tf.keras.optimizers.Adagrad

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	Initial value for the learning rate: either a floating point value, or a tf.keras.optimizers.schedules.LearningRateSchedule instance. Defaults to 0.001. Note that Adagrad tends to benefit from higher initial learning rate values compared to other optimizers. To match the exact form in the original paper, use 1.0.
initial_accumulator_value	Floating point value. Starting value for the accumulators (per-parameter momentum values). Must be non-negative.
epsilon	Small floating point value used to maintain numerical stability.
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 and represents the maximum L2 norm of each weight variable; "clipvalue" (float) clips gradient by value and represents the maximum absolute value of each weight variable.

Reference:

• Duchi et al., 2011.

Raises
ValueError	in case of any invalid argument.