# tf.train.GradientDescentOptimizer
|---|
| [ View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/training/gradient_descent.py#L30-L82) |
Optimizer that implements the gradient descent algorithm.
Inherits From: [`Optimizer`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/train/Optimizer)
#### View aliases
**Compat aliases for migration**
See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.
[`tf.compat.v1.train.GradientDescentOptimizer`](https://www.tensorflow.org/api_docs/python/tf/compat/v1/train/GradientDescentOptimizer)
tf.train.GradientDescentOptimizer(
learning_rate, use_locking=False, name='GradientDescent'
)
| ## Args ||
|---|---|
| `learning_rate` | A Tensor or a floating point value. The learning rate to use. |
| `use_locking` | If True use locks for update operations. |
| `name` | Optional name prefix for the operations created when applying gradients. Defaults to "GradientDescent". |
## Methods
### `apply_gradients`
[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/training/optimizer.py#L531-L638)
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.
| 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](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/training/optimizer.py#L415-L519)
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`.
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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/Variable) to update to minimize `loss`. Defaults to the list of variables collected in the graph under the key [`GraphKeys.TRAINABLE_VARIABLES`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/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.
### `get_name`
[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/training/optimizer.py#L352-L353)
get_name()
### `get_slot`
[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/training/optimizer.py#L735-L771)
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](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/training/optimizer.py#L773-L781)
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](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/training/optimizer.py#L355-L413)
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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/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](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/training/optimizer.py#L783-L809)
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`