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decode

Class: Autoencoder

Decode encoded data

Syntax

Y = decode(autoenc,Z)

Description

example

Y = decode(autoenc,Z)returns the decoded data Y, using the autoencoder autoenc.

Input Arguments

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Trained autoencoder, returned by the trainAutoencoder function as an object of the Autoencoder class.

Data encoded by autoenc, specified as a matrix. Each column of Z represents an encoded sample (observation).

Data Types: single | double

Output Arguments

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Decoded data, returned as a matrix or a cell array of image data.

If the autoencoder autoenc was trained on a cell array of image data, then Y is also a cell array of images.

If the autoencoder autoenc was trained on a matrix, then Y is also a matrix, where each column of Y corresponds to one sample or observation.

Examples

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Load the training data.

X = digitTrainCellArrayData;

X is a 1-by-5000 cell array, where each cell contains a 28-by-28 matrix representing a synthetic image of a handwritten digit.

Train an autoencoder using the training data with a hidden size of 15.

hiddenSize = 15;
autoenc = trainAutoencoder(X,hiddenSize);

Extract the encoded data for new images using the autoencoder.

Xnew = digitTestCellArrayData;
features = encode(autoenc,Xnew);

Decode the encoded data from the autoencoder.

Y = decode(autoenc,features);

Y is a 1-by-5000 cell array, where each cell contains a 28-by-28 matrix representing a synthetic image of a handwritten digit.

Algorithms

If the input to an autoencoder is a vector xDx, then the encoder maps the vector x to another vector zD(1) as follows:

z=h(1)(W(1)x+b(1)),

where the superscript (1) indicates the first layer. h(1):D(1)D(1) is a transfer function for the encoder, W(1)D(1)×Dx is a weight matrix, and b(1)D(1) is a bias vector. Then, the decoder maps the encoded representation z back into an estimate of the original input vector, x, as follows:

x^=h(2)(W(2)z+b(2)),

where the superscript (2) represents the second layer. h(2):DxDx is the transfer function for the decoder,W(1)Dx×D(1) is a weight matrix, and b(2)Dx is a bias vector.

Introduced in R2015b