Adam

class ivy.neural_net_stateful.optimizers.Adam(lr=0.0001, beta1=0.9, beta2=0.999, epsilon=1e-07, inplace=True, stop_gradients=True, dev_str=None)[source]

Bases: ivy.neural_net_stateful.optimizers.Optimizer

__init__(lr=0.0001, beta1=0.9, beta2=0.999, epsilon=1e-07, inplace=True, stop_gradients=True, dev_str=None)[source]

Construct an ADAM optimizer.

Parameters
  • lr (float, optional) – Learning rate, default is 1e-4.

  • beta1 (float, optional) – gradient forgetting factor, default is 0.9

  • beta2 (float, optional) – second moment of gradient forgetting factor, default is 0.999

  • epsilon (float, optional) – divisor during adam update, preventing division by zero, default is 1e-07

  • inplace (bool, optional) – Whether to update the variables in-place, or to create new variable handles. This is only relevant for frameworks with stateful variables such as PyTorch. Default is True.

  • stop_gradients (bool, optional) – Whether to stop the gradients of the variables after each gradient step. Default is True.

set_state(state)[source]

Set state of the optimizer.

Parameters

state (Ivy container of state tensors) – Nested state to update.

property state

Supported Frameworks:

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