tf.keras.optimizers.Adagrad

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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
)

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.