tf.keras.optimizers.legacy.SGD

Gradient descent (with momentum) optimizer.

Inherits From: Optimizer

tf.keras.optimizers.legacy.SGD(
    learning_rate=0.01,
    momentum=0.0,
    nesterov=False,
    name='SGD',
    **kwargs
)

Update rule for parameter w with gradient g when momentum=0:

w = w - learning_rate * g

Update rule when momentum is larger than 0:

velocity = momentum * velocity - learning_rate * g
w = w + velocity

When nesterov=True, this rule becomes:

velocity = momentum * velocity - learning_rate * g
w = w + momentum * velocity - learning_rate * g

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 `0.01`.
`momentum`	float hyperparameter >= 0 that accelerates gradient descent in the relevant direction and dampens oscillations. Vanilla gradient descent means no momentum. Defaults to `0.`.
`nesterov`	boolean. Whether to apply Nesterov momentum. Defaults to `False`.
`name`	Optional name prefix for the operations created when applying gradients. Defaults to `"SGD"`.
`**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.SGD(learning_rate=0.1)
var = tf.Variable(1.0)
loss = lambda: (var ** 2)/2.0         # d(loss)/d(var1) = var1
step_count = opt.minimize(loss, [var]).numpy()
# Step is `- learning_rate * grad`
var.numpy()
0.9

opt = tf.keras.optimizers.legacy.SGD(learning_rate=0.1, momentum=0.9)
var = tf.Variable(1.0)
val0 = var.value()
loss = lambda: (var ** 2)/2.0         # d(loss)/d(var1) = var1
# First step is `- learning_rate * grad`
step_count = opt.minimize(loss, [var]).numpy()
val1 = var.value()
(val0 - val1).numpy()
0.1
# On later steps, step-size increases because of momentum
step_count = opt.minimize(loss, [var]).numpy()
val2 = var.value()
(val1 - val2).numpy()
0.18

Reference
For `nesterov=True`, See Sutskever et al., 2013.

Reference

For nesterov=True, See Sutskever et al., 2013.

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 `tf.clip_by_global_norm` for more details.
`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`

View source

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`

View source

add_weight(
    name,
    shape,
    dtype=None,
    initializer='zeros',
    trainable=None,
    synchronization=tf.VariableSynchronization.AUTO,
    aggregation=tf.VariableAggregation.NONE
)

`apply_gradients`

View source

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`

View source

@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`

View source

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`

View source

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`

View source

get_slot(
    var, slot_name
)

`get_slot_names`

View source

get_slot_names()

A list of names for this optimizer's slots.

`get_updates`

View source

get_updates(
    loss, params
)

`get_weights`

View source

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`

View source

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`

View source

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`

View source

variables()

Returns variables of this Optimizer based on the order created.

tf.keras.optimizers.legacy.SGD

Args

Usage:

Reference

Raises

Attributes

Methods

add_slot

add_weight

apply_gradients

Example:

from_config

get_config

get_gradients

get_slot

get_slot_names

get_updates

get_weights

minimize

set_weights

variables

`add_slot`

`add_weight`

`apply_gradients`

`from_config`

`get_config`

`get_gradients`

`get_slot`

`get_slot_names`

`get_updates`

`get_weights`

`minimize`

`set_weights`

`variables`