btabase.blogg.se - Frontline solver optimization problem types

To take the weights all the way from initialization to learned model.

updates the solver state according to learning rate, history, and method.

incorporates the gradients into parameter updates according to the solver method.

calls network backward to compute the gradients.

calls network forward to compute the output and loss.

snapshots the model and solver state throughout the optimization.

(periodically) evaluates the test networks.

iteratively optimizes by calling forward / backward and updating parameters.scaffolds the optimization bookkeeping and creates the training network for learning and test network(s) for evaluation.Nesterov’s Accelerated Gradient ( type: "Nesterov") and.Stochastic Gradient Descent ( type: "SGD"),.The responsibilities of learning are divided between the Solver for overseeing the optimization and generating parameter updates and the Net for yielding loss and gradients.

The solver orchestrates model optimization by coordinating the network’s forward inference and backward gradients to form parameter updates that attempt to improve the loss.