encode

Class: Autoencoder

Encode input data

Syntax

Z = encode(autoenc,Xnew)

Description

Z = encode(autoenc,Xnew) returns the encoded data, Z, for the input data Xnew, using the autoencoder, autoenc.

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

Z — Data encoded by autoenc

matrix

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

Data Types: single | double

Examples

Encode Decoded Data for New Images

Load the sample 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 with a hidden size of 50 using the training data.

autoenc = trainAutoencoder(X,50);

Encode decoded data for new image data.

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

Xnew is a 1-by-5000 cell array. Z is a 50-by-5000 matrix, where each column represents the image data of one handwritten digit in the new data Xnew.

Algorithms

If the input to an autoencoder is a vector $$x\in {ℝ}^{D_x}$$, then the encoder maps the vector x to another vector $$z\in {ℝ}^{D^{\left(1\right)}}$$ as follows:

$$z=h^{{}^{\left(1\right)}}\left(W^{\left(1\right)}x+b^{\left(1\right)}\right),$$

where the superscript (1) indicates the first layer. $$h^{\left(1\right)}:{ℝ}^{D^{\left(1\right)}}\to {ℝ}^{D^{\left(1\right)}}$$ is a transfer function for the encoder, $$W^{\left(1\right)}\in {ℝ}^{D^{\left(1\right)}\times D_{{}^x}}$$ is a weight matrix, and $$b^{\left(1\right)}\in {ℝ}^{D^{\left(1\right)}}$$ is a bias vector.

Version History

Introduced in R2015b