AMSGradStep¶
The AMSGrad optimizer step.
Based on Reddi et al, 2018, AMSGrad is a modification of the popular Adam optimizer, which improves the convergence of the algorithm (guarantees it) by using a long-term memory of past gradients. If Adam fails on a problem (e.g., diverges/explodes), AMSGrad is a useful thing to try. Like all step nodes, this node only processes gradients, and the resulting updates must be applied manually to the weights (this can be accomplished using the Add node). However, you can also pass it to the StepSolver node which implements the full optimization loop. The learning rate can instead be given as a schedule, by wiring one of the Schedule nodes into the learning_rate_schedule port. More Info... Version 0.2.0
Ports/Properties¶
gradients¶
Gradients to be transformed.
weights¶
Optional current weights.
state¶
Explicit state of the node.
learning_rate_schedule¶
Optional learning rate schedule.
learning_rate¶
Learning rate. A typical choice may be 0.001 here, but this is problem dependent. If a learning rate schedule is provided, this value should be left unspecified.
beta1¶
Exponential decay rate for the first moment estimates.
beta2¶
Exponential decay rate for the second moment estimates.
epsilon¶
Small value applied to the denominator outside the square root to avoid dividing by zero when rescaling. Note that larger epsilon values have been explored in the literature.
epsilon_inroot¶
Small value applied to the denominator inside the square root to avoid dividing by zero when rescaling. A case where this is needed is when differentiating the optimizer itself, eg for bilevel optimization.
mu_precision¶
Numeric precision for the first-order accumulator. Keep resolves to the precision of the inputs.
set_breakpoint¶
Set a breakpoint on this node. If this is enabled, your debugger (if one is attached) will trigger a breakpoint.
metadata¶
User-definable meta-data associated with the node. Usually reserved for technical purposes.