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tf.compat.v1.train.MomentumOptimizer

Optimizer that implements the Momentum algorithm.

Inherits From: `Optimizer`

Computes (if `use_nesterov = False`):

``````accumulation = momentum * accumulation + gradient
variable -= learning_rate * accumulation
``````

Note that in the dense version of this algorithm, `accumulation` is updated and applied regardless of a gradient's value, whereas the sparse version (when the gradient is an `IndexedSlices`, typically because of `tf.gather` or an embedding) only updates variable slices and corresponding `accumulation` terms when that part of the variable was used in the forward pass.

`learning_rate` A `Tensor` or a floating point value. The learning rate.
`momentum` A `Tensor` or a floating point value. The momentum.
`use_locking` If `True` use locks for update operations.
`name` Optional name prefix for the operations created when applying gradients. Defaults to "Momentum".
`use_nesterov` If `True` use Nesterov Momentum. See (Sutskever et al., 2013). This implementation always computes gradients at the value of the variable(s) passed to the optimizer. Using Nesterov Momentum makes the variable(s) track the values called `theta_t + mu*v_t` in the paper. This implementation is an approximation of the original formula, valid for high values of momentum. It will compute the "adjusted gradient" in NAG by assuming that the new gradient will be estimated by the current average gradient plus the product of momentum and the change in the average gradient.

Methods

`apply_gradients`

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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 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`

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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 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.

eager compatibility

When eager execution is enabled, `gate_gradients`, `aggregation_method`, and `colocate_gradients_with_ops` are ignored.

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`get_slot`

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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`

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

See `get_slot()`.

Returns
A list of strings.

`minimize`

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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`

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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.

GATE_GRAPH `2`
GATE_NONE `0`
GATE_OP `1`

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