SGDStep¶
The Stochastic Gradient Descent (SGD) optimizer step.
Popularized in its modern incarnation by Sutskever et al. 2013, SGD is a simple yet powerful optimizer that that both can serve as a baseline and sometimes outperforms more complex optimizers, e.g., on reasonably benign network topologies. This implementation includes optional support for momentum and Nesterov acceleration, which are standard practice when optimizing DNNs. 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.
momentum¶
Optional exponential decay rate for momentum.
nesterov¶
Whether to use Nesterov acceleration.
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.