updateScore

Compute and accumulate Taylor-based importance scores for pruning

Since R2022a

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Syntax

prunableNet_new = updateScore(prunableNet,pruningActivations,pruningGradients)

Description

prunableNet_new = updateScore(prunableNet,pruningActivations,pruningGradients) computes and accumulates Taylor-based importance scores of convolution filters in prunable layers. This function returns another TaylorPrunableNetwork object whose state contains these updated scores.

To get robust estimates of the importance scores of the convolution filters in your network, execute updateScore several times on the same prunable network for different mini-batches of data.

To prune a deep neural network, you require the Deep Learning Toolbox™ Model Compression Library support package This support package is a free add-on that you can download using the Add-On Explorer. Alternatively, see Deep Learning Toolbox Model Compression Library.

example

Examples

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This example uses:

Open Live Script

This example shows how to prune a dlnetwork object by using a custom pruning loop.

Load dlnetwork Object

Load a trained dlnetwork object and the corresponding classes.

s = load("digitsCustom.mat");
dlnet_1 = s.dlnet;
classes = s.classes;

Inspect the layers of the dlnetwork object. The network has three convolution layers at locations 2, 5, and 8 of the Layer array.

layers_1 = dlnet_1.Layers
layers_1 = 
  12×1 Layer array with layers:

     1   'input'     Image Input           28×28×1 images with 'zerocenter' normalization
     2   'conv1'     2-D Convolution       20 5×5×1 convolutions with stride [1  1] and padding [0  0  0  0]
     3   'bn1'       Batch Normalization   Batch normalization with 20 channels
     4   'relu1'     ReLU                  ReLU
     5   'conv2'     2-D Convolution       20 3×3×20 convolutions with stride [1  1] and padding [1  1  1  1]
     6   'bn2'       Batch Normalization   Batch normalization with 20 channels
     7   'relu2'     ReLU                  ReLU
     8   'conv3'     2-D Convolution       20 3×3×20 convolutions with stride [1  1] and padding [1  1  1  1]
     9   'bn3'       Batch Normalization   Batch normalization with 20 channels
    10   'relu3'     ReLU                  ReLU
    11   'fc'        Fully Connected       10 fully connected layer
    12   'softmax'   Softmax               softmax

Load Data for Prediction

Load the digits data for prediction.

dataFolder = fullfile(toolboxdir("nnet"),"nndemos","nndatasets","DigitDataset");

imds = imageDatastore(dataFolder, ...
    IncludeSubfolders=true, ...
    LabelSource="foldernames");

Partition the data into pruning and validation sets. Set aside 10% of the data for validation using the splitEachLabel function.

[imdsPrune,imdsValidation] = splitEachLabel(imds,0.9,"randomize");

The network used in this example requires input images of size 28-by-28-by-1. To automatically resize the images, use augmented image datastores.

inputSize = [28 28 1];
augimdsPrune = augmentedImageDatastore(inputSize(1:2),imdsPrune);
augimdsValidation = augmentedImageDatastore(inputSize(1:2),imdsValidation);

Prune dlnetwork Object

Convert the dlnetwork object to a representation that is suitable for pruning by using the taylorPrunableNetwork function. This function returns a TaylorPrunableNetwork object that has the NumPrunables property set to 48. This indicates that 48 filters in the original model are suitable for pruning by using the Taylor pruning algorithm.

prunableNet_1 = taylorPrunableNetwork(dlnet_1)
prunableNet_1 = 
  TaylorPrunableNetwork with properties:

      Learnables: [14×3 table]
           State: [6×3 table]
      InputNames: {'input'}
     OutputNames: {'softmax'}
    NumPrunables: 48

Create a minibatchqueue object that processes and manages mini-batches of images during pruning. For each mini-batch:

  • Use the custom mini-batch preprocessing function preprocessMiniBatch (defined at the end of this example) to convert the labels to one-hot encoded variables.

  • Format the image data with the dimension labels "SSCB" (spatial, spatial, channel, batch). By default, the minibatchqueue object converts the data to dlarray objects with underlying type single. Do not format the class labels.

  • Train on a GPU if one is available. By default, the minibatchqueue object converts each output to a gpuArray if a GPU is available. Using a GPU requires Parallel Computing Toolbox™ and a supported GPU device. For information on supported devices, see GPU Computing Requirements (Parallel Computing Toolbox).

miniBatchSize = 128;
imds.ReadSize = miniBatchSize;

mbq = minibatchqueue(augimdsPrune, ...
    MiniBatchSize=miniBatchSize, ...
    MiniBatchFcn=@preprocessMiniBatch, ...
    MiniBatchFormat=["SSCB" ""]);

Calculate Taylor-based importance scores of the prunable filters in the network by looping over the mini-batches of data. For each mini-batch:

  • Calculate pruning activations and pruning gradients by using the modelLoss function defined at the end of this example

  • Update importance scores of the prunable filters by using the updateScore function

while hasdata(mbq)
    [X,T] = next(mbq);
    [~,pruningActivations,pruningGradients] = dlfeval(@modelLoss,prunableNet_1,X,T);
    prunableNet_1 = updateScore(prunableNet_1,pruningActivations,pruningGradients);
end

Finally, remove filters with the lowest importance scores to create a new TaylorPrunableNetwork object by using the updatePrunables function. By default, a single call to this function removes 8 filters. Observe that the new network prunableNet_2 has 40 prunable filters remaining.

prunableNet_2 = updatePrunables(prunableNet_1)
prunableNet_2 = 
  TaylorPrunableNetwork with properties:

      Learnables: [14×3 table]
           State: [6×3 table]
      InputNames: {'input'}
     OutputNames: {'softmax'}
    NumPrunables: 40

To further compress the model, run the custom pruning loop and update prunables again.

Extract Pruned dlnetwork Object

Use the dlnetwork function to extract the pruned dlnetwork object from the pruned TaylorPrunableNetwork object. You can now use this compressed dlnetwork object to perform inference.

dlnet_2 = dlnetwork(prunableNet_2);

Compare the convolution layers of the original and the pruned dlnetwork objects. Observe that the three convolution layers in the pruned network have fewer filters. These counts agree with the fact that, by default, a single call to the updatePrunables function removes 8 filters from the network.

conv_layers_1 = dlnet_1.Layers([2 5 8])
conv_layers_1 = 
  3×1 Convolution2DLayer array with layers:

     1   'conv1'   2-D Convolution   20 5×5×1 convolutions with stride [1  1] and padding [0  0  0  0]
     2   'conv2'   2-D Convolution   20 3×3×20 convolutions with stride [1  1] and padding [1  1  1  1]
     3   'conv3'   2-D Convolution   20 3×3×20 convolutions with stride [1  1] and padding [1  1  1  1]

conv_layers_2 = dlnet_2.Layers([2 5 8])
conv_layers_2 = 
  3×1 Convolution2DLayer array with layers:

     1   'conv1'   2-D Convolution   17 5×5×1 convolutions with stride [1  1] and padding [0  0  0  0]
     2   'conv2'   2-D Convolution   18 3×3×17 convolutions with stride [1  1] and padding [1  1  1  1]
     3   'conv3'   2-D Convolution   17 3×3×18 convolutions with stride [1  1] and padding [1  1  1  1]

Supporting Functions

Model Loss Function

The modelLoss function takes a TaylorPrunableNetwork object net, a mini-batch of input data X with corresponding targets T and returns activations in net and the gradients of the loss with respect to the activations in net. To compute the gradients automatically, this function uses the dlgradient function.

function [loss, pruningActivations, pruningGradients] = modelLoss(net,X,T)

% Calculate network output for training.
[out, ~, pruningActivations] = forward(net,X);

% Calculate loss.
loss = crossentropy(out,T);

% Compute pruning gradients.
pruningGradients = dlgradient(loss,pruningActivations);
end

Mini Batch Preprocessing Function

The preprocessMiniBatch function preprocesses a mini-batch of predictors and labels using the following steps:

  1. Preprocess the images using the preprocessMiniBatchPredictors function.

  2. Extract the label data from the incoming cell array and concatenate into a categorical array along the second dimension.

  3. One-hot encode the categorical labels into numeric arrays. Encoding into the first dimension produces an encoded array that matches the shape of the network output.

function [X,T] = preprocessMiniBatch(dataX,dataT)

% Preprocess predictors.
X = preprocessMiniBatchPredictors(dataX);

% Extract label data from cell and concatenate.
T = cat(2,dataT{1:end});

% One-hot encode labels.
T = onehotencode(T,1);

end

Mini-Batch Predictors Preprocessing Function

The preprocessMiniBatchPredictors function preprocesses a mini-batch of predictors by extracting the image data from the input cell array and concatenating into a numeric array. For grayscale input, concatenating over the fourth dimension adds a third dimension to each image, to use as a singleton channel dimension.

function X = preprocessMiniBatchPredictors(dataX)

% Concatenate.
X = cat(4,dataX{1:end});

% Normalize the images.
X = X/255;

end

Input Arguments

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Network for pruning by using first-order Taylor approximation, specified as a TaylorPrunableNetwork object.

Activations of the pruning layers, specified as a cell array containing dlarray objects. To retrieve these values, call the forward function on the prunable network.

Gradients of loss with respect to pruningActivations, specified as a cell array containing dlarray objects. To calculate pruningGradients, first calculate the loss and then use the dlgradient function.

Output Arguments

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Network object for pruning that been updated to contain the accumulated Taylor-based importance scores of the prunable filters, specified as a TaylorPrunableNetwork object.

Version History

Introduced in R2022a

See Also

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