featureInputLayer
Feature input layer
Description
A feature input layer inputs feature data to a neural network and applies data normalization. Use this layer when you have a data set of numeric scalars representing features (data without spatial or time dimensions).
For image input, use imageInputLayer.
Creation
Description
layer = featureInputLayer(numFeatures)InputSize property to the specified number of features.
layer = featureInputLayer(numFeatures,Name=Value)
Input Arguments
Name-Value Arguments
Properties
Examples
Create a feature input layer with the name "input" for observations consisting of 21 features.
layer = featureInputLayer(21,Name="input")layer =
FeatureInputLayer with properties:
Name: 'input'
InputSize: 21
SplitComplexInputs: 0
Hyperparameters
Normalization: 'none'
NormalizationDimension: 'auto'
Include a feature input layer in a Layer array.
numFeatures = 21;
numClasses = 3;
layers = [
featureInputLayer(numFeatures)
fullyConnectedLayer(numClasses)
softmaxLayer]layers =
3×1 Layer array with layers:
1 '' Feature Input 21 features
2 '' Fully Connected 3 fully connected layer
3 '' Softmax softmax
Define the size of the input image, the number of features of each observation, the number of classes, and the size and number of filters of the convolution layer.
imageInputSize = [28 28 1]; numFeatures = 1; numClasses = 10; filterSize = 5; numFilters = 16;
To create a network with two inputs, define the network in two parts and join them, for example, by using a concatenation layer.
Create a dlnetwork object.
net = dlnetwork;
Define the first part of the network. Define the image classification layers and include a flatten layer and a concatenation layer before the last fully connected layer.
layers = [
imageInputLayer(imageInputSize,Normalization="none")
convolution2dLayer(filterSize,numFilters,Name="conv")
reluLayer
fullyConnectedLayer(50)
flattenLayer
concatenationLayer(1,2,Name="concat")
fullyConnectedLayer(numClasses)
softmaxLayer];
net = addLayers(net,layers);For the second part of the network, add a feature input layer and connect it to the second input of the concatenation layer.
featInput = featureInputLayer(numFeatures,Name="features"); net = addLayers(net,featInput); net = connectLayers(net,"features","concat/in2")
net =
dlnetwork with properties:
Layers: [9×1 nnet.cnn.layer.Layer]
Connections: [8×2 table]
Learnables: [6×3 table]
State: [0×3 table]
InputNames: {'imageinput' 'features'}
OutputNames: {'softmax'}
Initialized: 0
View summary with summary.
Visualize the network.
plot(net)
If you have a data set of numeric features (for example tabular data without spatial or time dimensions), then you can train a deep neural network using a feature input layer.
Read the transmission casing data from the CSV file "transmissionCasingData.csv".
filename = "transmissionCasingData.csv"; tbl = readtable(filename,TextType="String");
Convert the labels for prediction to categorical using the convertvars function.
labelName = "GearToothCondition"; tbl = convertvars(tbl,labelName,"categorical");
To train a network using categorical features, you must first convert the categorical features to numeric. First, convert the categorical predictors to categorical using the convertvars function by specifying a string array containing the names of all the categorical input variables. In this data set, there are two categorical features with names "SensorCondition" and "ShaftCondition".
categoricalPredictorNames = ["SensorCondition" "ShaftCondition"]; tbl = convertvars(tbl,categoricalPredictorNames,"categorical");
Loop over the categorical input variables. For each variable, convert the categorical values to one-hot encoded vectors using the onehotencode function.
for i = 1:numel(categoricalPredictorNames) name = categoricalPredictorNames(i); tbl.(name) = onehotencode(tbl.(name),2); end
View the first few rows of the table. Notice that the categorical predictors have been split into multiple columns.
head(tbl)
SigMean SigMedian SigRMS SigVar SigPeak SigPeak2Peak SigSkewness SigKurtosis SigCrestFactor SigMAD SigRangeCumSum SigCorrDimension SigApproxEntropy SigLyapExponent PeakFreq HighFreqPower EnvPower PeakSpecKurtosis SensorCondition ShaftCondition GearToothCondition
________ _________ ______ _______ _______ ____________ ___________ ___________ ______________ _______ ______________ ________________ ________________ _______________ ________ _____________ ________ ________________ _______________ ______________ __________________
-0.94876 -0.9722 1.3726 0.98387 0.81571 3.6314 -0.041525 2.2666 2.0514 0.8081 28562 1.1429 0.031581 79.931 0 6.75e-06 3.23e-07 162.13 0 1 1 0 No Tooth Fault
-0.97537 -0.98958 1.3937 0.99105 0.81571 3.6314 -0.023777 2.2598 2.0203 0.81017 29418 1.1362 0.037835 70.325 0 5.08e-08 9.16e-08 226.12 0 1 1 0 No Tooth Fault
1.0502 1.0267 1.4449 0.98491 2.8157 3.6314 -0.04162 2.2658 1.9487 0.80853 31710 1.1479 0.031565 125.19 0 6.74e-06 2.85e-07 162.13 0 1 0 1 No Tooth Fault
1.0227 1.0045 1.4288 0.99553 2.8157 3.6314 -0.016356 2.2483 1.9707 0.81324 30984 1.1472 0.032088 112.5 0 4.99e-06 2.4e-07 162.13 0 1 0 1 No Tooth Fault
1.0123 1.0024 1.4202 0.99233 2.8157 3.6314 -0.014701 2.2542 1.9826 0.81156 30661 1.1469 0.03287 108.86 0 3.62e-06 2.28e-07 230.39 0 1 0 1 No Tooth Fault
1.0275 1.0102 1.4338 1.0001 2.8157 3.6314 -0.02659 2.2439 1.9638 0.81589 31102 1.0985 0.033427 64.576 0 2.55e-06 1.65e-07 230.39 0 1 0 1 No Tooth Fault
1.0464 1.0275 1.4477 1.0011 2.8157 3.6314 -0.042849 2.2455 1.9449 0.81595 31665 1.1417 0.034159 98.838 0 1.73e-06 1.55e-07 230.39 0 1 0 1 No Tooth Fault
1.0459 1.0257 1.4402 0.98047 2.8157 3.6314 -0.035405 2.2757 1.955 0.80583 31554 1.1345 0.0353 44.223 0 1.11e-06 1.39e-07 230.39 0 1 0 1 No Tooth Fault
View the class names of the data set.
classNames = categories(tbl{:,labelName})classNames = 2×1 cell
{'No Tooth Fault'}
{'Tooth Fault' }
Set aside data for testing. Partition the data into a training set containing 85% of the data and a test set containing the remaining 15% of the data. To partition the data, use the trainingPartitions function, attached to this example as a supporting file. To access this file, open the example as a live script.
numObservations = size(tbl,1); [idxTrain,idxTest] = trainingPartitions(numObservations,[0.85 0.15]); tblTrain = tbl(idxTrain,:); tblTest = tbl(idxTest,:);
Convert the data to a format that the trainnet function supports. Convert the predictors and targets to numeric and categorical arrays, respectively. For feature input, the network expects data with rows that correspond to observations and columns that correspond to the features. If your data has a different layout, then you can preprocess your data to have this layout or you can provide layout information using data formats. For more information, see Deep Learning Data Formats.
predictorNames = ["SigMean" "SigMedian" "SigRMS" "SigVar" "SigPeak" "SigPeak2Peak" ... "SigSkewness" "SigKurtosis" "SigCrestFactor" "SigMAD" "SigRangeCumSum" ... "SigCorrDimension" "SigApproxEntropy" "SigLyapExponent" "PeakFreq" ... "HighFreqPower" "EnvPower" "PeakSpecKurtosis" "SensorCondition" "ShaftCondition"]; XTrain = table2array(tblTrain(:,predictorNames)); TTrain = tblTrain.(labelName); XTest = table2array(tblTest(:,predictorNames)); TTest = tblTest.(labelName);
Define a network with a feature input layer and specify the number of features. Also, configure the input layer to normalize the data using Z-score normalization.
numFeatures = size(XTrain,2);
numClasses = numel(classNames);
layers = [
featureInputLayer(numFeatures,Normalization="zscore")
fullyConnectedLayer(16)
layerNormalizationLayer
reluLayer
fullyConnectedLayer(numClasses)
softmaxLayer];Specify the training options:
Train using the L-BFGS solver. This solver suits tasks with small networks and when the data fits in memory.
Train using the CPU. Because the network and data is small, the CPU is better suited.
Display the training progress in a plot.
Suppress the verbose output.
options = trainingOptions("lbfgs", ... ExecutionEnvironment="cpu", ... Plots="training-progress", ... Verbose=false);
Train the network using the trainnet function. For classification, use cross-entropy loss.
net = trainnet(XTrain,TTrain,layers,"crossentropy",options);
Test the network using the labeled test set. For single-label classification, evaluate the accuracy. The accuracy is the percentage of the labels that the network predicts correctly.
accuracy = testnet(net,XTest,TTest,"accuracy")accuracy = 100
Predict the labels of the test data using the trained network. Predict the classification scores using the trained network then convert the predictions to labels using the scores2label function.
scoresTest = minibatchpredict(net,XTest); YTest = scores2label(scoresTest,classNames);
Visualize the predictions in a confusion chart.
confusionchart(TTest,YTest)
