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Base class for optimizers.
tf.compat.v1.train.Optimizer(
use_locking, name
)
Migrate to TF2
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, whether it
is used with or without a tf.estimator.Estimator
, you cannot simply replace
that with the corresponding tf.keras.optimizers.Optimizer
s. To migrate to
TF2, it is advised the whole training program used with Estimator
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:
- Compute the gradients with
compute_gradients()
. - Process the gradients as you wish.
- 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
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
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
get_name()
get_slot
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
get_slot_names()
Return a list of the names of slots created by the Optimizer
.
See get_slot()
.
Returns | |
---|---|
A list of strings. |
minimize
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
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
|