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tf.keras.optimizers.Adam

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Class Adam

Optimizer that implements the Adam algorithm.

Inherits From: Optimizer

Aliases:

  • Class tf.compat.v1.keras.optimizers.Adam
  • Class tf.compat.v2.keras.optimizers.Adam
  • Class tf.compat.v2.optimizers.Adam

Adam optimization is a stochastic gradient descent method that is based on adaptive estimation of first-order and second-order moments. According to the paper Adam: A Method for Stochastic Optimization. Kingma et al., 2014, the method is "computationally efficient, has little memory requirement, invariant to diagonal rescaling of gradients, and is well suited for problems that are large in terms of data/parameters".

For AMSGrad see On The Convergence Of Adam And Beyond. Reddi et al., 5-8.

__init__

View source

__init__(
    learning_rate=0.001,
    beta_1=0.9,
    beta_2=0.999,
    epsilon=1e-07,
    amsgrad=False,
    name='Adam',
    **kwargs
)

Construct a new Adam optimizer.

If amsgrad = False: Initialization:

\(m_0 := 0 \text{(Initialize initial 1st moment vector)}\)
\(v_0 := 0 \text{(Initialize initial 2nd moment vector)}\)
\(t := 0 \text{(Initialize timestep)}\)

The update rule for variable with gradient g uses an optimization described at the end of section 2 of the paper:

\(t := t + 1\)
\(lr_t := \text{learning\_rate} * \sqrt{1 - beta_2^t} / (1 - beta_1^t)\)

\(m_t := beta_1 * m_{t-1} + (1 - beta_1) * g\)
\(v_t := beta_2 * v_{t-1} + (1 - beta_2) * g * g\)
\(variable := variable - lr_t * m_t / (\sqrt{v_t} + \epsilon)\)

If amsgrad = True: Initialization:

\(m_0 := 0 \text{(Initialize initial 1st moment vector)}\)
\(v_0 := 0 \text{(Initialize initial 2nd moment vector)}\)
\(v_hat_0 := 0 \text{(Initialize initial 2nd moment vector)}\)
\(t := 0 \text{(Initialize timestep)}\)

The update rule for variable with gradient g uses an optimization described at the end of section 2 of the paper:

\(t := t + 1\)
\(lr_t := \text{learning\_rate} * \sqrt{1 - beta_2^t} / (1 - beta_1^t)\)

\(m_t := beta_1 * m_{t-1} + (1 - beta_1) * g\)
\(v_t := beta_2 * v_{t-1} + (1 - beta_2) * g * g\)
\(v_hat_t := max(v_hat_{t-1}, v_t) \)
variable := variable - lr_t * m_t / (\sqrt{v_hat_t} + \epsilon)$$

The default value of 1e-7 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.
  • beta_1: A float value or a constant float tensor. The exponential decay rate for the 1st moment estimates.
  • beta_2: 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.
  • amsgrad: boolean. Whether to apply AMSGrad variant of this algorithm from the paper "On the Convergence of Adam and beyond".
  • name: Optional name for the operations created when applying gradients. Defaults to "Adam". @compatibility(eager) When eager execution is enabled, learning_rate, beta_1, beta_2, and epsilon can each be a callable that takes no arguments and returns the actual value to use. This can be useful for changing these values across different invocations of optimizer functions. @end_compatibility
  • **kwargs: keyword arguments. Allowed to be {clipnorm, clipvalue, lr, decay}. clipnorm is clip gradients by norm; clipvalue is clip gradients by value, decay is included for backward compatibility to allow time inverse decay of learning rate. lr is included for backward compatibility, recommended to use learning_rate instead.

Properties

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

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add_slot(
    var,
    slot_name,
    initializer='zeros'
)

Add a new slot variable for var.

add_weight

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

apply_gradients

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apply_gradients(
    grads_and_vars,
    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.
  • 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.

from_config

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from_config(
    cls,
    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.

Arguments:

  • 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()

get_gradients

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get_gradients(
    loss,
    params
)

Returns gradients of loss with respect to params.

Arguments:

  • 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

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get_slot(
    var,
    slot_name
)

get_slot_names

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get_slot_names()

A list of names for this optimizer's slots.

get_updates

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get_updates(
    loss,
    params
)

get_weights

View source

get_weights()

minimize

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minimize(
    loss,
    var_list,
    grad_loss=None,
    name=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: A callable taking no arguments which returns the value to minimize.
  • 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 name for the returned operation.

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.

set_weights

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set_weights(weights)

variables

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variables()

Returns variables of this Optimizer based on the order created.