var0.numpy())
[-14. -13.]

Description

This class defines the API to add Ops to train a model. You never use this class directly, but instead instantiate one of its subclasses such as GradientDescentOptimizer, AdagradOptimizer, or MomentumOptimizer.

Usage

# Create an optimizer with the desired parameters.
opt = GradientDescentOptimizer(learning_rate=0.1)
# Add Ops to the graph to minimize a cost by updating a list of variables.
# "cost" is a Tensor, and the list of variables contains tf.Variable
# objects.
opt_op = opt.minimize(cost, var_list=<list of variables>)

In the training program you will just have to run the returned Op.

# Execute opt_op to do one step of training:
opt_op.run()

Processing gradients before applying them.

Calling minimize() takes care of both computing the gradients and applying them to the variables. If you want to process the gradients before applying them you can instead use the optimizer in three steps:

1. Compute the gradients with compute_gradients().
2. Process the gradients as you wish.
3. Apply the processed gradients with apply_gradients().

Example:

# Create an optimizer.
opt = GradientDescentOptimizer(learning_rate=0.1)

# Compute the gradients for a list of variables.
grads_and_vars = opt.compute_gradients(loss, <list of variables>)

# grads_and_vars is a list of tuples (gradient, variable).  Do whatever you
# need to the 'gradient' part, for example cap them, etc.
capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for gv in grads_and_vars]

# Ask the optimizer to apply the capped gradients.
opt.apply_gradients(capped_grads_and_vars)

Gating Gradients

Both minimize() and compute_gradients() accept a gate_gradients argument that controls the degree of parallelism during the application of the gradients.

The possible values are: GATE_NONE, GATE_OP, and GATE_GRAPH.

GATE_NONE: Compute and apply gradients in parallel. This provides the maximum parallelism in execution, at the cost of some non-reproducibility in the results. For example the two gradients of matmul depend on the input values: With GATE_NONE one of the gradients could be applied to one of the inputs before the other gradient is computed resulting in non-reproducible results.

GATE_OP: For each Op, make sure all gradients are computed before they are used. This prevents race conditions for Ops that generate gradients for multiple inputs where the gradients depend on the inputs.

GATE_GRAPH: Make sure all gradients for all variables are computed before any one of them is used. This provides the least parallelism but can be useful if you want to process all gradients before applying any of them.

Slots

Some optimizer subclasses, such as MomentumOptimizer and AdagradOptimizer allocate and manage additional variables associated with the variables to train. These are called Slots. Slots have names and you can ask the optimizer for the names of the slots that it uses. Once you have a slot name you can ask the optimizer for the variable it created to hold the slot value.

This can be useful if you want to log debug a training algorithm, report stats about the slots, etc.

Args
use_locking	Bool. If True apply use locks to prevent concurrent updates to variables.
name	A non-empty string. The name to use for accumulators created for the optimizer.

Raises
ValueError	If name is malformed.

Methods

apply_gradients

View source

apply_gradients(
    grads_and_vars,
    global_step=None,
    name=None,
    skip_gradients_aggregation=False
)

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.
skip_gradients_aggregation	If true, gradients aggregation will not be performed inside optimizer. Usually this arg is set to True when you write custom code aggregating gradients outside the optimizer.

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

Caution: This API was designed for TensorFlow v1. Continue reading for details on how to migrate from this API to a native TensorFlow v2 equivalent. See the TensorFlow v1 to TensorFlow v2 migration guide for instructions on how to migrate the rest of your code.

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