Main Content

List of Functions with dlarray Support

Deep Learning Toolbox Functions with dlarray Support

These tables list and briefly describe the Deep Learning Toolbox™ functions that operate on dlarray objects.

Deep Learning Operations

FunctionDescription
avgpoolThe average pooling operation performs downsampling by dividing the input into pooling regions and computing the average value of each region.
batchnormThe batch normalization operation normalizes each input channel across a mini-batch. To speed up training of convolutional neural networks and reduce the sensitivity to network initialization, use batch normalization between convolution and nonlinear operations such as relu.
crossentropyThe cross-entropy operation computes the cross-entropy loss between network predictions and target values for single-label and multi-label classification tasks.
crosschannelnormThe cross-channel normalization operation uses local responses in different channels to normalize each activation. Cross-channel normalization typically follows a relu operation. Cross-channel normalization is also known as local response normalization.
dlconvThe convolution operation applies sliding filters to the input data. Use 1-D and 2-D filters with ungrouped or grouped convolutions and 3-D filters with ungrouped convolutions.
dltranspconvThe transposed convolution operation upsamples feature maps.
embedThe embed operation converts numeric indices to numeric vectors, where the indices correspond to discrete data. Use embeddings to map discrete data such as categorical values or words to numeric vectors.
fullyconnectThe fully connect operation multiplies the input by a weight matrix and then adds a bias vector.
groupnormThe group normalization operation divides the channels of the input data into groups and normalizes the activations across each group. To speed up training of convolutional neural networks and reduce the sensitivity to network initialization, use group normalization between convolution and nonlinear operations such as relu. You can perform instance normalization and layer normalization by setting the appropriate number of groups.
gruThe gated recurrent unit (GRU) operation allows a network to learn dependencies between time steps in time series and sequence data.
leakyreluThe leaky rectified linear unit (ReLU) activation operation performs a nonlinear threshold operation, where any input value less than zero is multiplied by a fixed scale factor.
lstmThe long short-term memory (LSTM) operation allows a network to learn long-term dependencies between time steps in time series and sequence data.
maxpoolThe maximum pooling operation performs downsampling by dividing the input into pooling regions and computing the maximum value of each region.
maxunpoolThe maximum unpooling operation unpools the output of a maximum pooling operation by upsampling and padding with zeros.
mseThe half mean squared error operation computes the half mean squared error loss between network predictions and target values for regression tasks.
reluThe rectified linear unit (ReLU) activation operation performs a nonlinear threshold operation, where any input value less than zero is set to zero.
onehotdecode

The one-hot decode operation decodes probability vectors, such as the output of a classification network, into classification labels.

The input A can be a dlarray. If A is formatted, the function ignores the data format.

sigmoidThe sigmoid activation operation applies the sigmoid function to the input data.
softmaxThe softmax activation operation applies the softmax function to the channel dimension of the input data.

dlarray-Specific Functions

FunctionDescription
dimsThis function returns the data format of a dlarray.
dlfevalThis function evaluates a dlarray function using automatic differentiation.
dlgradientThis function computes gradients using automatic differentiation.
dlresize (Image Processing Toolbox)This function resizes the spatial dimensions of a dlarray.
extractdataThis function extracts the data from a dlarray.
finddimThis function finds the indices of dlarray dimensions with a given dimension label.
stripdimsThis function removes the data format from a dlarray.

MATLAB Functions with dlarray Support

Many MATLAB® functions operate on dlarray objects. These tables list the usage notes and limitations for these functions when you use dlarray arguments.

Unary Element-wise Functions

FunctionNotes and Limitations
abs

The output dlarray has the same data format as the input dlarray.

acos
  • The output dlarray has the same data format as the input dlarray.

  • Because dlarray does not support complex numbers, each element x in the input dlarray must have x∣ ≤ 1 .

acosh
  • The output dlarray has the same data format as the input dlarray.

  • Because dlarray does not support complex numbers, each element x in the input dlarray must have x ≥ 1.

acot

The output dlarray has the same data format as the input dlarray.

acsc
  • The output dlarray has the same data format as the input dlarray.

  • Because dlarray does not support complex numbers, each element x in the input dlarray must have x∣ ≥ 1.

asec
asin
  • The output dlarray has the same data format as the input dlarray.

  • Because dlarray does not support complex numbers, each element x in the input dlarray must have x∣ ≤ 1.

asinh

The output dlarray has the same data format as the input dlarray.

atan
atan2
atanh
  • The output dlarray has the same data format as the input dlarray.

  • Because dlarray does not support complex numbers, each element x in the input dlarray must have x∣ ≤ 1.

cos

The output dlarray has the same data format as the input dlarray.

cosh
cot
csc
exp
log
  • The output dlarray has the same data format as the input dlarray.

  • Because dlarray does not support complex numbers, the input dlarray must have nonnegative values.

sec

The output dlarray has the same data format as the input dlarray.

sign
sin
sinh
sqrt
  • The output dlarray has the same data format as the input dlarray.

  • Because dlarray does not support complex numbers, the input dlarray must have nonnegative values.

tan

The output dlarray has the same data format as the input dlarray.

tanh
uminus, -
uplus, +

Binary Element-wise Operators

FunctionNotes and Limitations
minus, -

If the two dlarray inputs are formatted, then the output dlarray is formatted with a combination of both of their data formats. The function uses implicit expansion to combine the inputs. For more information, see Implicit Expansion with Data Formats.

plus, +
power, .^
  • If the two dlarray inputs are formatted, then the output dlarray is formatted with a combination of both of their data formats. The function uses implicit expansion to combine the inputs. For more information, see Implicit Expansion with Data Formats.

  • Because dlarray does not support complex numbers, the software generates an error if any element of the output is complex.

rdivide, ./

If the two dlarray inputs are formatted, then the output dlarray is formatted with a combination of both of their data formats. The function uses implicit expansion to combine the inputs. For more information, see Implicit Expansion with Data Formats.

times, .*

Reduction Functions

FunctionNotes and Limitations
mean
  • The output dlarray has the same data format as the input dlarray.

  • The 'omitnan' option is not supported.

  • If the input dlarray is on the GPU, the 'native' option is not supported.

prod
  • The output dlarray has the same data format as the input dlarray.

  • The 'omitnan' option is not supported.

sum

Extrema Functions

FunctionNotes and Limitations
ceil

The output dlarray has the same data format as the input dlarray.

eps
  • The output dlarray has the same data format as the input dlarray.

  • Use eps(ones(‘like’, x)) to get a scalar epsilon value based on the data type of a dlarray x.

fix

The output dlarray has the same data format as the input dlarray.

floor

The output dlarray has the same data format as the input dlarray.

max
  • When you find the maximum or minimum elements of a single dlarray, the output dlarray has the same data format as the input dlarray.

  • When you find the maximum or minimum elements between two formatted dlarray inputs, the output dlarray has a combination of both of their data formats. The function uses implicit expansion to combine the inputs. For more information, see Implicit Expansion with Data Formats.

min
rescale
  • If the first input dlarray A is unformatted, all additional inputs must be unformatted.

  • If the first input dlarray A is formatted, all additional inputs must either be unformatted scalars, or have data formats that are a subset of the data format of A. In this case, each dimension must either be singleton or match the length of the corresponding dimension of A.

round
  • Only the syntax Y = round(X) is supported.

  • The output dlarray has the same data format as the input dlarray.

Other Math Operations

FunctionNotes and Limitations
colon, :
  • The supported operations are:

    • a:b

    • a:b:c

    For information on indexing into a dlarray, see Indexing.

  • All inputs must be real scalars. The output dlarray is unformatted.

interp1
  • Sample points input x must be a finite, increasing vector without repeating elements.

  • method must be 'linear' or 'nearest'.

  • The piecewise polynomial syntax ('pp') is not supported.

  • Only the sample values input v can be a formatted dlarray. All other inputs must be unformatted. If v is a formatted dlarray, query points input xq must be a vector, and the output vq has the same data format as v.

mrdivide, /

The second dlarray input must be a scalar. The output dlarray has the same data format as the first dlarray input.

mtimes, *

One input can be a formatted dlarray only when the other input is an unformatted scalar. In this case, the output dlarray has the same data format as the formatted dlarray input.

pagemtimes

One input can be a formatted dlarray only when the other input is unformatted, with scalar pages. In this case, the output dlarray has the same data format as the formatted dlarray input.

Logical Operations

FunctionNotes and Limitations
all

The output dlarray has the same data format as the input dlarray.

and, &

If the two dlarray inputs are formatted, then the output dlarray is formatted with a combination of both of their data formats. The function uses implicit expansion to combine the inputs. For more information, see Implicit Expansion with Data Formats.

any

The output dlarray has the same data format as the input dlarray.

eq, ==

If the two dlarray inputs are formatted, then the output dlarray is formatted with a combination of both of their data formats. The function uses implicit expansion to combine the inputs. For more information, see Implicit Expansion with Data Formats.

ge, >=
gt, >
le, <=
lt, <
ne, ~=
not, ~

The output dlarray has the same data format as the input dlarray.

or, |

If the two dlarray inputs are formatted, then the output dlarray is formatted with a combination of both of their data formats. The function uses implicit expansion to combine the inputs. For more information, see Implicit Expansion with Data Formats.

xor

Size Manipulation Functions

FunctionNotes and Limitations
reshape

The output dlarray is unformatted, even if the input dlarray is formatted.

squeeze

Two-dimensional dlarray objects are unaffected by squeeze. If the input dlarray is formatted, the function removes dimension labels belonging to singleton dimensions. If the input dlarray has more than two dimensions and its third and above dimensions are singleton, then the function discards these dimensions and their labels.

Transposition Operations

FunctionNotes and Limitations
ctranspose, '

If the input dlarray is formatted, then the labels of both dimensions must be the same. The function performs transposition implicitly, and transposes directly only if necessary for other operations.

permute

If the input dlarray is formatted, then the permutation must be among only those dimensions that have the same label. The function performs permutations implicitly, and permutes directly only if necessary for other operations.

transpose, .'

If the input dlarray is formatted, then the labels of both dimensions must be the same. The function performs transposition implicitly, and transposes directly only if necessary for other operations.

Concatenation Functions

FunctionNotes and Limitations
cat

The dlarray inputs must have matching formats or be unformatted. Mixed formatted and unformatted inputs are supported. If any dlarray inputs are formatted, then the output dlarray is formatted with the same data format.

horzcat
vertcat

Conversion Functions

FunctionNotes and Limitations
cast
  • cast(dlA,newdatatype) copies the data in the dlarray dlA into a dlarray of the underlying data type newdatatype. The newdatatype option must be 'double', 'single', or 'logical'. The output dlarray is formatted with the same data format as dlA.

  • cast(A,'like',Y) returns an array of the same type as Y. If Y is a dlarray, then the output is a dlarray that has the same underlying data type as Y. If Y is on the GPU, then the output is on the GPU. If both A and Y are dlarray objects, then the output dlarray is formatted with the same data format as the input A.

double

The output is a dlarray that contains data of type double.

gather (Parallel Computing Toolbox)
  • The supported syntaxes are:

    • dlX = gather(dlA)

    • [dlX,dlY,dlZ,...] = gather(dlA,dlB,dlC,...)

  • gather(dlA) returns a dlarray containing numeric or logical data. This function applies gather to the underlying data in the dlarray dlA. If dlA is on the GPU, then dlX is in the local workspace, not on the GPU. If dlA is in the local workspace (not on the GPU), then dlX is equal to dlA.

  • gather(dlA,dlB,dlC,...) gathers multiple arrays.

gpuArray (Parallel Computing Toolbox)
  • This function requires Parallel Computing Toolbox™.

  • gpuArray returns a dlarray containing a gpuArray. This function applies gpuArray to the underlying data. If the input dlarray is in the local workspace, then its data is moved to the GPU and internally represented as a gpuArray. If the input dlarray is on the GPU, then the output dlarray is equal to the input dlarray.

logicalThe output is a dlarray that contains data of type logical.
singleThe output is a dlarray that contains data of type single.

Comparison Functions

FunctionNotes and Limitations
isequal
  • The syntax with more than two input arguments is not supported.

  • Two dlarray inputs are equal if the numeric data they represent are equal and if they both are either formatted with the same data format or unformatted.

isequaln
  • The syntax with more than two input arguments is not supported.

  • Two dlarray inputs are equal if the numeric data they represent are equal (treating NaNs as equal) and if they both are either formatted with the same data format or unformatted.

Data Type and Value Identification Functions

FunctionNotes and Limitations
isdlarray N/A
isfinite

The software applies the function to the underlying data of an input dlarray.

isfloat
isgpuarray (Parallel Computing Toolbox)
isinf
islogical
isnan
isnumeric
isreal

Because dlarray does not support complex numbers, this function always returns true for a dlarray input.

isUnderlyingTypeN/A
mustBeUnderlyingType
underlyingType

Size Identification Functions

FunctionNotes and Limitations
iscolumnThis function returns true for a dlarray that is a column vector, where each dimension except the first is a singleton. For example, a 3-by-1-by-1 dlarray is a column vector.
ismatrixThis function returns true for dlarray objects with only two dimensions and for dlarray objects where each dimension except the first two is a singleton. For example, a 3-by-4-by-1 dlarray is a matrix.
isrowThis function returns true for a dlarray that is a row vector, where each dimension except the second is a singleton. For example, a 1-by-3-by-1 dlarray is a row vector.
isscalarN/A
isvectorThis function returns true for a dlarray that is a row vector or column vector. Note that isvector does not consider a 1-by-1-by-3 dlarray to be a vector.
lengthN/A
ndims

If the input dlarray dlX is formatted, then ndims(dlX) returns the number of dimension labels, even if some of the labeled dimensions are trailing singleton dimensions.

numelN/A
size

If the input dlarray dlX is formatted, then size(dlX) returns a vector of length equal to the number of dimension labels, even if some of the labeled dimensions are trailing singleton dimensions.

Creator Functions

FunctionNotes and Limitations
falseOnly the 'like' syntax is supported for dlarray.
inf
nan
ones
rand
randi
randn
true
zeros

String and Character Functions

FunctionNotes and Limitations
composeN/A
fprintf
int2str
mat2str
num2str
sprintf

Notable dlarray Behaviors

Implicit Expansion with Data Formats

Some functions use implicit expansion to combine two formatted dlarray inputs. The function introduces labeled singleton dimensions (dimensions of size 1) into the inputs, as necessary, to make their formats match. The function inserts singleton dimensions at the end of each block of dimensions with the same label.

To see an example of this behavior, enter the following code.

X = ones(2,3,2);
dlX = dlarray(X,'SCB')
Y = 1:3;
dlY = dlarray(Y,'C')
dlZ = dlX.*dlY
dlX = 

  2(S) × 3(C) × 2(B) dlarray


(:,:,1) =

     1     1     1
     1     1     1


(:,:,2) =

     1     1     1
     1     1     1


dlY = 

  3(C) × 1(U) dlarray

     1
     2
     3


dlZ = 

  2(S) × 3(C) × 2(B) dlarray


(:,:,1) =

     1     2     3
     1     2     3


(:,:,2) =

     1     2     3
     1     2     3
In this example, dlZ(i,j,k) = dlX(i,j,k).*dlY(j) for indices i, j, and k. The second dimension of dlZ (labeled 'C') corresponds to the second dimension of dlX and the first dimension of dlY.

In general, the format of one dlarray input does not need to be a subset of the format of another dlarray input. For example, if dlX and dlY are input arguments with dims(dlX) = 'SCB' and dims(dlY) = 'SSCT', then the output dlZ has dims(dlZ) = 'SSCBT'. The 'S' dimension of dlX maps to the first 'S' dimension of dlY.

Special 'U' Dimension Behavior

The 'U' dimension of a dlarray behaves differently from other labeled dimensions in that it exhibits the standard MATLAB singleton dimension behavior. You can think of a formatted dlarray as having infinitely many 'U' dimensions of size 1 following the dimensions returned by size.

The software discards a 'U' label unless the dimension is nonsingleton or it is one of the first two dimensions of the dlarray.

To see an example of this behavior, enter the following code.

X = ones(2,2);
dlX = dlarray(X,'SC')
dlX(:,:,2) = 2
dlX = 

  2(S) × 2(C) dlarray

     1     1
     1     1


dlX = 

  2(S) × 2(C) × 2(U) dlarray


(:,:,1) =

     1     1
     1     1


(:,:,2) =

     2     2
     2     2
In this example, the software expands a formatted two-dimensional dlarray to a three-dimensional dlarray, and labels the third dimension with 'U' by default. For an example of how the 'U' dimension is used in implicit expansion, see Implicit Expansion with Data Formats.

Indexing

Indexing with a dlarray is supported and exhibits the following behaviors:

  • dlX(idx1,...,idxn) returns a dlarray with the same data format as dlX if n is greater than or equal to ndims(dlX). Otherwise, it returns an unformatted dlarray.

  • If you set dlY(idx1,...,idxn) = dlX, then the data format of dlY is preserved, although the software might add or remove trailing 'U' dimension labels. The data format of dlX has no impact on this operation.

  • If you delete parts of a dlarray using dlX(idx1,…,idxn) = [], then the data format of dlX is preserved if n is greater than or equal to ndims(dlX). Otherwise, dlX is returned unformatted.

Round-off Error

When you use a function with a dlarray input, the order of the operations within the function can change based on the internal storage order of the dlarray. This change can result in differences on the order of round-off for two dlarray objects that are otherwise equal.

See Also

| | |

Related Topics