validate
Quantize and validate a deep neural network
Description
valResults = validate(quantObj,valData)dlquantizer object,
quantObj, using the data specified by
valData.
valResults = validate(quantObj,valData,quantOpts)quantOpts.
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
Quantize a Neural Network for GPU Target
This example shows how to quantize learnable parameters in the convolution layers of a neural network for GPU and explore the behavior of the quantized network. In this example, you quantize the squeezenet neural network after retraining the network to classify new images according to the Train Deep Learning Network to Classify New Images example. In this example, the memory required for the network is reduced approximately 75% through quantization while the accuracy of the network is not affected.
Load the pretrained network. net is the output network of the Train Deep Learning Network to Classify New Images example.
load squeezenetmerch
netnet =
DAGNetwork with properties:
Layers: [68×1 nnet.cnn.layer.Layer]
Connections: [75×2 table]
InputNames: {'data'}
OutputNames: {'new_classoutput'}
Define calibration and validation data to use for quantization.
The calibration data is used to collect the dynamic ranges of the weights and biases in the convolution and fully connected layers of the network and the dynamic ranges of the activations in all layers of the network. For the best quantization results, the calibration data must be representative of inputs to the network.
The validation data is used to test the network after quantization to understand the effects of the limited range and precision of the quantized convolution layers in the network.
In this example, use the images in the MerchData data set. Define an augmentedImageDatastore object to resize the data for the network. Then, split the data into calibration and validation data sets.
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.
quantObj = dlquantizer(net);
Define a metric function to use to compare the behavior of the network before and after quantization. This example uses the hComputeModelAccuracy metric function.
function accuracy = hComputeModelAccuracy(predictionScores, net, dataStore) %% Computes model-level accuracy statistics % Load ground truth tmp = readall(dataStore); groundTruth = tmp.response; % Compare with predicted label with actual ground truth predictionError = {}; for idx=1:numel(groundTruth) [~, idy] = max(predictionScores(idx,:)); yActual = net.Layers(end).Classes(idy); predictionError{end+1} = (yActual == groundTruth(idx)); %#ok end % Sum all prediction errors. predictionError = [predictionError{:}]; accuracy = sum(predictionError)/numel(predictionError); end
Specify the metric function in a dlquantizationOptions object.
quantOpts = dlquantizationOptions('MetricFcn',{@(x)hComputeModelAccuracy(x, net, aug_valData)});Use the calibrate function to exercise the network with sample inputs and collect range information. The calibrate function exercises the network and collects the dynamic ranges of the weights and biases in the convolution and fully connected layers of the network and the dynamic ranges of the activations in all layers of the network. The function returns a table. Each row of the table contains range information for a learnable parameter of the optimized network.
calResults = calibrate(quantObj, aug_calData)
calResults=121×5 table
Optimized Layer Name Network Layer Name Learnables / Activations MinValue MaxValue
____________________________ ____________________ ________________________ _________ ________
{'conv1_Weights' } {'conv1' } "Weights" -0.91985 0.88489
{'conv1_Bias' } {'conv1' } "Bias" -0.07925 0.26343
{'fire2-squeeze1x1_Weights'} {'fire2-squeeze1x1'} "Weights" -1.38 1.2477
{'fire2-squeeze1x1_Bias' } {'fire2-squeeze1x1'} "Bias" -0.11641 0.24273
{'fire2-expand1x1_Weights' } {'fire2-expand1x1' } "Weights" -0.7406 0.90982
{'fire2-expand1x1_Bias' } {'fire2-expand1x1' } "Bias" -0.060056 0.14602
{'fire2-expand3x3_Weights' } {'fire2-expand3x3' } "Weights" -0.74397 0.66905
{'fire2-expand3x3_Bias' } {'fire2-expand3x3' } "Bias" -0.051778 0.074239
{'fire3-squeeze1x1_Weights'} {'fire3-squeeze1x1'} "Weights" -0.7712 0.68917
{'fire3-squeeze1x1_Bias' } {'fire3-squeeze1x1'} "Bias" -0.10138 0.32675
{'fire3-expand1x1_Weights' } {'fire3-expand1x1' } "Weights" -0.72035 0.9743
{'fire3-expand1x1_Bias' } {'fire3-expand1x1' } "Bias" -0.067029 0.30425
{'fire3-expand3x3_Weights' } {'fire3-expand3x3' } "Weights" -0.61443 0.7741
{'fire3-expand3x3_Bias' } {'fire3-expand3x3' } "Bias" -0.053613 0.10329
{'fire4-squeeze1x1_Weights'} {'fire4-squeeze1x1'} "Weights" -0.7422 1.0877
{'fire4-squeeze1x1_Bias' } {'fire4-squeeze1x1'} "Bias" -0.10885 0.13881
⋮
Use the validate function to quantize the learnable parameters in the convolution layers of the network and exercise the network. The function uses the metric function defined in the dlquantizationOptions object to compare the results of the network before and after quantization.
valResults = validate(quantObj, aug_valData, quantOpts)
valResults = struct with fields:
NumSamples: 20
MetricResults: [1×1 struct]
Statistics: [2×2 table]
Examine the validation output to see the performance of the quantized network.
valResults.MetricResults.Result
ans=2×2 table
NetworkImplementation MetricOutput
_____________________ ____________
{'Floating-Point'} 1
{'Quantized' } 1
valResults.Statistics
ans=2×2 table
NetworkImplementation LearnableParameterMemory(bytes)
_____________________ _______________________________
{'Floating-Point'} 2.9003e+06
{'Quantized' } 7.3393e+05
In this example, the memory required for the network was reduced approximately 75% through quantization. The accuracy of the network is not affected.
The weights, biases, and activations of the convolution layers of the network specified in the dlquantizer object now use scaled 8-bit integer data types.
Quantize a Neural Network for FPGA Target
This example shows how to quantize learnable parameters in the
convolution layers of a neural network and explore the behavior of the quantized
network. In this example, you quantize the logo recognition network
(LogoNet). Quantization helps reduce the memory requirement of a
deep neural network by quantizing weights, biases and activations of network layers to
8-bit scaled integer data types. Use MATLAB® to retrieve the prediction results from the
target device.
This example uses the products listed under FPGA in Quantization Workflow Prerequisites.
Create a file in your current working directory called
getLogoNetwork.m. Enter these lines into the file:
function net = getLogoNetwork() data = getLogoData(); net = data.convnet; end function data = getLogoData() if ~isfile('LogoNet.mat') url = 'https://www.mathworks.com/supportfiles/gpucoder/cnn_models/logo_detection/LogoNet.mat'; websave('LogoNet.mat',url); end data = load('LogoNet.mat'); end
Load the pretrained network.
snet = getLogoNetwork();
snet =
SeriesNetwork with properties:
Layers: [22×1 nnet.cnn.layer.Layer]
InputNames: {'imageinput'}
OutputNames: {'classoutput'}Define calibration and validation data to use for quantization.
The calibration data is used to collect the dynamic ranges of the weights and biases in the convolution and fully connected layers of the network and the dynamic ranges of the activations in all layers of the network. For the best quantization results, the calibration data must be representative of inputs to the network.
The validation data is used to test the network after quantization to understand the effects of the limited range and precision of the quantized convolution layers in the network.
This example uses the images in the logos_dataset data set.
Define an imageDatastore, then split the data into calibration and
validation data sets.
curDir = pwd; newDir = fullfile(matlabroot,'examples','deeplearning_shared','data','logos_dataset.zip'); copyfile(newDir,curDir); unzip('logos_dataset.zip'); imageData = imageDatastore(fullfile(curDir,'logos_dataset'),... 'IncludeSubfolders',true,'FileExtensions','.JPG','LabelSource','foldernames'); [calibrationData,validationData] = splitEachLabel(imageData,0.5,'randomized');
Create a dlquantizer object and specify the network to quantize.
Set the execution environment for the quantized network to FPGA.
dlQuantObj = dlquantizer(snet,'ExecutionEnvironment','FPGA');
Use the calibrate function to exercise the network with sample
inputs and collect range information. The calibrate function
exercises the network and collects the dynamic ranges of the weights and biases in
the convolution and fully connected layers of the network and the dynamic ranges of
the activations in all layers of the network. The function returns a table. Each row
of the table contains range information for a learnable parameter of the optimized
network.
dlQuantObj.calibrate(calibrationData)
ans =
Optimized Layer Name Network Layer Name Learnables / Activations MinValue MaxValue
____________________________ __________________ ________________________ ___________ __________
{'conv_1_Weights' } {'conv_1' } "Weights" -0.048978 0.039352
{'conv_1_Bias' } {'conv_1' } "Bias" 0.99996 1.0028
{'conv_2_Weights' } {'conv_2' } "Weights" -0.055518 0.061901
{'conv_2_Bias' } {'conv_2' } "Bias" -0.00061171 0.00227
{'conv_3_Weights' } {'conv_3' } "Weights" -0.045942 0.046927
{'conv_3_Bias' } {'conv_3' } "Bias" -0.0013998 0.0015218
{'conv_4_Weights' } {'conv_4' } "Weights" -0.045967 0.051
{'conv_4_Bias' } {'conv_4' } "Bias" -0.00164 0.0037892
{'fc_1_Weights' } {'fc_1' } "Weights" -0.051394 0.054344
{'fc_1_Bias' } {'fc_1' } "Bias" -0.00052319 0.00084454
{'fc_2_Weights' } {'fc_2' } "Weights" -0.05016 0.051557
{'fc_2_Bias' } {'fc_2' } "Bias" -0.0017564 0.0018502
{'fc_3_Weights' } {'fc_3' } "Weights" -0.050706 0.04678
{'fc_3_Bias' } {'fc_3' } "Bias" -0.02951 0.024855
{'imageinput' } {'imageinput'} "Activations" 0 255
{'imageinput_normalization'} {'imageinput'} "Activations" -139.34 198.72
Create a target object with a custom name for your target device and an interface to connect your target device to the host computer.
hTarget = dlhdl.Target('Intel','Interface','JTAG');
Define a metric function to use to compare the behavior of the network before and after quantization. Save this function in a local file.
function accuracy = hComputeModelAccuracy(predictionScores,net,dataStore) %% hComputeModelAccuracy test helper function computes model level accuracy statistics % Copyright 2020 The MathWorks, Inc. % Load ground truth groundTruth = dataStore.Labels; % Compare predicted label with ground truth predictionError = {}; for idx=1:numel(groundTruth) [~, idy] = max(predictionScores(idx,:)); yActual = net.Layers(end).Classes(idy); predictionError{end+1} = (yActual == groundTruth(idx)); %#ok end % Sum all prediction errors. predictionError = [predictionError{:}]; accuracy = sum(predictionError)/numel(predictionError); end
Specify the metric function and FPGA execution environment options in a
dlquantizationOptions object.
options = dlquantizationOptions('MetricFcn', ... {@(x)hComputeModelAccuracy(x,snet,validationData)},'Bitstream','arria10soc_int8',... 'Target',hTarget);
Compile and deploy the quantized network. Use the validate
function to quantize the learnable parameters in the convolution layers of the
network and exercise the network. This function uses the output of the compile
function to program the FPGA board by using the programming file. It also downloads
the network weights and biases. The deploy function checks for the Intel®
Quartus® tool and the supported tool version. It then programs the FPGA device
using the sof file, displays progress messages, and the time it takes to deploy the
network. The validate function uses the metric function defined in
the dlquantizationOptions object to compare the results of the
network before and after quantization.
prediction = dlQuantObj.validate(validationData,options);
offset_name offset_address allocated_space
_______________________ ______________ _________________
"InputDataOffset" "0x00000000" "48.0 MB"
"OutputResultOffset" "0x03000000" "4.0 MB"
"SystemBufferOffset" "0x03400000" "60.0 MB"
"InstructionDataOffset" "0x07000000" "8.0 MB"
"ConvWeightDataOffset" "0x07800000" "8.0 MB"
"FCWeightDataOffset" "0x08000000" "12.0 MB"
"EndOffset" "0x08c00000" "Total: 140.0 MB"
### Programming FPGA Bitstream using JTAG...
### Programming the FPGA bitstream has been completed successfully.
### Loading weights to Conv Processor.
### Conv Weights loaded. Current time is 16-Jul-2020 12:45:10
### Loading weights to FC Processor.
### FC Weights loaded. Current time is 16-Jul-2020 12:45:26
### Finished writing input activations.
### Running single input activations.
Deep Learning Processor Profiler Performance Results
LastLayerLatency(cycles) LastLayerLatency(seconds) FramesNum Total Latency Frames/s
------------- ------------- --------- --------- ---------
Network 13570959 0.09047 30 380609145 11.8
conv_module 12667786 0.08445
conv_1 3938907 0.02626
maxpool_1 1544560 0.01030
conv_2 2910954 0.01941
maxpool_2 577524 0.00385
conv_3 2552707 0.01702
maxpool_3 676542 0.00451
conv_4 455434 0.00304
maxpool_4 11251 0.00008
fc_module 903173 0.00602
fc_1 536164 0.00357
fc_2 342643 0.00228
fc_3 24364 0.00016
* The clock frequency of the DL processor is: 150MHz
### Finished writing input activations.
### Running single input activations.
Deep Learning Processor Profiler Performance Results
LastLayerLatency(cycles) LastLayerLatency(seconds) FramesNum Total Latency Frames/s
------------- ------------- --------- --------- ---------
Network 13570364 0.09047 30 380612682 11.8
conv_module 12667103 0.08445
conv_1 3939296 0.02626
maxpool_1 1544371 0.01030
conv_2 2910747 0.01940
maxpool_2 577654 0.00385
conv_3 2551829 0.01701
maxpool_3 676548 0.00451
conv_4 455396 0.00304
maxpool_4 11355 0.00008
fc_module 903261 0.00602
fc_1 536206 0.00357
fc_2 342688 0.00228
fc_3 24365 0.00016
* The clock frequency of the DL processor is: 150MHz
### Finished writing input activations.
### Running single input activations.
Deep Learning Processor Profiler Performance Results
LastLayerLatency(cycles) LastLayerLatency(seconds) FramesNum Total Latency Frames/s
------------- ------------- --------- --------- ---------
Network 13571561 0.09048 30 380608338 11.8
conv_module 12668340 0.08446
conv_1 3939070 0.02626
maxpool_1 1545327 0.01030
conv_2 2911061 0.01941
maxpool_2 577557 0.00385
conv_3 2552082 0.01701
maxpool_3 676506 0.00451
conv_4 455582 0.00304
maxpool_4 11248 0.00007
fc_module 903221 0.00602
fc_1 536167 0.00357
fc_2 342643 0.00228
fc_3 24409 0.00016
* The clock frequency of the DL processor is: 150MHz
### Finished writing input activations.
### Running single input activations.
Deep Learning Processor Profiler Performance Results
LastLayerLatency(cycles) LastLayerLatency(seconds) FramesNum Total Latency Frames/s
------------- ------------- --------- --------- ---------
Network 13569862 0.09047 30 380613327 11.8
conv_module 12666756 0.08445
conv_1 3939212 0.02626
maxpool_1 1543267 0.01029
conv_2 2911184 0.01941
maxpool_2 577275 0.00385
conv_3 2552868 0.01702
maxpool_3 676438 0.00451
conv_4 455353 0.00304
maxpool_4 11252 0.00008
fc_module 903106 0.00602
fc_1 536050 0.00357
fc_2 342645 0.00228
fc_3 24409 0.00016
* The clock frequency of the DL processor is: 150MHz
### Finished writing input activations.
### Running single input activations.
Deep Learning Processor Profiler Performance Results
LastLayerLatency(cycles) LastLayerLatency(seconds) FramesNum Total Latency Frames/s
------------- ------------- --------- --------- ---------
Network 13570823 0.09047 30 380619836 11.8
conv_module 12667607 0.08445
conv_1 3939074 0.02626
maxpool_1 1544519 0.01030
conv_2 2910636 0.01940
maxpool_2 577769 0.00385
conv_3 2551800 0.01701
maxpool_3 676795 0.00451
conv_4 455859 0.00304
maxpool_4 11248 0.00007
fc_module 903216 0.00602
fc_1 536165 0.00357
fc_2 342643 0.00228
fc_3 24406 0.00016
* The clock frequency of the DL processor is: 150MHz
offset_name offset_address allocated_space
_______________________ ______________ _________________
"InputDataOffset" "0x00000000" "48.0 MB"
"OutputResultOffset" "0x03000000" "4.0 MB"
"SystemBufferOffset" "0x03400000" "60.0 MB"
"InstructionDataOffset" "0x07000000" "8.0 MB"
"ConvWeightDataOffset" "0x07800000" "8.0 MB"
"FCWeightDataOffset" "0x08000000" "12.0 MB"
"EndOffset" "0x08c00000" "Total: 140.0 MB"
### FPGA bitstream programming has been skipped as the same bitstream is already loaded on the target FPGA.
### Deep learning network programming has been skipped as the same network is already loaded on the target FPGA.
### Finished writing input activations.
### Running single input activations.
Deep Learning Processor Profiler Performance Results
LastLayerLatency(cycles) LastLayerLatency(seconds) FramesNum Total Latency Frames/s
------------- ------------- --------- --------- ---------
Network 13572329 0.09048 10 127265075 11.8
conv_module 12669135 0.08446
conv_1 3939559 0.02626
maxpool_1 1545378 0.01030
conv_2 2911243 0.01941
maxpool_2 577422 0.00385
conv_3 2552064 0.01701
maxpool_3 676678 0.00451
conv_4 455657 0.00304
maxpool_4 11227 0.00007
fc_module 903194 0.00602
fc_1 536140 0.00357
fc_2 342688 0.00228
fc_3 24364 0.00016
* The clock frequency of the DL processor is: 150MHz
### Finished writing input activations.
### Running single input activations.
Deep Learning Processor Profiler Performance Results
LastLayerLatency(cycles) LastLayerLatency(seconds) FramesNum Total Latency Frames/s
------------- ------------- --------- --------- ---------
Network 13572527 0.09048 10 127266427 11.8
conv_module 12669266 0.08446
conv_1 3939776 0.02627
maxpool_1 1545632 0.01030
conv_2 2911169 0.01941
maxpool_2 577592 0.00385
conv_3 2551613 0.01701
maxpool_3 676811 0.00451
conv_4 455418 0.00304
maxpool_4 11348 0.00008
fc_module 903261 0.00602
fc_1 536205 0.00357
fc_2 342689 0.00228
fc_3 24365 0.00016
* The clock frequency of the DL processor is: 150MHzThe weights, biases, and activations of the convolution layers of the network
specified in the dlquantizer object now use scaled 8-bit integer
data types.
Examine the MetricResults.Result field of the validation output
to see the performance of the quantized network.
validateOut = prediction.MetricResults.Result
ans =
NetworkImplementation MetricOutput
_____________________ ____________
{'Floating-Point'} 0.9875
{'Quantized' } 0.9875
Examine the QuantizedNetworkFPS field of the validation output
to see the frames per second performance of the quantized network.
prediction.QuantizedNetworkFPS
ans = 11.8126
Validate Network Quantized for FPGA Target Using Simulation
This example shows how to quantize learnable parameters in the convolution layers of a neural network, and validate the quantized network. Rapidly prototype the quantized network by using simulation to validate the quantized network. Simulation does not require an FPGA board for the prototyping process. In this example, you quantize the LogoNet neural network.
This example uses the products listed under FPGA in Quantization Workflow Prerequisites.
Load the pretrained network and analyze the network architecture.
snet = getLogoNetwork; analyzeNetwork(snet);
Define calibration and validation data to use for quantization.
This example uses the logos_dataset data set. The data set
consists of 320 images. Each image is 227-by-227 in size and has three color channels
(RGB). Create an imageDatastore object to use for calibration and
validation. Expedite the calibration and validation process by reducing the calibration
data set to 20 images. The MATLAB simulation workflow has a maximum limit of five images
when validating the quantized network. Reduce the validation data set sizes to five
images.
curDir = pwd; newDir = fullfile(matlabroot,'examples','deeplearning_shared','data','logos_dataset.zip'); copyfile(newDir,curDir,'f'); unzip('logos_dataset.zip'); imageData = imageDatastore(fullfile(curDir,'logos_dataset'),... 'IncludeSubfolders',true,'FileExtensions','.JPG','LabelSource','foldernames'); [calibrationData, validationData] = splitEachLabel(imageData,0.5,'randomized'); calibrationData_reduced = calibrationData.subset(1:20); validationData_reduced = validationData.subset(1:5);
Create a dlquantizer object with the FPGA execution environment. To
use simulation for validation of the quantized network, set the
'Simulation' property to 'on'.
dlQuantObj = dlquantizer(snet,'ExecutionEnvironment','FPGA','Simulation','on');
Use the calibrate function to exercise the network with sample
inputs and collect the range information. The calibrate function
exercises the network and collects the dynamic ranges of the weights and biases in the
convolution and fully connected layers of the network and the dynamic ranges of the
activations in all layers of the network. The calibrate function returns a table. Each
row of the table contains range information for a learnable parameter of the quantized
network.
dlQuantObj.calibrate(calibrationData_reduced)
ans =
35x5 table
Optimized Layer Name Network Layer Name Learnables / Activations MinValue MaxValue
____________________ __________________ ________________________ ___________ ________
{'conv_1_Weights'} {'conv_1' } "Weights" -0.048978 0.039352
{'conv_1_Bias' } {'conv_1' } "Bias" 0.99996 1.0028
{'conv_2_Weights'} {'conv_2' } "Weights" -0.055518 0.061901
{'conv_2_Bias' } {'conv_2' } "Bias" -0.00061171 0.00227
{'conv_3_Weights'} {'conv_3' } "Weights" -0.045942 0.046927
: : : : :
{'fc_2' } {'fc_2' } "Activations" -16.557 17.512
{'relu_6' } {'relu_6' } "Activations" 0 17.512
{'fc_3' } {'fc_3' } "Activations" -13.049 37.204
{'softmax' } {'softmax' } "Activations" 1.4971e-22 1
{'classoutput' } {'classoutput'} "Activations" 1.4971e-22 1Use the validate function to quantize the learnable parameters
in the convolution layers of the network. The validate function
simulates the quantized network in MATLAB®. The validate function uses the default metric
function for classification, Top-1 Accuracy, to compare the results of the single data
type network object to the results of the quantized network object.
Note
If no custom metric function is specified using a
dlquantizationOptions object, the default metric function will be
used for validation. The default metric function uses at most 5 files from the
validation datastore when the simulation option is selected for validation. Custom
metric functions do not have this restriction.
prediction = dlQuantObj.validate(validationData_reduced)
Compiling leg: conv_1>>relu_4 ...
Compiling leg: conv_1>>relu_4 ... complete.
Compiling leg: maxpool_4 ...
Compiling leg: maxpool_4 ... complete.
Compiling leg: fc_1>>fc_3 ...
Compiling leg: fc_1>>fc_3 ... complete.
prediction =
struct with fields:
NumSamples: 5
MetricResults: [1x1 struct]
Statistics: []Examine the MetricResults.Result field of the validation output
to see the performance of the quantized network.
metricFcn = prediction.MetricResults.MetricFunction validateOut = prediction.MetricResults.Result
metricFcn =
'Top-1 accuracy'
validateOut =
2x2 table
NetworkImplementation MetricOutput
_____________________ ____________
{'Floating-Point'} 1
{'Quantized' } 1 Quantize a Neural Network for CPU Target
This example shows how to quantize and validate a neural network for a CPU target. This workflow is similar to other execution environments, but before validating you must establish a raspi connection.
First, load your network. This example uses the pretrained network squeezenet.
load squeezenetmerch
netnet =
DAGNetwork with properties:
Layers: [68×1 nnet.cnn.layer.Layer]
Connections: [75×2 table]
InputNames: {'data'}
OutputNames: {'new_classoutput'}
Then define your calibration and validation data, calDS and valDS respectively.
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 the dlquantizer object and specify a CPU execution environment.
dq = dlquantizer(net,'ExecutionEnvironment','CPU')
dq =
dlquantizer with properties:
NetworkObject: [1×1 DAGNetwork]
ExecutionEnvironment: 'CPU'
Calibrate the network.
calResults = calibrate(dq,aug_calData)
Attempt to calibrate with host GPU errored with the message: Unable to find a supported GPU device. For more information on GPU support, see GPU Support by Release. Reverting to use host CPU.
calResults=121×5 table
Optimized Layer Name Network Layer Name Learnables / Activations MinValue MaxValue
____________________________ ____________________ ________________________ _________ ________
{'conv1_Weights' } {'conv1' } "Weights" -0.91985 0.88489
{'conv1_Bias' } {'conv1' } "Bias" -0.07925 0.26343
{'fire2-squeeze1x1_Weights'} {'fire2-squeeze1x1'} "Weights" -1.38 1.2477
{'fire2-squeeze1x1_Bias' } {'fire2-squeeze1x1'} "Bias" -0.11641 0.24273
{'fire2-expand1x1_Weights' } {'fire2-expand1x1' } "Weights" -0.7406 0.90982
{'fire2-expand1x1_Bias' } {'fire2-expand1x1' } "Bias" -0.060056 0.14602
{'fire2-expand3x3_Weights' } {'fire2-expand3x3' } "Weights" -0.74397 0.66905
{'fire2-expand3x3_Bias' } {'fire2-expand3x3' } "Bias" -0.051778 0.074239
{'fire3-squeeze1x1_Weights'} {'fire3-squeeze1x1'} "Weights" -0.7712 0.68917
{'fire3-squeeze1x1_Bias' } {'fire3-squeeze1x1'} "Bias" -0.10138 0.32675
{'fire3-expand1x1_Weights' } {'fire3-expand1x1' } "Weights" -0.72035 0.9743
{'fire3-expand1x1_Bias' } {'fire3-expand1x1' } "Bias" -0.067029 0.30425
{'fire3-expand3x3_Weights' } {'fire3-expand3x3' } "Weights" -0.61443 0.7741
{'fire3-expand3x3_Bias' } {'fire3-expand3x3' } "Bias" -0.053613 0.10329
{'fire4-squeeze1x1_Weights'} {'fire4-squeeze1x1'} "Weights" -0.7422 1.0877
{'fire4-squeeze1x1_Bias' } {'fire4-squeeze1x1'} "Bias" -0.10885 0.13881
⋮
Use the MATLAB Support Package for Raspberry Pi function, raspi, to create a connection to the Raspberry Pi. In the following code, replace:
raspinamewith the name or address of your Raspberry Piusernamewith your user namepasswordwith your password
% r = raspi('raspiname','username','password');Validate the quantized network with the validate function.
valResults = validate(dq,aug_valData)
### Starting application: 'codegen/lib/validate_predict_int8/pil/validate_predict_int8.elf'
To terminate execution: clear validate_predict_int8_pil
### Launching application validate_predict_int8.elf...
### Host application produced the following standard output (stdout) and standard error (stderr) messages:
valResults = struct with fields:
NumSamples: 20
MetricResults: [1×1 struct]
Statistics: []
Examine the validation output to see the performance of the quantized network.
valResults.MetricResults.Result
ans=2×2 table
NetworkImplementation MetricOutput
_____________________ ____________
{'Floating-Point'} 0.95
{'Quantized' } 0.95
Input Arguments
Output Arguments
Algorithms
The validate function determines the default metric function to use
for the validation based on the type of network that is being quantized.
| Type of Network | Metric Function |
|---|---|
| Classification | Top-1 Accuracy — Accuracy of the network |
| Object Detection | Average Precision — Average precision over all detection results. See evaluateDetectionPrecision (Computer Vision Toolbox). |
| Regression | MSE — Mean squared error of the network |
| Semantic Segmentation | evaluateSemanticSegmentation (Computer Vision Toolbox) — Evaluate semantic segmentation data set
against ground truth |
| Single Shot Detector (SSD) | WeightedIOU — Average IoU of each class, weighted by the number of pixels in that class |
When the 'Simulation' property of the dlquantizer
object is set to 'on', the default metric function uses at most 5 files
from the validation datastore. Custom metric functions, specified using a
dlquantizationOptions object, do not have this restriction.
Version History
Introduced in R2020aSee Also
Apps
Functions
You can also select a web site from the following list:
Americas
- América Latina (Español)
- Canada (English)
- United States (English)
Europe
- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)
- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom (English)