tf.compat.v1.train.Optimizer

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.Optimizers. 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`

View source

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.

@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.

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`

View source

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`

View source

get_name()

`get_slot`

View source

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`

View source

get_slot_names()

Return a list of the names of slots created by the Optimizer.

See get_slot().

Returns
A list of strings.

`minimize`

View source

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`

View source

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`

tf.compat.v1.train.Optimizer

Migrate to TF2

Structural Mapping to Native TF2

How to Map Arguments

Before & After Usage Example

Description

Usage

Processing gradients before applying them.

Example:

Gating Gradients

Slots

Args

Raises

Methods

apply_gradients

How to Map Arguments

compute_gradients

Migrate to TF2

Description

get_name

get_slot

get_slot_names

minimize

eager compatibility

variables

Class Variables

`apply_gradients`

`compute_gradients`

`get_name`

`get_slot`

`get_slot_names`

`minimize`

`variables`