estimateNetworkMetrics

Estimate network metrics for specific layers of a neural network

Since R2022a

Syntax

dataTable = estimateNetworkMetrics(net)

[dataTable1,dataTable2,…,dataTableN] = estimateNetworkMetrics(net1,net2,…,netN)

Description

dataTable = estimateNetworkMetrics(net) returns a table containing these estimated layer-wise metrics for a deep neural network:

• LayerName — Name of layer

• LayerType — Type of layer

• NumberOfLearnables — Number of non-zero learnable parameters (weights and biases) in the network

• NumberOfOperations — Total number of multiplications and additions

• ParameterMemory (MB) — Memory required to store all of the learnable parameters

• NumberOfMACs — Number of multiply-accumulate operations

• ArithmeticIntensity (FLOP/B) — Amount of reuse of data fetched from memory, measured as the number of floating-point operations performed per the bytes of memory access required to support those operations. For example, convolutional layers reuse the same weight data across computations for multiple input features, resulting in a relatively high arithmetic intensity.

This function estimates metrics for learnable layers, which have weights and bias, in the network. Estimated metrics are provided for the following supported layers.

• convolution2dLayer

• groupedConvolution2dLayer

• fullyConnectedLayer

• lstmLayer

• lstmProjectedLayer

• bilstmLayer

[dataTable1,dataTable2,…,dataTableN] = estimateNetworkMetrics(net1,net2,…,netN) returns metrics for multiple networks.

This function requires Deep Learning Toolbox Model Quantization Library. To learn about the products required to quantize a deep neural network, see Quantization Workflow Prerequisites.

Examples

Estimate Metrics for Neural Network Layers

This example shows how to estimate layer-wise metrics for a neural network.

Load the pretrained network. net is a SqueezeNet convolutional neural network that has been retrained using transfer learning to classify images in the MerchData data set.

load squeezedlnetmerch
net

net = 
  dlnetwork with properties:

         Layers: [67×1 nnet.cnn.layer.Layer]
    Connections: [74×2 table]
     Learnables: [52×3 table]
          State: [0×3 table]
     InputNames: {'data'}
    OutputNames: {'prob'}
    Initialized: 1

  View summary with summary.

Use the estimateNetworkMetrics function to estimate metrics for the supported layers in your network.

estNet = estimateNetworkMetrics(net)

estNet=26×7 table
        LayerName             LayerType        NumberOfLearnables    NumberOfOperations    ParameterMemory (MB)    NumberOfMACs    ArithmeticIntensity
    __________________    _________________    __________________    __________________    ____________________    ____________    ___________________

    "conv1"               "2-D Convolution"           1792                4.413e+07             0.0068359           2.2065e+07           25.739       
    "fire2-squeeze1x1"    "2-D Convolution"           1040               6.4225e+06             0.0039673           3.2113e+06           12.748       
    "fire2-expand1x1"     "2-D Convolution"           1088               6.4225e+06             0.0041504           3.2113e+06           12.748       
    "fire2-expand3x3"     "2-D Convolution"           9280               5.7803e+07                0.0354           2.8901e+07           111.12       
    "fire3-squeeze1x1"    "2-D Convolution"           2064               1.2845e+07             0.0078735           6.4225e+06           14.158       
    "fire3-expand1x1"     "2-D Convolution"           1088               6.4225e+06             0.0041504           3.2113e+06           12.748       
    "fire3-expand3x3"     "2-D Convolution"           9280               5.7803e+07                0.0354           2.8901e+07           111.12       
    "fire4-squeeze1x1"    "2-D Convolution"           4128               6.4225e+06              0.015747           3.2113e+06           24.791       
    "fire4-expand1x1"     "2-D Convolution"           4224               6.4225e+06              0.016113           3.2113e+06           24.791       
    "fire4-expand3x3"     "2-D Convolution"          36992               5.7803e+07               0.14111           2.8901e+07           178.07       
    "fire5-squeeze1x1"    "2-D Convolution"           8224               1.2845e+07              0.031372           6.4225e+06           27.449       
    "fire5-expand1x1"     "2-D Convolution"           4224               6.4225e+06              0.016113           3.2113e+06           24.791       
    "fire5-expand3x3"     "2-D Convolution"          36992               5.7803e+07               0.14111           2.8901e+07           178.07       
    "fire6-squeeze1x1"    "2-D Convolution"          12336               4.8169e+06              0.047058           2.4084e+06            33.51       
    "fire6-expand1x1"     "2-D Convolution"           9408               3.6127e+06              0.035889           1.8063e+06           32.109       
    "fire6-expand3x3"     "2-D Convolution"          83136               3.2514e+07               0.31714           1.6257e+07           125.07       
      ⋮

Compare Metrics for Floating-Point and Quantized Neural Network

This example shows how to estimate the metrics for a floating-point and quantized neural network.

Load the pretrained network. net is a SqueezeNet convolutional neural network that has been retrained using transfer learning to classify images in the MerchData data set.

load squeezedlnetmerch
net

net = 
  dlnetwork with properties:

         Layers: [67×1 nnet.cnn.layer.Layer]
    Connections: [74×2 table]
     Learnables: [52×3 table]
          State: [0×3 table]
     InputNames: {'data'}
    OutputNames: {'prob'}
    Initialized: 1

  View summary with summary.

Unzip and load the MerchData images as an image datastore. Define an augmentedImageDatastore object to resize the data for the network, and split the data into calibration and validation data sets to use for quantization.

unzip('MerchData.zip');
imds = imageDatastore('MerchData', ...
    'IncludeSubfolders',true, ...
    'LabelSource','foldernames');
[calData, valData] = splitEachLabel(imds, 0.7,'randomized');
aug_calData = augmentedImageDatastore([227 227],calData);
aug_valData = augmentedImageDatastore([227 227],valData);

Create a dlquantizer object and specify the network to quantize. Set the execution environment to MATLAB. When you use the MATLAB execution environment, quantization is performed using the fi fixed-point data type which requires a Fixed-Point Designer™ license.

quantObj = dlquantizer(net,'ExecutionEnvironment','MATLAB');

Use the calibrate function to exercise the network with sample inputs and collect range information.

calResults = calibrate(quantObj,aug_calData);

Use the quantize method to quantize the network object and return a simulatable quantized network.

qNet = quantize(quantObj)

qNet = 
  Quantized dlnetwork with properties:

         Layers: [67×1 nnet.cnn.layer.Layer]
    Connections: [74×2 table]
     Learnables: [52×3 table]
          State: [0×3 table]
     InputNames: {'data'}
    OutputNames: {'prob'}
    Initialized: 1

  View summary with summary.
  Use the quantizationDetails function to extract quantization details.

Use the estimateNetworkMetrics function to estimate metrics for the floating-point and quantized networks.

[dataTableFloat,dataTableQuantized] = estimateNetworkMetrics(net,qNet)

dataTableFloat=26×7 table
        LayerName             LayerType        NumberOfLearnables    NumberOfOperations    ParameterMemory (MB)    NumberOfMACs    ArithmeticIntensity
    __________________    _________________    __________________    __________________    ____________________    ____________    ___________________

    "conv1"               "2-D Convolution"           1792                4.413e+07             0.0068359           2.2065e+07           25.739       
    "fire2-squeeze1x1"    "2-D Convolution"           1040               6.4225e+06             0.0039673           3.2113e+06           12.748       
    "fire2-expand1x1"     "2-D Convolution"           1088               6.4225e+06             0.0041504           3.2113e+06           12.748       
    "fire2-expand3x3"     "2-D Convolution"           9280               5.7803e+07                0.0354           2.8901e+07           111.12       
    "fire3-squeeze1x1"    "2-D Convolution"           2064               1.2845e+07             0.0078735           6.4225e+06           14.158       
    "fire3-expand1x1"     "2-D Convolution"           1088               6.4225e+06             0.0041504           3.2113e+06           12.748       
    "fire3-expand3x3"     "2-D Convolution"           9280               5.7803e+07                0.0354           2.8901e+07           111.12       
    "fire4-squeeze1x1"    "2-D Convolution"           4128               6.4225e+06              0.015747           3.2113e+06           24.791       
    "fire4-expand1x1"     "2-D Convolution"           4224               6.4225e+06              0.016113           3.2113e+06           24.791       
    "fire4-expand3x3"     "2-D Convolution"          36992               5.7803e+07               0.14111           2.8901e+07           178.07       
    "fire5-squeeze1x1"    "2-D Convolution"           8224               1.2845e+07              0.031372           6.4225e+06           27.449       
    "fire5-expand1x1"     "2-D Convolution"           4224               6.4225e+06              0.016113           3.2113e+06           24.791       
    "fire5-expand3x3"     "2-D Convolution"          36992               5.7803e+07               0.14111           2.8901e+07           178.07       
    "fire6-squeeze1x1"    "2-D Convolution"          12336               4.8169e+06              0.047058           2.4084e+06            33.51       
    "fire6-expand1x1"     "2-D Convolution"           9408               3.6127e+06              0.035889           1.8063e+06           32.109       
    "fire6-expand3x3"     "2-D Convolution"          83136               3.2514e+07               0.31714           1.6257e+07           125.07       
      ⋮

dataTableQuantized=26×7 table
        LayerName             LayerType        NumberOfLearnables    NumberOfOperations    ParameterMemory (MB)    NumberOfMACs    ArithmeticIntensity
    __________________    _________________    __________________    __________________    ____________________    ____________    ___________________

    "conv1"               "2-D Convolution"           1792                4.413e+07               0.001709          2.2065e+07           25.739       
    "fire2-squeeze1x1"    "2-D Convolution"           1040               6.4225e+06             0.00099182          3.2113e+06           12.748       
    "fire2-expand1x1"     "2-D Convolution"           1088               6.4225e+06              0.0010376          3.2113e+06           12.748       
    "fire2-expand3x3"     "2-D Convolution"           9280               5.7803e+07              0.0088501          2.8901e+07           111.12       
    "fire3-squeeze1x1"    "2-D Convolution"           2064               1.2845e+07              0.0019684          6.4225e+06           14.158       
    "fire3-expand1x1"     "2-D Convolution"           1088               6.4225e+06              0.0010376          3.2113e+06           12.748       
    "fire3-expand3x3"     "2-D Convolution"           9280               5.7803e+07              0.0088501          2.8901e+07           111.12       
    "fire4-squeeze1x1"    "2-D Convolution"           4128               6.4225e+06              0.0039368          3.2113e+06           24.791       
    "fire4-expand1x1"     "2-D Convolution"           4224               6.4225e+06              0.0040283          3.2113e+06           24.791       
    "fire4-expand3x3"     "2-D Convolution"          36992               5.7803e+07               0.035278          2.8901e+07           178.07       
    "fire5-squeeze1x1"    "2-D Convolution"           8224               1.2845e+07               0.007843          6.4225e+06           27.449       
    "fire5-expand1x1"     "2-D Convolution"           4224               6.4225e+06              0.0040283          3.2113e+06           24.791       
    "fire5-expand3x3"     "2-D Convolution"          36992               5.7803e+07               0.035278          2.8901e+07           178.07       
    "fire6-squeeze1x1"    "2-D Convolution"          12336               4.8169e+06               0.011765          2.4084e+06            33.51       
    "fire6-expand1x1"     "2-D Convolution"           9408               3.6127e+06              0.0089722          1.8063e+06           32.109       
    "fire6-expand3x3"     "2-D Convolution"          83136               3.2514e+07               0.079285          1.6257e+07           125.07       
      ⋮

Compare the parameter memory requirements of the layers supported by estimateNetworkMetrics for the floating-point and quantized networks.

totalMemoryFloat = sum(dataTableFloat.("ParameterMemory (MB)"));
totalMemoryQuantized = sum(dataTableQuantized.("ParameterMemory (MB)"));
percentReduction = (totalMemoryFloat - totalMemoryQuantized)*100/totalMemoryFloat

percentReduction = 75

Input Arguments

net — Neural network

dlnetwork object | DAGNetwork object | SeriesNetwork object | taylorPrunableNetwork object | RegressionNeuralNetwork object | CompactRegressionNeuralNetwork object | ClassificationNeuralNetwork object | CompactClassificationNeuralNetwork object

Neural network, specified as one of these values:

• dlnetwork object

• DAGNetwork object

• SeriesNetwork object

• taylorPrunableNetwork object

• RegressionNeuralNetwork (Statistics and Machine Learning Toolbox) object

• CompactRegressionNeuralNetwork (Statistics and Machine Learning Toolbox) object

• ClassificationNeuralNetwork (Statistics and Machine Learning Toolbox) object

• CompactClassificationNeuralNetwork (Statistics and Machine Learning Toolbox) object

estimateNetworkMetrics supports both floating-point and quantized networks.

net1,net2,…,netN — Neural networks

dlnetwork object | DAGNetwork object | SeriesNetwork object | taylorPrunableNetwork object | RegressionNeuralNetwork object | CompactRegressionNeuralNetwork object | ClassificationNeuralNetwork object | CompactClassificationNeuralNetwork object

Neural networks, specified as a comma-separated list of any of the following values:

• dlnetwork objects

• DAGNetwork objects

• SeriesNetwork objects

• taylorPrunableNetwork objects

• RegressionNeuralNetwork (Statistics and Machine Learning Toolbox) objects

• CompactRegressionNeuralNetwork (Statistics and Machine Learning Toolbox) objects

• ClassificationNeuralNetwork (Statistics and Machine Learning Toolbox) objects

• CompactClassificationNeuralNetwork (Statistics and Machine Learning Toolbox) objects

estimateNetworkMetrics supports both floating-point and quantized networks.

Version History

Introduced in R2022a