gruLayer
Description
A GRU layer is an RNN layer that learns dependencies between time steps in timeseries and sequence data.
Creation
Description
creates a GRU layer and sets the layer
= gruLayer(numHiddenUnits
)NumHiddenUnits
property.
sets additional layer
= gruLayer(numHiddenUnits
,Name,Value
)OutputMode
, Activations, State, Parameters and Initialization, Learning Rate and Regularization, and
Name
properties
using one or more namevalue pair arguments. You can specify multiple namevalue
pair arguments. Enclose each property name in quotes.
Properties
GRU
NumHiddenUnits
— Number of hidden units
positive integer
Number of hidden units (also known as the hidden size), specified as a positive integer.
The number of hidden units corresponds to the amount of information that the layer remembers between time steps (the hidden state). The hidden state can contain information from all the previous time steps, regardless of the sequence length. If the number of hidden units is too large, then the layer can overfit to the training data. The hidden state does not limit the number of time steps that the layer processes in an iteration.
The layer outputs data with NumHiddenUnits
channels.
To set this property, use the numHiddenUnits
argument when you
create the GRULayer
object. After you create a
GRULayer
object, this property is readonly.
Data Types: single
 double
 int8
 int16
 int32
 int64
 uint8
 uint16
 uint32
 uint64
OutputMode
— Output mode
"sequence"
(default)  "last"
Output mode, specified as one of these values:
"sequence"
— Output the complete sequence."last"
— Output the last time step of the sequence.
The GRULayer
object stores this property as a character vector.
To set this property, use the corresponding namevalue argument when you create the GRULayer
object. After you create a GRULayer
object, this property is readonly.
HasStateInputs
— Flag for state inputs to layer
0
(false
) (default)  1
(true
)
Flag for state inputs to the layer, specified as 0
(false
) or 1
(true
).
If the HasStateInputs
property is 0
(false
), then the layer has one input with the name
"in"
, which corresponds to the input data. In this case, the layer
uses the HiddenState
property for the layer
operation.
If the HasStateInputs
property is 1
(true
), then the layer has two inputs with the names
"in"
and "hidden"
, which correspond to the input
data and hidden state, respectively. In this case, the layer uses the values that the
network passes to these inputs for the layer operation. If HasStateInputs
is 1
(true
), then the
HiddenState
property must be empty.
To set this property, use the corresponding namevalue argument when you create the GRULayer
object. After you create a GRULayer
object, this property is readonly.
HasStateOutputs
— Flag for state outputs from layer
0
(false
) (default)  1
(true
)
Flag for state outputs from the layer, specified as 0
(false
) or 1
(true
).
If the HasStateOutputs
property is 0
(false
), then the layer has one output with the name
"out"
, which corresponds to the output data.
If the HasStateOutputs
property is 1
(true
), then the layer has two outputs with the names
"out"
and "hidden"
, which correspond
to the output data and hidden state, respectively. In this case, the layer also
outputs the state values computed during the layer operation.
To set this property, use the corresponding namevalue argument when you create the GRULayer
object. After you create a GRULayer
object, this property is readonly.
ResetGateMode
— Reset gate mode
"aftermultiplication"
(default) 
"beforemultiplication"

"recurrentbiasaftermultiplication"
Reset gate mode, specified as one of these values:
"aftermultiplication"
— Apply the reset gate after matrix multiplication. This option is cuDNN compatible."beforemultiplication"
— Apply the reset gate before matrix multiplication."recurrentbiasaftermultiplication"
— Apply the reset gate after matrix multiplication and use an additional set of bias terms for the recurrent weights.
For more information about the reset gate calculations, see Gated Recurrent Unit Layer.
Before R2023a: dlnetwork
objects support GRU layers with the ResetGateMode
set
to "aftermultiplication"
only.
InputSize
— Input size
"auto"
(default)  positive integer
This property is readonly.
Input size, specified as a positive integer or "auto"
. If
InputSize
is "auto"
, then the software
automatically assigns the input size at training time.
If InputSize
is "auto"
, then the
GRULayer
object stores this property as a character
vector.
Data Types: double
 char
 string
Activations
StateActivationFunction
— Activation function to update hidden state
"tanh"
(default) 
"softsign"
Activation function to update the hidden state, specified as one of these values:
"tanh"
— Use the hyperbolic tangent function (tanh)."softsign"
— Use the softsign function, $$\text{softsign}(x)=\frac{x}{1+\leftx\right}$$.
The software uses this option as the function $${\sigma}_{s}$$ in the calculations to update the hidden state.
The GRULayer
object stores this property as a character vector.
To set this property, use the corresponding namevalue argument when you create the GRULayer
object. After you create a GRULayer
object, this property is readonly.
GateActivationFunction
— Activation function to apply to gates
"sigmoid"
(default)  "hardsigmoid"
Activation function to apply to the gates, specified as one of these values:
"sigmoid"
— Use the sigmoid function, $$\sigma (x)={(1+{e}^{x})}^{1}$$."hardsigmoid"
— Use the hard sigmoid function,$$\sigma (x)=\{\begin{array}{cc}\begin{array}{l}0\hfill \\ 0.2x+0.5\hfill \\ 1\hfill \end{array}& \begin{array}{l}\text{if}x2.5\hfill \\ \text{if}2.5\le x\le 2.5\hfill \\ \text{if}x2.5\hfill \end{array}\end{array}.$$
The software uses this option as the function $${\sigma}_{g}$$ in the calculations for the layer gates.
The GRULayer
object stores this property as a character vector.
To set this property, use the corresponding namevalue argument when you create the GRULayer
object. After you create a GRULayer
object, this property is readonly.
State
HiddenState
— Hidden state
[]
(default)  numeric vector
Hidden state to use in the layer operation, specified as a
NumHiddenUnits
by1 numeric vector. This value corresponds to the
initial hidden state when data is passed to the layer.
After you set this property manually, calls to the resetState
function set the hidden state to this value.
If HasStateInputs
is 1
(true
), then the HiddenState
property must be empty.
Data Types: single
 double
Parameters and Initialization
InputWeightsInitializer
— Function to initialize input weights
"glorot"
(default) 
"he"

"orthogonal"

"narrownormal"

"zeros"

"ones"
 function handle
Function to initialize the input weights, specified as one of the following:
"glorot"
— Initialize the input weights with the Glorot initializer [2] (also known as Xavier initializer). The Glorot initializer independently samples from a uniform distribution with a mean of zero and a variance of2/(InputSize + numOut)
, wherenumOut = 3*NumHiddenUnits
."he"
— Initialize the input weights with the He initializer [3]. The He initializer samples from a normal distribution with a mean of zero and a variance of2/InputSize
."orthogonal"
— Initialize the input weights with Q, the orthogonal matrix given by the QR decomposition of Z = QR for a random matrix Z sampled from a unit normal distribution. [4]"narrownormal"
— Initialize the input weights by independently sampling from a normal distribution with a mean of zero and a standard deviation of 0.01."zeros"
— Initialize the input weights with zeros."ones"
— Initialize the input weights with ones.Function handle — Initialize the input weights with a custom function. If you specify a function handle, then the function must be of the form
weights = func(sz)
, wheresz
is the size of the input weights.
The layer only initializes the input weights when the
InputWeights
property is empty.
The GRULayer
object stores this property as a character vector or a
function handle.
Data Types: char
 string
 function_handle
RecurrentWeightsInitializer
— Function to initialize recurrent weights
"orthogonal"
(default) 
"glorot"

"he"

"narrownormal"

"zeros"

"ones"
 function handle
Function to initialize the recurrent weights, specified as one of the following:
"orthogonal"
— Initialize the recurrent weights with Q, the orthogonal matrix given by the QR decomposition of Z = QR for a random matrix Z sampled from a unit normal distribution. [4]"glorot"
— Initialize the recurrent weights with the Glorot initializer [2] (also known as Xavier initializer). The Glorot initializer independently samples from a uniform distribution with a mean of zero and a variance of2/(numIn + numOut)
, wherenumIn = NumHiddenUnits
andnumOut = 3*NumHiddenUnits
."he"
— Initialize the recurrent weights with the He initializer [3]. The He initializer samples from a normal distribution with a mean of zero and a variance of2/NumHiddenUnits
."narrownormal"
— Initialize the recurrent weights by independently sampling from a normal distribution with a mean of zero and a standard deviation of 0.01."zeros"
— Initialize the recurrent weights with zeros."ones"
— Initialize the recurrent weights with ones.Function handle — Initialize the recurrent weights with a custom function. If you specify a function handle, then the function must be of the form
weights = func(sz)
, wheresz
is the size of the recurrent weights.
The layer only initializes the recurrent weights when the
RecurrentWeights
property is empty.
The GRULayer
object stores this property as a character vector or a
function handle.
Data Types: char
 string
 function_handle
BiasInitializer
— Function to initialize bias
"zeros"
(default) 
"narrownormal"

"ones"
 function handle
Function to initialize the bias, specified as one of these values:
"zeros"
— Initialize the bias with zeros."narrownormal"
— Initialize the bias by independently sampling from a normal distribution with a mean of zero and standard deviation 0.01."ones"
— Initialize the bias with ones.Function handle — Initialize the bias with a custom function. If you specify a function handle, then the function must have the form
bias = func(sz)
, wheresz
is the size of the bias.
The layer initializes the bias only when the Bias
property is
empty.
The GRULayer
object stores this property as a character vector or a
function handle.
Data Types: char
 string
 function_handle
InputWeights
— Input weights
[]
(default)  matrix
Input weights, specified as a matrix.
The input weight matrix is a concatenation of the three input weight matrices for the components in the GRU layer. The three matrices are concatenated vertically in the following order:
Reset gate
Update gate
Candidate state
The input weights are learnable parameters. When you train a
neural network using the trainnet
function,
if InputWeights
is nonempty, then the software uses the
InputWeights
property as the initial value. If InputWeights
is empty, then the software uses the initializer
specified by InputWeightsInitializer
.
At training time, InputWeights
is a
3*NumHiddenUnits
byInputSize
matrix.
RecurrentWeights
— Recurrent weights
[]
(default)  matrix
Recurrent weights, specified as a matrix.
The recurrent weight matrix is a concatenation of the three recurrent weight matrices for the components in the GRU layer. The three matrices are vertically concatenated in the following order:
Reset gate
Update gate
Candidate state
The recurrent weights are learnable parameters. When you train
an RNN using the trainnet
function,
if RecurrentWeights
is nonempty, then the software uses the
RecurrentWeights
property as the initial value. If
RecurrentWeights
is empty, then the software uses the
initializer specified by RecurrentWeightsInitializer
.
At training time RecurrentWeights
is a
3*NumHiddenUnits
byNumHiddenUnits
matrix.
Bias
— Layer biases
[]
(default)  numeric vector
Layer biases, specified as a numeric vector.
If ResetGateMode
is
"aftermultiplication"
or
"beforemultiplication"
, then the bias vector is a concatenation
of three bias vectors for the components in the layer operation. The layer concatenates
the vectors vertically in this order:
Reset gate
Update gate
Candidate state
In this case, at training time, Bias
is a 3*NumHiddenUnits
by1 numeric vector.
If ResetGateMode
is
"recurrentbiasaftermultiplication"
, then the bias vector is a
concatenation of six bias vectors for the components in the GRU layer. The layer
concatenates the vectors vertically in this order:
Reset gate
Update gate
Candidate state
Reset gate (recurrent bias)
Update gate (recurrent bias)
Candidate state (recurrent bias)
In this case, at training time, Bias
is a 6*NumHiddenUnits
by1 numeric vector.
The layer biases are learnable parameters. When you train a neural network, if Bias
is nonempty, then the trainnet
and trainNetwork
functions use the Bias
property as the initial value. If Bias
is empty, then software uses the initializer specified by BiasInitializer
.
For more information about the reset gate calculations, see Gated Recurrent Unit Layer.
Learning Rate and Regularization
InputWeightsLearnRateFactor
— Learning rate factor for input weights
1
(default)  numeric scalar  1by3 numeric vector
Learning rate factor for the input weights, specified as a numeric scalar or a 1by3 numeric vector.
The software multiplies this factor by the global learning rate
to determine the learning rate factor for the input weights of the layer. For example, if
InputWeightsLearnRateFactor
is 2
, then the learning
rate factor for the input weights of the layer is twice the current global learning rate. The
software determines the global learning rate based on the settings you specify with the
trainingOptions
function.
To control the value of the learning rate factor for the three individual matrices in
InputWeights
, specify a 1by3 vector. The entries of
InputWeightsLearnRateFactor
correspond to the learning rate
factor of these values:
Reset gate
Update gate
Candidate state
To specify the same value for all the matrices, specify a nonnegative scalar.
Example: 2
Example: [1 2 1]
RecurrentWeightsLearnRateFactor
— Learning rate factor for recurrent weights
1
(default)  numeric scalar  1by3 numeric vector
Learning rate factor for the recurrent weights, specified as a numeric scalar or a 1by3 numeric vector.
The software multiplies this factor by the global learning rate
to determine the learning rate for the recurrent weights of the layer. For example, if
RecurrentWeightsLearnRateFactor
is 2
, then the
learning rate for the recurrent weights of the layer is twice the current global learning rate.
The software determines the global learning rate based on the settings you specify using the
trainingOptions
function.
To control the value of the learning rate factor for the three individual matrices in
RecurrentWeights
, specify a 1by3 vector. The entries of
RecurrentWeightsLearnRateFactor
correspond to the learning rate
factor of these values:
Reset gate
Update gate
Candidate state
To specify the same value for all the matrices, specify a nonnegative scalar.
Example: 2
Example: [1 2 1]
BiasLearnRateFactor
— Learning rate factor for biases
1
(default)  nonnegative scalar  1by3 numeric vector
Learning rate factor for the biases, specified as a nonnegative scalar or a 1by3 numeric vector.
The software multiplies this factor by the global learning rate to determine the learning rate for the biases in this layer. For example, if BiasLearnRateFactor
is 2
, then the learning rate for the biases in the layer is twice the current global learning rate. The software determines the global learning rate based on the settings you specify using the trainingOptions
function.
To control the value of the learning rate factor for the three individual vectors in
Bias
, specify a 1by3 vector. The entries of
BiasLearnRateFactor
correspond to the learning rate factor of
these values:
Reset gate
Update gate
Candidate state
If ResetGateMode
is
"recurrentbiasaftermultiplication"
, then the software uses the
same vector for the recurrent bias vectors.
To specify the same value for all the vectors, specify a nonnegative scalar.
Example: 2
Example: [1 2 1]
InputWeightsL2Factor
— L_{2} regularization factor for input weights
1
(default)  numeric scalar  1by3 numeric vector
L_{2} regularization factor for the input weights, specified as a numeric scalar or a 1by3 numeric vector.
The software multiplies this factor by the global
L_{2} regularization factor to determine the
L_{2} regularization factor for the input weights
of the layer. For example, if InputWeightsL2Factor
is 2
,
then the L_{2} regularization factor for the input
weights of the layer is twice the current global L_{2}
regularization factor. The software determines the L_{2}
regularization factor based on the settings you specify using the trainingOptions
function.
To control the value of the L_{2}
regularization factor for the three individual matrices in
InputWeights
, specify a 1by3 vector. The entries of
InputWeightsL2Factor
correspond to the
L_{2} regularization factor of these
values:
Reset gate
Update gate
Candidate state
To specify the same value for all the matrices, specify a nonnegative scalar.
Example:
2
Example:
[1 2 1]
RecurrentWeightsL2Factor
— L_{2} regularization factor for recurrent weights
1
(default)  numeric scalar  1by3 numeric vector
L_{2} regularization factor for the recurrent weights, specified as a numeric scalar or a 1by3 numeric vector.
The software multiplies this factor by the global
L_{2} regularization factor to determine the
L_{2} regularization factor for the recurrent
weights of the layer. For example, if RecurrentWeightsL2Factor
is
2
, then the L_{2} regularization
factor for the recurrent weights of the layer is twice the current global
L_{2} regularization factor. The software
determines the L_{2} regularization factor based on the
settings you specify using the trainingOptions
function.
To control the value of the L_{2}
regularization factor for the three individual matrices in
RecurrentWeights
, specify a 1by3 vector. The entries of
RecurrentWeightsL2Factor
correspond to the
L_{2} regularization factor of these
values:
Reset gate
Update gate
Candidate state
To specify the same value for all the matrices, specify a nonnegative scalar.
Example:
2
Example:
[1 2 1]
BiasL2Factor
— L_{2} regularization factor for biases
0
(default)  nonnegative scalar  1by3 numeric vector
L_{2} regularization factor for the biases, specified as a nonnegative scalar or a 1by3 numeric vector.
The software multiplies this factor by the global L_{2} regularization factor to determine the L_{2} regularization for the biases in this layer. For example, if BiasL2Factor
is 2
, then the L_{2} regularization for the biases in this layer is twice the global L_{2} regularization factor. The software determines the global L_{2} regularization factor based on the settings you specify using the trainingOptions
function.
To control the value of the L_{2}
regularization factor for the individual vectors in Bias
, specify a
1by3 vector. The entries of BiasL2Factor
correspond to the
L_{2} regularization factor of these
values:
Reset gate
Update gate
Candidate state
If ResetGateMode
is
"recurrentbiasaftermultiplication"
, then the software uses the
same vector for the recurrent bias vectors.
To specify the same value for all the vectors, specify a nonnegative scalar.
Example:
2
Example:
[1 2 1]
Layer
Name
— Layer name
""
(default)  character vector  string scalar
NumInputs
— Number of inputs
1

2
This property is readonly.
Number of inputs to the layer.
If the HasStateInputs
property is 0
(false
), then the layer has one input with the name
"in"
, which corresponds to the input data. In this case, the layer
uses the HiddenState
property for the layer
operation.
If the HasStateInputs
property is 1
(true
), then the layer has two inputs with the names
"in"
and "hidden"
, which correspond to the input
data and hidden state, respectively. In this case, the layer uses the values that the
network passes to these inputs for the layer operation. If HasStateInputs
is 1
(true
), then the
HiddenState
property must be empty.
Data Types: double
InputNames
— Layer input names
"in"

["in" "hidden"]
This property is readonly.
Layer input names.
If the HasStateInputs
property is 0
(false
), then the layer has one input with the name
"in"
, which corresponds to the input data. In this case, the layer
uses the HiddenState
property for the layer
operation.
If the HasStateInputs
property is 1
(true
), then the layer has two inputs with the names
"in"
and "hidden"
, which correspond to the input
data and hidden state, respectively. In this case, the layer uses the values that the
network passes to these inputs for the layer operation. If HasStateInputs
is 1
(true
), then the
HiddenState
property must be empty.
The GRULayer
object stores this property as a cell array of character
vectors.
NumOutputs
— Number of outputs
1

2
This property is readonly.
Number of outputs from the layer.
If the HasStateOutputs
property is 0
(false
), then the layer has one output with the name
"out"
, which corresponds to the output data.
If the HasStateOutputs
property is 1
(true
), then the layer has two outputs with the names
"out"
and "hidden"
, which correspond
to the output data and hidden state, respectively. In this case, the layer also
outputs the state values computed during the layer operation.
Data Types: double
OutputNames
— Layer output names
"out"

["out" "hidden"]
This property is readonly.
Layer output names.
If the HasStateOutputs
property is 0
(false
), then the layer has one output with the name
"out"
, which corresponds to the output data.
If the HasStateOutputs
property is 1
(true
), then the layer has two outputs with the names
"out"
and "hidden"
, which correspond
to the output data and hidden state, respectively. In this case, the layer also
outputs the state values computed during the layer operation.
The GRULayer
object stores this property as a cell array of character
vectors.
Examples
Create GRU Layer
Create a GRU layer with the name gru1
and 100 hidden units.
layer = gruLayer(100,Name="gru1")
layer = GRULayer with properties: Name: 'gru1' InputNames: {'in'} OutputNames: {'out'} NumInputs: 1 NumOutputs: 1 HasStateInputs: 0 HasStateOutputs: 0 Hyperparameters InputSize: 'auto' NumHiddenUnits: 100 OutputMode: 'sequence' StateActivationFunction: 'tanh' GateActivationFunction: 'sigmoid' ResetGateMode: 'aftermultiplication' Learnable Parameters InputWeights: [] RecurrentWeights: [] Bias: [] State Parameters HiddenState: [] Use properties method to see a list of all properties.
Include a GRU layer in a Layer
array.
inputSize = 12;
numHiddenUnits = 100;
numClasses = 9;
layers = [ ...
sequenceInputLayer(inputSize)
gruLayer(numHiddenUnits)
fullyConnectedLayer(numClasses)
softmaxLayer]
layers = 4x1 Layer array with layers: 1 '' Sequence Input Sequence input with 12 dimensions 2 '' GRU GRU with 100 hidden units 3 '' Fully Connected 9 fully connected layer 4 '' Softmax softmax
Algorithms
Gated Recurrent Unit Layer
A GRU layer is an RNN layer that learns dependencies between time steps in timeseries and sequence data.
The hidden state of the layer at time step t contains the output of the GRU layer for this time step. At each time step, the layer adds information to or removes information from the state. The layer controls these updates using gates.
These components control the hidden state of the layer.
Component  Purpose 

Reset gate (r)  Control level of state reset 
Update gate (z)  Control level of state update 
Candidate state ($$\tilde{h}$$)  Control level of update added to hidden state 
The learnable weights of a GRU layer are the input weights W
(InputWeights
), the recurrent weights R
(RecurrentWeights
), and the bias b
(Bias
). If the ResetGateMode
property is "recurrentbiasaftermultiplication"
, then the gate and
state calculations require two sets of bias values. The matrices W and
R are concatenations of the input weights and the recurrent weights
of each component, respectively. The layer concatenates the matrices in this order:
$$W=\left[\begin{array}{c}{W}_{r}\\ {W}_{z}\\ {W}_{\tilde{h}}\end{array}\right],R=\left[\begin{array}{c}{R}_{r}\\ {R}_{z}\\ {R}_{\tilde{h}}\end{array}\right],$$
where r, z, and $$\tilde{h}$$ denote the reset gate, update gate, and candidate state, respectively.
The bias vector depends on the ResetGateMode
property. If
ResetGateMode
is
"aftermultiplication"
or "beforemultiplication"
,
then the bias vector is a concatenation of three vectors:
$$b=\left[\begin{array}{c}{b}_{{W}_{r}}\\ {b}_{{W}_{z}}\\ {b}_{{W}_{\tilde{h}}}\end{array}\right],$$
where the subscript W indicates that this bias corresponds to the input weights multiplication.
If ResetGateMode
is
"recurrentbiasaftermultiplication"
, then the bias vector is a
concatenation of six vectors:
$$b=\left[\begin{array}{c}{b}_{{W}_{r}}\\ {b}_{{W}_{z}}\\ {b}_{{W}_{\tilde{h}}}\\ {b}_{{R}_{r}}\\ {b}_{{R}_{z}}\\ {b}_{{R}_{\tilde{h}}}\end{array}\right],$$
where the subscript R indicates that this is the bias corresponding to the recurrent weights multiplication.
The hidden state at time step t is given by this equation:
$${h}_{t}=(1{z}_{t})\odot {\tilde{h}}_{t}+{z}_{t}\odot {h}_{t1}.$$
These formulas describe the components at time step t.
Component  ResetGateMode  Formula  

Reset gate  "aftermultiplication"  $${r}_{t}={\sigma}_{g}\left({W}_{r}{x}_{t}+{b}_{{W}_{r}}+\text{}{R}_{r}{h}_{t1}\right)$$  
"beforemultiplication"  
"recurrentbiasaftermultiplication"  $${r}_{t}={\sigma}_{g}\left({W}_{r}{x}_{t}+{b}_{{W}_{r}}+\text{}{R}_{r}{h}_{t1}+{b}_{{R}_{r}}\right)$$  
Update gate  "aftermultiplication"  $${z}_{t}={\sigma}_{g}\left({W}_{z}{x}_{t}+{b}_{{W}_{z}}+\text{}{R}_{z}{h}_{t1}\right)$$  
"beforemultiplication"  
"recurrentbiasaftermultiplication"  $${z}_{t}={\sigma}_{g}\left({W}_{z}{x}_{t}+{b}_{{W}_{z}}+\text{}{R}_{z}{h}_{t1}+{b}_{{R}_{z}}\right)$$  
Candidate state  "aftermultiplication"  $${\tilde{h}}_{t}={\sigma}_{s}\left({W}_{\tilde{h}}{x}_{t}+{b}_{{W}_{\tilde{h}}}+{r}_{t}\odot \text{}({\text{R}}_{\tilde{h}}{h}_{t1})\right)$$  
"beforemultiplication"  $${\tilde{h}}_{t}={\sigma}_{s}\left({W}_{\tilde{h}}{x}_{t}+{b}_{{W}_{\tilde{h}}}+{R}_{\tilde{h}}({r}_{t}\odot \text{}{h}_{t1})\right)$$  
"recurrentbiasaftermultiplication"  $${\tilde{h}}_{t}={\sigma}_{s}\left({W}_{\tilde{h}}{x}_{t}+{b}_{{W}_{\tilde{h}}}+{r}_{t}\odot \text{}\left({\text{R}}_{\tilde{h}}{h}_{t1}+{b}_{{R}_{\tilde{h}}}\right)\right)$$ 
In these calculations, $${\sigma}_{g}$$ and $${\sigma}_{s}$$ denote the gate and state activation functions, respectively. The
gruLayer
function, by default, uses the sigmoid function given by $$\sigma (x)={(1+{e}^{x})}^{1}$$ to compute the gate activation function and the hyperbolic tangent
function (tanh) to compute the state activation function. To specify the state and gate
activation functions, use the StateActivationFunction
and GateActivationFunction
properties, respectively.
Layer Input and Output Formats
Layers in a layer array or layer graph pass data to subsequent layers as formatted dlarray
objects.
The format of a dlarray
object is a string of characters, in which each
character describes the corresponding dimension of the data. The formats consist of one or
more of these characters:
"S"
— Spatial"C"
— Channel"B"
— Batch"T"
— Time"U"
— Unspecified
For example, 2D image data that is represented as a 4D array, where the first two dimensions
correspond to the spatial dimensions of the images, the third dimension corresponds to the
channels of the images, and the fourth dimension corresponds to the batch dimension, can be
described as having the format "SSCB"
(spatial, spatial, channel,
batch).
You can interact with these dlarray
objects in automatic differentiation
workflows, such as those for developing a custom layer, using a functionLayer
object, or using the forward
and predict
functions with
dlnetwork
objects.
This table shows the supported input formats of GRULayer
objects and the
corresponding output format. If the software passes the output of the layer to a custom
layer that does not inherit from the nnet.layer.Formattable
class, or a
FunctionLayer
object with the Formattable
property
set to 0
(false
), then the layer receives an
unformatted dlarray
object with dimensions ordered according to the formats
in this table. The formats listed here are only a subset. The layer may support additional
formats such as formats with additional "S"
(spatial) or
"U"
(unspecified) dimensions.
Input Format  OutputMode  Output Format 

 "sequence" 

"last"  
 "sequence" 

"last" 
 
 "sequence" 

"last" 
In dlnetwork
objects, GRULayer
objects also support these input and output format combinations.
Input Format  OutputMode  Output Format 

 "sequence" 

"last"  
 "sequence"  
"last"  
 "sequence"  
"last"  
 "sequence" 

"last" 
 
 "sequence" 

"last" 
 
 "sequence" 

"last" 
 
 "sequence" 

"last"  
 "sequence"  
"last"  
 "sequence"  
"last"  
 "sequence" 

"last" 
 
 "sequence" 

"last" 
 
 "sequence" 

"last" 
 
 "sequence" 

"last" 
 
 "sequence" 

"last"  
 "sequence"  
"last"  
 "sequence" 

"last" 
 
 "sequence" 

"last" 
 
 "sequence" 

"last" 
 
 "sequence" 

"last" 

If the HasStateInputs
property is 1
(true
), then the layer has two additional inputs with the names
"hidden"
and "cell"
, which correspond to the
hidden state and cell state, respectively. These additional inputs expect input format
"CB"
(channel, batch).
If the HasStateOutputs
property is 1
(true
), then the layer has two additional outputs with names
"hidden"
and "cell"
, which correspond to the
hidden state and cell state, respectively. These additional outputs have output format
"CB"
(channel, batch).
References
[1] Cho, Kyunghyun, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. "Learning phrase representations using RNN encoderdecoder for statistical machine translation." arXiv preprint arXiv:1406.1078 (2014).
[2] Glorot, Xavier, and Yoshua Bengio. "Understanding the Difficulty of Training Deep Feedforward Neural Networks." In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 249–356. Sardinia, Italy: AISTATS, 2010. https://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf
[3] He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. "Delving Deep into Rectifiers: Surpassing HumanLevel Performance on ImageNet Classification." In 2015 IEEE International Conference on Computer Vision (ICCV), 1026–34. Santiago, Chile: IEEE, 2015. https://doi.org/10.1109/ICCV.2015.123
[4] Saxe, Andrew M., James L. McClelland, and Surya Ganguli. "Exact Solutions to the Nonlinear Dynamics of Learning in Deep Linear Neural Networks.” Preprint, submitted February 19, 2014. https://arxiv.org/abs/1312.6120.
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
Usage notes and limitations:
For code generation in general, the HasStateInputs
and
HasStateOutputs
properties must be set to
0
(false).
When generating code with Intel^{®} MKLDNN or ARM^{®} Compute Library:
The
StateActivationFunction
property must be set to"tanh"
.The
GateActivationFunction
property must be set to"sigmoid"
.The
ResetGateMode
property must be set to"aftermultiplication"
or"recurrentbiasaftermultiplication"
.
When generating generic C/C++ code:
The
ResetGateMode
property can be set to"aftermultiplication"
,"beforemultiplication"
or"recurrentbiasaftermultiplication"
.
GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.
Usage notes and limitations:
The
StateActivationFunction
property must be set to"tanh"
.The
GateActivationFunction
property must be set to"sigmoid"
.The
ResetGateMode
property must be set to"aftermultiplication"
or"recurrentbiasaftermultiplication"
.The
HasStateInputs
andHasStateOutputs
properties must be set to0
(false).
Version History
Introduced in R2020aR2023a: Specify reset gate mode for GRU layers in dlnetwork
objects
For GRU layers in dlnetwork
objects, specify the
reset gate mode using the ResetGateMode
property.
See Also
trainnet
 trainingOptions
 dlnetwork
 sequenceInputLayer
 lstmLayer
 bilstmLayer
 convolution1dLayer
 maxPooling1dLayer
 averagePooling1dLayer
 globalMaxPooling1dLayer
 globalAveragePooling1dLayer
Topics
 Sequence Classification Using Deep Learning
 Sequence Classification Using 1D Convolutions
 Time Series Forecasting Using Deep Learning
 SequencetoSequence Classification Using Deep Learning
 SequencetoSequence Regression Using Deep Learning
 Classify Videos Using Deep Learning
 Long ShortTerm Memory Neural Networks
 List of Deep Learning Layers
 Deep Learning Tips and Tricks
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