tf.train.AdamOptimizer
tf.train.AdamOptimizer
class tf.train.AdamOptimizer
Defined in tensorflow/python/training/adam.py
.
See the guide: Training > Optimizers
Optimizer that implements the Adam algorithm.
See Kingma et. al., 2014 (pdf).
Methods
__init__
__init__( learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, use_locking=False, name='Adam' )
Construct a new Adam optimizer.
Initialization:
m_0 <- 0 (Initialize initial 1st moment vector) v_0 <- 0 (Initialize initial 2nd moment vector) t <- 0 (Initialize timestep)
The update rule for variable
with gradient g
uses an optimization described at the end of section2 of the paper:
t <- t + 1 lr_t <- learning_rate * sqrt(1 - beta2^t) / (1 - beta1^t) m_t <- beta1 * m_{t-1} + (1 - beta1) * g v_t <- beta2 * v_{t-1} + (1 - beta2) * g * g variable <- variable - lr_t * m_t / (sqrt(v_t) + epsilon)
The default value of 1e-8 for epsilon might not be a good default in general. For example, when training an Inception network on ImageNet a current good choice is 1.0 or 0.1. Note that since AdamOptimizer uses the formulation just before Section 2.1 of the Kingma and Ba paper rather than the formulation in Algorithm 1, the "epsilon" referred to here is "epsilon hat" in the paper.
The sparse implementation of this algorithm (used when the gradient is an IndexedSlices object, typically because of tf.gather
or an embedding lookup in the forward pass) does apply momentum to variable slices even if they were not used in the forward pass (meaning they have a gradient equal to zero). Momentum decay (beta1) is also applied to the entire momentum accumulator. This means that the sparse behavior is equivalent to the dense behavior (in contrast to some momentum implementations which ignore momentum unless a variable slice was actually used).
Args:
-
learning_rate
: A Tensor or a floating point value. The learning rate. -
beta1
: A float value or a constant float tensor. The exponential decay rate for the 1st moment estimates. -
beta2
: A float value or a constant float tensor. The exponential decay rate for the 2nd moment estimates. -
epsilon
: A small constant for numerical stability. This epsilon is "epsilon hat" in the Kingma and Ba paper (in the formula just before Section 2.1), not the epsilon in Algorithm 1 of the paper. -
use_locking
: If True use locks for update operations. -
name
: Optional name for the operations created when applying gradients. Defaults to "Adam".
apply_gradients
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.
Args:
-
grads_and_vars
: List of (gradient, variable) pairs as returned bycompute_gradients()
. -
global_step
: OptionalVariable
to increment by one after the variables have been updated. -
name
: Optional name for the returned operation. Default to the name passed to theOptimizer
constructor.
Returns:
An Operation
that applies the specified gradients. If global_step
was not None, that operation also increments global_step
.
Raises:
-
TypeError
: Ifgrads_and_vars
is malformed. -
ValueError
: If none of the variables have gradients.
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
.
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.
Args:
-
loss
: A Tensor containing the value to minimize. -
var_list
: Optional list or tuple oftf.Variable
to update to minimizeloss
. Defaults to the list of variables collected in the graph under the keyGraphKey.TRAINABLE_VARIABLES
. -
gate_gradients
: How to gate the computation of gradients. Can beGATE_NONE
,GATE_OP
, orGATE_GRAPH
. -
aggregation_method
: Specifies the method used to combine gradient terms. Valid values are defined in the classAggregationMethod
. -
colocate_gradients_with_ops
: If True, try colocating gradients with the corresponding op. -
grad_loss
: Optional. ATensor
holding the gradient computed forloss
.
Returns:
A list of (gradient, variable) pairs. Variable is always present, but gradient can be None
.
Raises:
-
TypeError
: Ifvar_list
contains anything else thanVariable
objects. -
ValueError
: If some arguments are invalid.
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 tominimize()
orapply_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
: ATensor
containing the value to minimize. -
global_step
: OptionalVariable
to increment by one after the variables have been updated. -
var_list
: Optional list or tuple ofVariable
objects to update to minimizeloss
. Defaults to the list of variables collected in the graph under the keyGraphKeys.TRAINABLE_VARIABLES
. -
gate_gradients
: How to gate the computation of gradients. Can beGATE_NONE
,GATE_OP
, orGATE_GRAPH
. -
aggregation_method
: Specifies the method used to combine gradient terms. Valid values are defined in the classAggregationMethod
. -
colocate_gradients_with_ops
: If True, try colocating gradients with the corresponding op. -
name
: Optional name for the returned operation. -
grad_loss
: Optional. ATensor
holding the gradient computed forloss
.
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 notVariable
objects.
Class Members
GATE_GRAPH
GATE_NONE
GATE_OP
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/train/AdamOptimizer