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Optimizer that implements the Adam algorithm.
Inherits From: Optimizer
tf.keras.optimizers.legacy.Adam(
learning_rate=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-07,
amsgrad=False,
name='Adam',
**kwargs
)
Adam optimization is a stochastic gradient descent method that is based on adaptive estimation of first-order and second-order moments.
According to Kingma et al., 2014, the method is "computationally efficient, has little memory requirement, invariant to diagonal rescaling of gradients, and is well suited for problems that are large in terms of data/parameters".
Args | |
---|---|
learning_rate
|
A Tensor , floating point value, or a schedule that is a
tf.keras.optimizers.schedules.LearningRateSchedule , or a callable
that takes no arguments and returns the actual value to use, The
learning rate. Defaults to 0.001 .
|
beta_1
|
A float value or a constant float tensor, or a callable
that takes no arguments and returns the actual value to use. The
exponential decay rate for the 1st moment estimates. Defaults to 0.9 .
|
beta_2
|
A float value or a constant float tensor, or a callable
that takes no arguments and returns the actual value to use, The
exponential decay rate for the 2nd moment estimates. Defaults to
0.999 .
|
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. Defaults to
1e-7 .
|
amsgrad
|
Boolean. Whether to apply AMSGrad variant of this algorithm from
the paper "On the Convergence of Adam and beyond". Defaults to False .
|
name
|
Optional name for the operations created when applying gradients.
Defaults to "Adam" .
|
**kwargs
|
keyword arguments. Allowed arguments are clipvalue ,
clipnorm , global_clipnorm .
If clipvalue (float) is set, the gradient of each weight
is clipped to be no higher than this value.
If clipnorm (float) is set, the gradient of each weight
is individually clipped so that its norm is no higher than this value.
If global_clipnorm (float) is set the gradient of all weights is
clipped so that their global norm is no higher than this value.
|
Usage:
opt = tf.keras.optimizers.legacy.Adam(learning_rate=0.1)
var1 = tf.Variable(10.0)
loss = lambda: (var1 ** 2)/2.0 # d(loss)/d(var1) == var1
step_count = opt.minimize(loss, [var1]).numpy()
# The first step is `-learning_rate*sign(grad)`
var1.numpy()
9.9
Reference | |
---|---|
|
Notes:
The default value of 1e-7 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 Adam 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).
Raises | |
---|---|
ValueError
|
in case of any invalid argument. |
Attributes | |
---|---|
clipnorm
|
float or None . If set, clips gradients to a maximum norm.
|
clipvalue
|
float or None . If set, clips gradients to a maximum value.
|
global_clipnorm
|
float or None .
If set, clips gradients to a maximum norm. Check |
iterations
|
Variable. The number of training steps this Optimizer has run. |
weights
|
Returns variables of this Optimizer based on the order created. |
Methods
add_slot
add_slot(
var, slot_name, initializer='zeros', shape=None
)
Add a new slot variable for var
.
A slot variable is an additional variable associated with var
to
train. It is allocated and managed by optimizers, e.g. Adam
.
Args | |
---|---|
var
|
a Variable object.
|
slot_name
|
name of the slot variable. |
initializer
|
initializer of the slot variable |
shape
|
(Optional) shape of the slot variable. If not set, it will
default to the shape of var .
|
Returns | |
---|---|
A slot variable. |
add_weight
add_weight(
name,
shape,
dtype=None,
initializer='zeros',
trainable=None,
synchronization=tf.VariableSynchronization.AUTO
,
aggregation=tf.VariableAggregation.NONE
)
apply_gradients
apply_gradients(
grads_and_vars, name=None, experimental_aggregate_gradients=True
)
Apply gradients to variables.
This is the second part of minimize()
. It returns an Operation
that
applies gradients.
The method sums gradients from all replicas in the presence of
tf.distribute.Strategy
by default. You can aggregate gradients
yourself by passing experimental_aggregate_gradients=False
.
Example:
grads = tape.gradient(loss, vars)
grads = tf.distribute.get_replica_context().all_reduce('sum', grads)
# Processing aggregated gradients.
optimizer.apply_gradients(zip(grads, vars),
experimental_aggregate_gradients=False)
Args | |
---|---|
grads_and_vars
|
List of (gradient, variable) pairs. |
name
|
Optional name for the returned operation. When None , uses the
name passed to the Optimizer constructor. Defaults to None .
|
experimental_aggregate_gradients
|
Whether to sum gradients from
different replicas in the presence of tf.distribute.Strategy . If
False, it's user responsibility to aggregate the gradients. Default
to True .
|
Returns | |
---|---|
An Operation that applies the specified gradients. The iterations
will be automatically increased by 1.
|
Raises | |
---|---|
TypeError
|
If grads_and_vars is malformed.
|
ValueError
|
If none of the variables have gradients. |
RuntimeError
|
If called in a cross-replica context. |
from_config
@classmethod
from_config( config, custom_objects=None )
Creates an optimizer from its config.
This method is the reverse of get_config
,
capable of instantiating the same optimizer from the config
dictionary.
Args | |
---|---|
config
|
A Python dictionary, typically the output of get_config. |
custom_objects
|
A Python dictionary mapping names to additional Python objects used to create this optimizer, such as a function used for a hyperparameter. |
Returns | |
---|---|
An optimizer instance. |
get_config
get_config()
Returns the config of the optimizer.
An optimizer config is a Python dictionary (serializable) containing the configuration of an optimizer. The same optimizer can be reinstantiated later (without any saved state) from this configuration.
Returns | |
---|---|
Python dictionary. |
get_gradients
get_gradients(
loss, params
)
Returns gradients of loss
with respect to params
.
Should be used only in legacy v1 graph mode.
Args | |
---|---|
loss
|
Loss tensor. |
params
|
List of variables. |
Returns | |
---|---|
List of gradient tensors. |
Raises | |
---|---|
ValueError
|
In case any gradient cannot be computed (e.g. if gradient function not implemented). |
get_slot
get_slot(
var, slot_name
)
get_slot_names
get_slot_names()
A list of names for this optimizer's slots.
get_updates
get_updates(
loss, params
)
get_weights
get_weights()
Returns the current weights of the optimizer.
The weights of an optimizer are its state (ie, variables). This function returns the weight values associated with this optimizer as a list of Numpy arrays. The first value is always the iterations count of the optimizer, followed by the optimizer's state variables in the order they were created. The returned list can in turn be used to load state into similarly parameterized optimizers.
For example, the RMSprop optimizer for this simple model returns a list of three values-- the iteration count, followed by the root-mean-square value of the kernel and bias of the single Dense layer:
opt = tf.keras.optimizers.legacy.RMSprop()
m = tf.keras.models.Sequential([tf.keras.layers.Dense(10)])
m.compile(opt, loss='mse')
data = np.arange(100).reshape(5, 20)
labels = np.zeros(5)
results = m.fit(data, labels) # Training.
len(opt.get_weights())
3
Returns | |
---|---|
Weights values as a list of numpy arrays. |
minimize
minimize(
loss, var_list, grad_loss=None, name=None, tape=None
)
Minimize loss
by updating var_list
.
This method simply computes gradient using tf.GradientTape
and calls
apply_gradients()
. If you want to process the gradient before applying
then call tf.GradientTape
and apply_gradients()
explicitly instead
of using this function.
Args | |
---|---|
loss
|
Tensor or callable. If a callable, loss should take no
arguments and return the value to minimize. If a Tensor , the
tape argument must be passed.
|
var_list
|
list or tuple of Variable objects to update to minimize
loss , or a callable returning the list or tuple of Variable
objects. Use callable when the variable list would otherwise be
incomplete before minimize since the variables are created at the
first time loss is called.
|
grad_loss
|
(Optional). A Tensor holding the gradient computed for
loss .
|
name
|
(Optional) str. Name for the returned operation. |
tape
|
(Optional) tf.GradientTape . If loss is provided as a
Tensor , the tape that computed the loss must be provided.
|
Returns | |
---|---|
An Operation that updates the variables in var_list . The
iterations will be automatically increased by 1.
|
Raises | |
---|---|
ValueError
|
If some of the variables are not Variable objects.
|
set_weights
set_weights(
weights
)
Set the weights of the optimizer.
The weights of an optimizer are its state (ie, variables). This function takes the weight values associated with this optimizer as a list of Numpy arrays. The first value is always the iterations count of the optimizer, followed by the optimizer's state variables in the order they are created. The passed values are used to set the new state of the optimizer.
For example, the RMSprop optimizer for this simple model takes a list of three values-- the iteration count, followed by the root-mean-square value of the kernel and bias of the single Dense layer:
opt = tf.keras.optimizers.legacy.RMSprop()
m = tf.keras.models.Sequential([tf.keras.layers.Dense(10)])
m.compile(opt, loss='mse')
data = np.arange(100).reshape(5, 20)
labels = np.zeros(5)
results = m.fit(data, labels) # Training.
new_weights = [np.array(10), np.ones([20, 10]), np.zeros([10])]
opt.set_weights(new_weights)
opt.iterations
<tf.Variable 'RMSprop/iter:0' shape=() dtype=int64, numpy=10>
Args | |
---|---|
weights
|
weight values as a list of numpy arrays. |
variables
variables()
Returns variables of this Optimizer based on the order created.