ยป Keras API reference / Optimizers / Adagrad

Adagrad

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Adagrad class

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

Optimizer that implements the Adagrad algorithm.

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.

Arguments

  • 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 arguments are clipvalue, clipnorm, global_clipnorm. If clipvalue (float) is set, the gradient of each weight is clipped to be no higher than this value. If clipnorm (float) is set, the gradient of each weight is individually clipped so that its norm is no higher than this value. If global_clipnorm (float) is set the gradient of all weights is clipped so that their global norm is no higher than this value..

Reference