mse

Half mean squared error

Since R2019b

Syntax

loss = mse(Y,targets)

loss = mse(Y,targets,'DataFormat',FMT)

Description

The half mean squared error operation computes the half mean squared error loss between network predictions and target values for regression tasks.

The loss is calculated using the following formula

$$\text{loss}=\frac{1}{2N}{\displaystyle \sum _{i=1}^M{(X_i-T_i)}^2}$$

where X_i is the network prediction, T_i is the target value, M is the total number of responses in X (across all observations), and N is the total number of observations in X.

Note

This function computes the half mean squared error loss between predictions and targets stored as dlarray data. If you want to calculate the half mean squared error loss within a layerGraph object or Layer array for use with trainNetwork, use the following layer:

• regressionLayer

loss = mse(Y,targets) computes the half mean squared error loss between the predictions Y and the target values targets for regression problems. The input Y must be a formatted dlarray. The output loss is an unformatted dlarray scalar.

loss = mse(Y,targets,'DataFormat',FMT) also specifies the dimension format FMT when Y is not a formatted dlarray.

Examples

Find Half Mean Squared Error Between Predicted and Target Values

The half mean squared error evaluates how well the network predictions correspond to the target values.

Create the input predictions as a single observation of random values with a height and width of six and a single channel.

height = 6;
width = 6;
channels = 1;
observations = 1;

Y = rand(height,width,channels,observations);
Y = dlarray(Y,'SSCB')

Create the target values as a numeric array with the same dimension order as the input data Y.

targets = ones(height,width,channels,observations);

Compute the half mean squared error between the predictions and the targets.

loss = mse(Y,targets)

loss =

  1x1 dlarray

    5.2061

Input Arguments

Y — Predictions

dlarray | numeric array

Predictions, specified as a formatted dlarray, an unformatted dlarray, or a numeric array. When Y is not a formatted dlarray, you must specify the dimension format using the DataFormat option.

If Y is a numeric array, targets must be a dlarray.

targets — Target responses

dlarray | numeric array

Target responses, specified as a formatted or unformatted dlarray or a numeric array.

The size of each dimension of targets must match the size of the corresponding dimension of Y.

If targets is a formatted dlarray, then its format must be the same as the format of Y, or the same as DataFormat if Y is unformatted.

If targets is an unformatted dlarray or a numeric array, then the function applies the format of Y or the value of DataFormat to targets.

Tip

Formatted dlarray objects automatically permute the dimensions of the underlying data to have order "S" (spatial), "C" (channel), "B" (batch), "T" (time), then "U" (unspecified). To ensure that the dimensions of Y and targets are consistent, when Y is a formatted dlarray, also specify targets as a formatted dlarray.

FMT — Dimension order of unformatted data

char array | string

Dimension order of unformatted input data, specified as the comma-separated pair consisting of 'DataFormat' and a character array or string FMT that provides a label for each dimension of the data. Each character in FMT must be one of the following:

• 'S' — Spatial

• 'C' — Channel

• 'B' — Batch (for example, samples and observations)

• 'T' — Time (for example, sequences)

• 'U' — Unspecified

You can specify multiple dimensions labeled 'S' or 'U'. You can use the labels 'C', 'B', and 'T' at most once.

You must specify 'DataFormat',FMT when the input data is not a formatted dlarray.

Example: 'DataFormat','SSCB'

Data Types: char | string

Output Arguments

loss — Half mean squared error loss

dlarray scalar

Half mean squared error loss, returned as an unformatted dlarray scalar. The output loss has the same underlying data type as the input Y.

More About

Half Mean Squared Error Loss

The mse function computes the half-mean-squared-error loss for regression problems. For more information, see the definition of Regression Output Layer on the RegressionOutputLayer reference page.

Extended Capabilities

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Usage notes and limitations:

• When at least one of the following input arguments is a gpuArray or a dlarray with underlying data of type gpuArray, this function runs on the GPU:

  • Y

  • targets

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Version History

Introduced in R2019b