tf.keras.optimizers

TensorFlow 1 version

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Optimizer that implements the Adagrad algorithm.

Inherits From: Optimizer

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Main aliases

tf.optimizers.Adagrad

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.optimizers.Adagrad

tf.keras.optimizers.Adagrad(
    learning_rate=0.001, initial_accumulator_value=0.1, epsilon=1e-07,
    name='Adagrad', **kwargs
)

Used in the notebooks

Used in the tutorials
Building deep retrieval models Multi-task recommenders Creating Keras Models with TFL Layers Taking advantage of context features Deep & Cross Network (DCN)

Adagrad is an optimizer with parameter-specific learning rates, which are adapted relative to how frequently a parameter gets updated during training. The more updates a parameter receives, the smaller the updates.

Args
`learning_rate`	A `Tensor`, floating point value, or a schedule that is a `tf.keras.optimizers.schedules.LearningRateSchedule`. The learning rate.
`initial_accumulator_value`	A floating point value. Starting value for the accumulators, must be non-negative.
`epsilon`	A small floating point value to avoid zero denominator.
`name`	Optional name prefix for the operations created when applying gradients. Defaults to `"Adagrad"`.
`**kwargs`	Keyword arguments. Allowed to be one of `"clipnorm"` or `"clipvalue"`. `"clipnorm"` (float) clips gradients by norm; `"clipvalue"` (float) clips gradients by value.

Reference:

Duchi et al., 2011.

Raises
`ValueError`	in case of any invalid argument.

TensorFlow

tf

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tf.keras / optimizers / optimizers.Overview

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Used in the notebooks

Args

Reference:

Raises