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Base class for Keras optimizers.
tf.keras.optimizers.Optimizer( name, **kwargs )
You should not use this class directly, but instead instantiate one of its subclasses such as tf.keras.optimizers.SGD
, tf.keras.optimizers.Adam
, etc.
# Create an optimizer with the desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is a callable that takes no argument and returns the value # to minimize. loss = lambda: 3 * var1 * var1 + 2 * var2 * var2 # In graph mode, returns op that minimizes the loss by updating the listed # variables. opt_op = opt.minimize(loss, var_list=[var1, var2]) opt_op.run() # In eager mode, simply call minimize to update the list of variables. opt.minimize(loss, var_list=[var1, var2])
In Keras models, sometimes variables are created when the model is first called, instead of construction time. Examples include 1) sequential models without input shape pre-defined, or 2) subclassed models. Pass var_list as callable in these cases.
opt = tf.keras.optimizers.SGD(learning_rate=0.1) model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden, activation='relu')) model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid')) loss_fn = lambda: tf.keras.losses.mse(model(input), output) var_list_fn = lambda: model.trainable_weights for input, output in data: opt.minimize(loss_fn, var_list_fn)
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:
tf.GradientTape
.apply_gradients()
.# Create an optimizer. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # Compute the gradients for a list of variables. with tf.GradientTape() as tape: loss = <call_loss_function> vars = <list_of_variables> grads = tape.gradient(loss, vars) # Process the gradients, for example cap them, etc. # capped_grads = [MyCapper(g) for g in grads] processed_grads = [process_gradient(g) for g in grads] # Ask the optimizer to apply the processed gradients. opt.apply_gradients(zip(processed_grads, var_list))
tf.distribute.Strategy
This optimizer class is tf.distribute.Strategy
aware, which means it automatically sums gradients across all replicas. To average gradients, you divide your loss by the global batch size, which is done automatically if you use tf.keras
built-in training or evaluation loops. See the reduction
argument of your loss which should be set to tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE
for averaging or tf.keras.losses.Reduction.SUM
for not.
To aggregate gradients yourself, call apply_gradients
with experimental_aggregate_gradients
set to False. This is useful if you need to process aggregated gradients.
If you are not using these and you want to average gradients, you should use tf.math.reduce_sum
to add up your per-example losses and then divide by the global batch size. Note that when using tf.distribute.Strategy
, the first component of a tensor's shape is the replica-local batch size, which is off by a factor equal to the number of replicas being used to compute a single step. As a result, using tf.math.reduce_mean
will give the wrong answer, resulting in gradients that can be many times too big.
All Keras optimizers respect variable constraints. If constraint function is passed to any variable, the constraint will be applied to the variable after the gradient has been applied to the variable. Important: If gradient is sparse tensor, variable constraint is not supported.
The entire optimizer is currently thread compatible, not thread-safe. The user needs to perform synchronization if necessary.
Many optimizer subclasses, such as Adam
and Adagrad
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.
These are arguments passed to the optimizer subclass constructor (the __init__
method), and then passed to self._set_hyper()
. They can be either regular Python values (like 1.0), tensors, or callables. If they are callable, the callable will be called during apply_gradients()
to get the value for the hyper parameter.
Hyperparameters can be overwritten through user code:
# Create an optimizer with the desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is a callable that takes no argument and returns the value # to minimize. loss = lambda: 3 * var1 + 2 * var2 # In eager mode, simply call minimize to update the list of variables. opt.minimize(loss, var_list=[var1, var2]) # update learning rate opt.learning_rate = 0.05 opt.minimize(loss, var_list=[var1, var2])
Optimizer accepts a callable learning rate in two ways. The first way is through built-in or customized tf.keras.optimizers.schedules.LearningRateSchedule
. The schedule will be called on each iteration with schedule(iteration)
, a tf.Variable
owned by the optimizer.
var = tf.Variable(np.random.random(size=(1,))) learning_rate = tf.keras.optimizers.schedules.ExponentialDecay( initial_learning_rate=.01, decay_steps=20, decay_rate=.1) opt = tf.keras.optimizers.SGD(learning_rate=learning_rate) loss = lambda: 3 * var opt.minimize(loss, var_list=[var]) <tf.Variable...
The second way is through a callable function that does not accept any arguments.
var = tf.Variable(np.random.random(size=(1,))) def lr_callable(): return .1 opt = tf.keras.optimizers.SGD(learning_rate=lr_callable) loss = lambda: 3 * var opt.minimize(loss, var_list=[var]) <tf.Variable...
If you intend to create your own optimization algorithm, simply inherit from this class and override the following methods:
_resource_apply_dense
(update variable given gradient tensor is dense)_resource_apply_sparse
(update variable given gradient tensor is sparse)_create_slots
(if your optimizer algorithm requires additional variables)get_config
(serialization of the optimizer, include all hyper parameters)Args | |
---|---|
name | A non-empty string. The name to use for accumulators created for the optimizer. |
**kwargs | keyword arguments. Allowed to be {clipnorm , clipvalue , lr , decay }. clipnorm is clip gradients by norm; clipvalue is clip gradients by value, decay is included for backward compatibility to allow time inverse decay of learning rate. lr is included for backward compatibility, recommended to use learning_rate instead. |
Raises | |
---|---|
ValueError | If name is malformed. |
Attributes | |
---|---|
iterations | Variable. The number of training steps this Optimizer has run. |
weights | Returns variables of this Optimizer based on the order created. |
add_slot
add_slot( var, slot_name, initializer='zeros' )
Add a new slot variable for var
.
add_weight
add_weight( name, shape, dtype=None, initializer='zeros', trainable=None, synchronization=tf.VariableSynchronization.AUTO, aggregation=tf.compat.v1.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
.
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. Default to the name passed to the Optimizer constructor. |
experimental_aggregate_gradients | Whether to sum gradients from different replicas in the presense 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. |
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.
Arguments | |
---|---|
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
@abc.abstractmethod 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
.
Arguments | |
---|---|
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.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) print('Training'); 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 )
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 | A callable taking no arguments which returns the value to minimize. |
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 name for the returned operation. |
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.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) print('Training'); 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>
Arguments | |
---|---|
weights | weight values as a list of numpy arrays. |
variables
variables()
Returns variables of this Optimizer based on the order created.
© 2020 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/versions/r2.3/api_docs/python/tf/keras/optimizers/Optimizer