Class AdamOptimizer

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Inherits From: Optimizer

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Defined in tensorflow/python/training/adam.py.

See the guide: Training > Optimizers

Optimizer that implements the Adam algorithm.

See Kingma et al., 2014 (pdf).

Methods

__init__

Construct a new Adam optimizer.

Initialization:

The update rule for variable with gradient g uses an optimization described at the end of section2 of the paper:

The default value of 1e-8 for epsilon might not be a good default in general. For example, when training an Inception network on ImageNet a current good choice is 1.0 or 0.1. Note that since AdamOptimizer uses the formulation just before Section 2.1 of the Kingma and Ba paper rather than the formulation in Algorithm 1, the 'epsilon' referred to here is 'epsilon hat' in the paper.

The sparse implementation of this algorithm (used when the gradient is an IndexedSlices object, typically because of tf.gather or an embedding lookup in the forward pass) does apply momentum to variable slices even if they were not used in the forward pass (meaning they have a gradient equal to zero). Momentum decay (beta1) is also applied to the entire momentum accumulator. This means that the sparse behavior is equivalent to the dense behavior (in contrast to some momentum implementations which ignore momentum unless a variable slice was actually used).

Args:

• learning_rate: A Tensor or a floating point value. The learning rate.
• beta1: A float value or a constant float tensor. The exponential decay rate for the 1st moment estimates.
• beta2: A float value or a constant float tensor. The exponential decay rate for the 2nd moment estimates.
• epsilon: A small constant for numerical stability. This epsilon is 'epsilon hat' in the Kingma and Ba paper (in the formula just before Section 2.1), not the epsilon in Algorithm 1 of the paper.
• use_locking: If True use locks for update operations.
• name: Optional name for the operations created when applying gradients. Defaults to 'Adam'.

apply_gradients

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

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.

get_name

get_slot

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

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

See get_slot().

Returns:

A list of strings.

minimize

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 elements of var_list as arguments and computes the value to be minimized. If var_list is None, loss should take no arguments. 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

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 Members

GATE_GRAPH

GATE_NONE

GATE_OP

© 2018 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/api_docs/python/tf/train/AdamOptimizer

You’re browsing the documentation for v2.x and earlier. For v3.x, click here.

This page assumes you’ve already read the Components Basics. Read that first if you are new to components.

In 2.6.0, we introduced a new unified syntax (the v-slot directive) for named and scoped slots. It replaces the slot and slot-scope attributes, which are now deprecated, but have not been removed and are still documented here. The rationale for introducing the new syntax is described in this RFC.

Slot Content

Vue implements a content distribution API inspired by the Web Components spec draft, using the <slot> element to serve as distribution outlets for content.

This allows you to compose components like this:

Then in the template for <navigation-link>, you might have:

When the component renders, <slot></slot> will be replaced by “Your Profile”. Slots can contain any template code, including HTML:

Or even other components:

If <navigation-link>‘s template did not contain a <slot> element, any content provided between its opening and closing tag would be discarded.

Compilation Scope

When you want to use data inside a slot, such as in:

Get Slotted

That slot has access to the same instance properties (i.e. the same “scope”) as the rest of the template. The slot does not have access to <navigation-link>‘s scope. For example, trying to access url would not work:

As a rule, remember that:

Everything in the parent template is compiled in parent scope; everything in the child template is compiled in the child scope.

Fallback Content

There are cases when it’s useful to specify fallback (i.e. default) content for a slot, to be rendered only when no content is provided. For example, in a <submit-button> component:

We might want the text “Submit” to be rendered inside the <button> most of the time. To make “Submit” the fallback content, we can place it in between the <slot> tags:

Now when we use <submit-button> in a parent component, providing no content for the slot:

will render the fallback content, “Submit”:

But if we provide content:

Then the provided content will be rendered instead:

Named Slots

Updated in 2.6.0+. See here for the deprecated syntax using the slot attribute.

There are times when it’s useful to have multiple slots. For example, in a <base-layout> component with the following template:

For these cases, the <slot> element has a special attribute, name, which can be used to define additional slots:

A <slot> outlet without name implicitly has the name “default”.

To provide content to named slots, we can use the v-slot directive on a <template>, providing the name of the slot as v-slot‘s argument:

Now everything inside the <template> elements will be passed to the corresponding slots. Any content not wrapped in a <template> using v-slot is assumed to be for the default slot.

However, you can still wrap default slot content in a <template> if you wish to be explicit:

Either way, the rendered HTML will be:

Note that v-slot can only be added to a <template> (with one exception), unlike the deprecated slot attribute.

Scoped Slots

Updated in 2.6.0+. See here for the deprecated syntax using the slot-scope attribute.

Sometimes, it’s useful for slot content to have access to data only available in the child component. For example, imagine a <current-user> component with the following template:

We might want to replace this fallback content to display the user’s first name, instead of last, like this:

That won’t work, however, because only the <current-user> component has access to the user and the content we’re providing is rendered in the parent.

To make user available to the slot content in the parent, we can bind user as an attribute to the <slot> element:

Attributes bound to a <slot> element are called slot props. Now, in the parent scope, we can use v-slot with a value to define a name for the slot props we’ve been provided:

In this example, we’ve chosen to name the object containing all our slot props slotProps, but you can use any name you like.

Abbreviated Syntax for Lone Default Slots

In cases like above, when only the default slot is provided content, the component’s tags can be used as the slot’s template. This allows us to use v-slot directly on the component:

This can be shortened even further. Just as non-specified content is assumed to be for the default slot, v-slot without an argument is assumed to refer to the default slot:

Note that the abbreviated syntax for default slot cannot be mixed with named slots, as it would lead to scope ambiguity:

Whenever there are multiple slots, use the full <template> based syntax for all slots:

Destructuring Slot Props

Internally, scoped slots work by wrapping your slot content in a function passed a single argument:

That means the value of v-slot can actually accept any valid JavaScript expression that can appear in the argument position of a function definition. So in supported environments (single-file components or modern browsers), you can also use ES2015 destructuring to pull out specific slot props, like so:

This can make the template much cleaner, especially when the slot provides many props. It also opens other possibilities, such as renaming props, e.g. user to person:

You can even define fallbacks, to be used in case a slot prop is undefined:

Dynamic Slot Names

New in 2.6.0+

Dynamic directive arguments also work on v-slot, allowing the definition of dynamic slot names:

Named Slots Shorthand

New in 2.6.0+

Similar to v-on and v-bind, v-slot also has a shorthand, replacing everything before the argument (v-slot:) with the special symbol #. For example, v-slot:header can be rewritten as #header:

However, just as with other directives, the shorthand is only available when an argument is provided. That means the following syntax is invalid:

Instead, you must always specify the name of the slot if you wish to use the shorthand:

Other Examples

Slot props allow us to turn slots into reusable templates that can render different content based on input props. This is most useful when you are designing a reusable component that encapsulates data logic while allowing the consuming parent component to customize part of its layout.

For example, we are implementing a <todo-list> component that contains the layout and filtering logic for a list:

Instead of hard-coding the content for each todo, we can let the parent component take control by making every todo a slot, then binding todo as a slot prop:

Now when we use the <todo-list> component, we can optionally define an alternative <template> for todo items, but with access to data from the child:

However, even this barely scratches the surface of what scoped slots are capable of. For real-life, powerful examples of scoped slot usage, we recommend browsing libraries such as Vue Virtual Scroller, Vue Promised, and Portal Vue.

Deprecated Syntax

The v-slot directive was introduced in Vue 2.6.0, offering an improved, alternative API to the still-supported slot and slot-scope attributes. The full rationale for introducing v-slot is described in this RFC. The slot and slot-scope attributes will continue to be supported in all future 2.x releases, but are officially deprecated and will eventually be removed in Vue 3.

Named Slots with the slot Attribute

Deprecated in 2.6.0+. See here for the new, recommended syntax.

To pass content to named slots from the parent, use the special slot attribute on <template> (using the <base-layout> component described here as example):

Get Slotted Slot Cars

Or, the slot attribute can also be used directly on a normal element:

There can still be one unnamed slot, which is the default slot that serves as a catch-all for any unmatched content. In both examples above, the rendered HTML would be:

Scoped Slots with the slot-scope Attribute

Deprecated in 2.6.0+. See here for the new, recommended syntax.

To receive props passed to a slot, the parent component can use <template> with the slot-scope attribute (using the <slot-example> described here as example):

Here, slot-scope declares the received props object as the slotProps variable, and makes it available inside the <template> scope. You can name slotProps anything you like similar to naming function arguments in JavaScript.

Here slot='default' can be omitted as it is implied:

The slot-scope attribute can also be used directly on a non-<template> element (including components):

The value of slot-scope can accept any valid JavaScript expression that can appear in the argument position of a function definition. This means in supported environments (single-file components or modern browsers) you can also use ES2015 destructuring in the expression, like so:

Get Slotted Meaning

Using the <todo-list> described here as an example, here’s the equivalent usage using slot-scope: