% Evaluate the model gradients, state, and loss using dlfeval and the % modelGradients function and update the network state. = modelAccuracy(prunableNet, augimdsTest, anchorBoxes, anchorBoxMasks, classNames, 16) Īddpoints(lineAccuracyPruning, iteration, accuracy)Īddpoints(lineNumPrunables,iteration,double(prunableNet.NumPrunables))Įnd % Set x-axis tick values at the end of each pruning iteration. If (mod(pruningIteration, validationFrequency) = 0 || isLastPruningIteration) IsLastPruningIteration = pruningIteration = maxPruningIterations % Show results on validation data set in a subset of pruning % iterations. PrunableNet = updatePrunables(prunableNet, MaxToPrune = maxToPrune) if (fineTuningIteration > numMinibatchUpdates)īreak end end % Prune filters based on previously computed Taylor scores. ![]() % Stop the fine-tuning loop when numMinibatchUpdates is reached. % Synchronize the x-axis of the accuracy plot with the loss plot. Title(tl, "Processing Pruning Iteration: " + pruningIteration + " of " + maxPruningIterations +. PrunableNet = updateScore(prunableNet, pruningActivations, pruningGradients) ĭ = duration(0,0,toc(start), 'Format', 'hh:mm:ss') Īddpoints(lineLossFinetune, iteration, double(loss.totalLoss)) ![]() % Compute first-order Taylor scores and accumulate the score across % previous mini-batches of data. NetGradients, velocity, learnRate, momentum) ![]() % Update the network parameters using the SGDM optimizer. % Evaluate the pruning activations, gradients of the pruning % activations, model gradients, state, and loss using dlfeval and % modelLossPruning functions. ![]() % Reset the velocity parameter for the SGDM solver in every pruning % iteration.įineTuningIteration = fineTuningIteration + 1 For pruningIteration = 1:maxPruningIterations
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