RegressionOutputLayer

Regression output layer

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Description

A regression layer computes the half-mean-squared-error loss for regression problems.

Creation

Create a regression output layer using regressionLayer.

Properties

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Regression Output

`ResponseNames` — Names of responses
`{}` (default) | cell array of character vectors | string array

Names of the responses, specified a cell array of character vectors or a string array. At training time, the software automatically sets the response names according to the training data. The default is {}.

Data Types: cell

`LossFunction` — Loss function for training
`'mean-squared-error'`

Loss function the software uses for training, specified as 'mean-squared-error'.

Layer

`Name` — Layer name
`''` (default) | character vector | string scalar

Layer name, specified as a character vector or a string scalar. To include a layer in a layer graph, you must specify a nonempty unique layer name. If you train a series network with the layer and Name is set to '', then the software automatically assigns a name to the layer at training time.

Data Types: char | string

`NumInputs` — Number of inputs
1 (default)

Number of inputs of the layer. This layer accepts a single input only.

Data Types: double

`InputNames` — Input names
`{'in'}` (default)

Input names of the layer. This layer accepts a single input only.

Data Types: cell

`NumOutputs` — Number of outputs
0 (default)

Number of outputs of the layer. The layer has no outputs.

Data Types: double

`OutputNames` — Output names
`{}` (default)

Output names of the layer. The layer has no outputs.

Data Types: cell

Examples

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Create Regression Output Layer

Open Live Script

Create a regression output layer with the name 'routput'.

layer = regressionLayer('Name','routput')

layer = 
  RegressionOutputLayer with properties:

             Name: 'routput'
    ResponseNames: {}

   Hyperparameters
     LossFunction: 'mean-squared-error'

The default loss function for regression is mean-squared-error.

Include a regression output layer in a Layer array.

layers = [ ...
    imageInputLayer([28 28 1])
    convolution2dLayer(12,25)
    reluLayer
    fullyConnectedLayer(1)
    regressionLayer]

layers = 
  5x1 Layer array with layers:

     1   ''   Image Input         28x28x1 images with 'zerocenter' normalization
     2   ''   Convolution         25 12x12 convolutions with stride [1  1] and padding [0  0  0  0]
     3   ''   ReLU                ReLU
     4   ''   Fully Connected     1 fully connected layer
     5   ''   Regression Output   mean-squared-error

More About

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Regression Output Layer

A regression layer computes the half-mean-squared-error loss for regression problems. For typical regression problems, a regression layer must follow the final fully connected layer.

For a single observation, the mean-squared-error is given by:

$MSE = \sum_{i = 1}^{R} \frac{{(t_{i} - y_{i})}^{2}}{R},$

where R is the number of responses, t_i is the target output, and y_i is the network’s prediction for response i.

For image and sequence-to-one regression networks, the loss function of the regression layer is the half-mean-squared-error of the predicted responses, not normalized by R:

$loss = \frac{1}{2} \sum_{i = 1}^{R} {(t_{i} - y_{i})}^{2} .$

For image-to-image regression networks, the loss function of the regression layer is the half-mean-squared-error of the predicted responses for each pixel, not normalized by R:

$loss = \frac{1}{2} \sum_{p = 1}^{H W C} {(t_{p} - y_{p})}^{2},$

where H, W, and C denote the height, width, and number of channels of the output respectively, and p indexes into each element (pixel) of t and y linearly.

For sequence-to-sequence regression networks, the loss function of the regression layer is the half-mean-squared-error of the predicted responses for each time step, not normalized by R:

$loss = \frac{1}{2 S} \sum_{i = 1}^{S} \sum_{j = 1}^{R} {(t_{i j} - y_{i j})}^{2},$

where S is the sequence length.

When training, the software calculates the mean loss over the observations in the mini-batch.

Documentation

RegressionOutputLayer

Description

Creation

Properties

Regression Output

`ResponseNames` — Names of responses
`{}` (default) | cell array of character vectors | string array

`LossFunction` — Loss function for training
`'mean-squared-error'`

Layer

`Name` — Layer name
`''` (default) | character vector | string scalar

`NumInputs` — Number of inputs
1 (default)

`InputNames` — Input names
`{'in'}` (default)

`NumOutputs` — Number of outputs
0 (default)

`OutputNames` — Output names
`{}` (default)

Examples

Create Regression Output Layer

More About

Regression Output Layer

See Also

Topics

Deep Learning Toolbox Documentation

Support

Documentation

RegressionOutputLayer

Description

Creation

Properties

Regression Output

ResponseNames — Names of responses {} (default) | cell array of character vectors | string array

LossFunction — Loss function for training 'mean-squared-error'

Layer

Name — Layer name '' (default) | character vector | string scalar

NumInputs — Number of inputs 1 (default)

InputNames — Input names {'in'} (default)

NumOutputs — Number of outputs 0 (default)

OutputNames — Output names {} (default)

Examples

Create Regression Output Layer

More About

Regression Output Layer

See Also

Topics

Deep Learning Toolbox Documentation

Support

`ResponseNames` — Names of responses
`{}` (default) | cell array of character vectors | string array

`LossFunction` — Loss function for training
`'mean-squared-error'`

`Name` — Layer name
`''` (default) | character vector | string scalar

`NumInputs` — Number of inputs
1 (default)

`InputNames` — Input names
`{'in'}` (default)

`NumOutputs` — Number of outputs
0 (default)

`OutputNames` — Output names
`{}` (default)