ยป Keras API reference / Optimizers / Adagrad

Adagrad

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 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