predict
Class: Autoencoder
Reconstruct the inputs using trained autoencoder
Syntax
Description
Input Arguments
autoenc
— Trained autoencoder
Autoencoder
object
Trained autoencoder, returned as an object of the Autoencoder
class.
Xnew
— Input data
matrix | cell array of image data | array of single image data
Input data, specified as a matrix of samples, a cell array of image data, or an array of single image data.
If the autoencoder autoenc
was trained
on a matrix, where each column represents a single sample, then Xnew
must
be a matrix, where each column represents a single sample.
If the autoencoder autoenc
was trained
on a cell array of images, then Xnew
must either
be a cell array of image data or an array of single image data.
Data Types: single
| double
| cell
Output Arguments
Y
— Predictions for the input data Xnew
matrix | cell array of image data | array of single image data
Predictions for the input data Xnew
, returned
as a matrix or a cell array of image data.
If
Xnew
is a matrix, thenY
is also a matrix, where each column corresponds to a single sample (observation or example).If
Xnew
is a cell array of image data, thenY
is also a cell array of image data, where each cell contains the data for a single image.If
Xnew
is an array of a single image data, thenY
is also an array of a single image data.
Examples
Predict Continuous Measurements Using Trained Autoencoder
Load the training data.
X = iris_dataset;
The training data contains measurements on four attributes of iris flowers: Sepal length, sepal width, petal length, petal width.
Train an autoencoder on the training data using the positive saturating linear transfer function in the encoder and linear transfer function in the decoder.
autoenc = trainAutoencoder(X,'EncoderTransferFunction',... 'satlin','DecoderTransferFunction','purelin');
Reconstruct the measurements using the trained network, autoenc
.
xReconstructed = predict(autoenc,X);
Plot the predicted measurement values along with the actual values in the training dataset.
for i = 1:4 h(i) = subplot(1,4,i); plot(X(i,:),'r.'); hold on plot(xReconstructed(i,:),'go'); hold off; end title(h(1),{'Sepal';'Length'}); title(h(2),{'Sepal';'Width'}); title(h(3),{'Petal';'Length'}); title(h(4),{'Petal';'Width'});
The red dots represent the training data and the green circles represent the reconstructed data.
Reconstruct Handwritten Digit Images Using Sparse Autoencoder
Load the training data.
XTrain = digitTrainCellArrayData;
The training data is a 1-by-5000 cell array, where each cell containing a 28-by-28 matrix representing a synthetic image of a handwritten digit.
Train an autoencoder with a hidden layer containing 25 neurons.
hiddenSize = 25; autoenc = trainAutoencoder(XTrain,hiddenSize,... 'L2WeightRegularization',0.004,... 'SparsityRegularization',4,... 'SparsityProportion',0.15);
Load the test data.
XTest = digitTestCellArrayData;
The test data is a 1-by-5000 cell array, with each cell containing a 28-by-28 matrix representing a synthetic image of a handwritten digit.
Reconstruct the test image data using the trained autoencoder, autoenc
.
xReconstructed = predict(autoenc,XTest);
View the actual test data.
figure; for i = 1:20 subplot(4,5,i); imshow(XTest{i}); end
View the reconstructed test data.
figure; for i = 1:20 subplot(4,5,i); imshow(xReconstructed{i}); end
Version History
Introduced in R2015b
See Also
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