bilstmLayer

Bidirectional long short-term memory (BiLSTM) layer

Description

A bidirectional LSTM (BiLSTM) layer learns bidirectional long-term dependencies between time steps of time series or sequence data. These dependencies can be useful when you want the network to learn from the complete time series at each time step.

Creation

Syntax

layer = bilstmLayer(numHiddenUnits)

layer = bilstmLayer(numHiddenUnits,Name,Value)

Description

layer = bilstmLayer(numHiddenUnits) creates a bidirectional LSTM layer and sets the NumHiddenUnits property.

layer = bilstmLayer(numHiddenUnits,Name,Value) sets additional OutputMode, Activations, State, Parameters and Initialization, Learning Rate and Regularization, and Name properties using one or more name-value pair arguments. You can specify multiple name-value pair arguments. Enclose each property name in quotes.

Properties

BiLSTM

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 remembered between time steps (the hidden state). The hidden state can contain information from all previous time steps, regardless of the sequence length. If the number of hidden units is too large, then the layer might overfit to the training data. This value can vary from a few dozen to a few thousand.

The hidden state does not limit the number of time steps that are processed in an iteration. To split your sequences into smaller sequences for training, use the 'SequenceLength' option in trainingOptions.

Example: 200

OutputMode — Output mode

'sequence' (default) | 'last'

Output mode, specified as one of the following:

• 'sequence' – Output the complete sequence.

• 'last' – Output the last time step of the sequence.

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 name 'in', which corresponds to the input data. In this case, the layer uses the HiddenState and CellState properties for the layer operation.

If the HasStateInputs property is 1 (true), then the layer has three inputs with names 'in', 'hidden', and 'cell', which correspond to the input data, hidden state, and cell state respectively. In this case, the layer uses the values passed to these inputs for the layer operation. If HasStateInputs is 1 (true), then the HiddenState and CellState properties must be empty.

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 name 'out', which corresponds to the output data.

If the HasStateOutputs property is 1 (true), then the layer has three outputs with names 'out', 'hidden', and 'cell', which correspond to the output data, hidden state, and cell state, respectively. In this case, the layer also outputs the state values computed during the layer operation.

InputSize — Input size

'auto' (default) | positive integer

Input size, specified as a positive integer or 'auto'. If InputSize is 'auto', then the software automatically assigns the input size at training time.

Example: 100

Activations

StateActivationFunction — Activation function to update the cell and hidden state

'tanh' (default) | 'softsign'

Activation function to update the cell and hidden state, specified as one of the following:

• 'tanh' – Use the hyperbolic tangent function (tanh).

• 'softsign' – Use the softsign function $$\text{softsign}(x)=\frac{x}{1+\left|x\right|}$$.

The layer uses this option as the function $${\sigma }_c$$ in the calculations to update the cell and hidden state. For more information on how activation functions are used in an LSTM layer, see Long Short-Term Memory Layer.

GateActivationFunction — Activation function to apply to the gates

'sigmoid' (default) | 'hard-sigmoid'

Activation function to apply to the gates, specified as one of the following:

• 'sigmoid' – Use the sigmoid function $$\sigma (x)={(1+e^{-x})}^{-1}$$.

• 'hard-sigmoid' – Use the hard sigmoid function

  $$\sigma (x)=\{\begin{array}{cc}\begin{array}{l}0\hfill \\ 0.2x+0.5\hfill \\ 1\hfill \end{array}& \begin{array}{l}\text{if }x<-2.5\hfill \\ \text{if}-2.5\le x\le 2.5\hfill \\ \text{if }x>2.5\hfill \end{array}\end{array}.$$

The layer uses this option as the function $${\sigma }_g$$ in the calculations for the layer gates.

State

CellState — Cell state

numeric vector

Cell state to use in the layer operation, specified as a 2*NumHiddenUnits-by-1 numeric vector. This value corresponds to the initial cell state when data is passed to the layer.

After setting this property manually, calls to the resetState function set the cell state to this value.

If HasStateInputs is true, then the CellState property must be empty.

HiddenState — Hidden state

numeric vector

Hidden state to use in the layer operation, specified as a 2*NumHiddenUnits-by-1 numeric vector. This value corresponds to the initial hidden state when data is passed to the layer.

After setting this property manually, calls to the resetState function set the hidden state to this value.

If HasStateInputs is true, then the HiddenState property must be empty.

Parameters and Initialization

InputWeightsInitializer — Function to initialize input weights

'glorot' (default) | 'he' | 'orthogonal' | 'narrow-normal' | '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 [1] (also known as Xavier initializer). The Glorot initializer independently samples from a uniform distribution with zero mean and variance 2/(InputSize + numOut), where numOut = 8*NumHiddenUnits.

• 'he' – Initialize the input weights with the He initializer [2]. The He initializer samples from a normal distribution with zero mean and variance 2/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. [3]

• 'narrow-normal' – Initialize the input weights by independently sampling from a normal distribution with zero mean and standard deviation 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), where sz is the size of the input weights.

The layer only initializes the input weights when the InputWeights property is empty.

Data Types: char | string | function_handle

RecurrentWeightsInitializer — Function to initialize recurrent weights

'orthogonal' (default) | 'glorot' | 'he' | 'narrow-normal' | 'zeros' | 'ones' | function handle

Function to initialize the recurrent weights, specified as one of the following:

• '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. [3]

• 'glorot' – Initialize the recurrent weights with the Glorot initializer [1] (also known as Xavier initializer). The Glorot initializer independently samples from a uniform distribution with zero mean and variance 2/(numIn + numOut), where numIn = NumHiddenUnits and numOut = 8*NumHiddenUnits.

• 'he' – Initialize the recurrent weights with the He initializer [2]. The He initializer samples from a normal distribution with zero mean and variance 2/NumHiddenUnits.

• 'narrow-normal' – Initialize the recurrent weights by independently sampling from a normal distribution with zero mean and standard deviation 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), where sz is the size of the recurrent weights.

The layer only initializes the recurrent weights when the RecurrentWeights property is empty.

Data Types: char | string | function_handle

BiasInitializer — Function to initialize bias

'unit-forget-gate' (default) | 'narrow-normal' | 'ones' | function handle

Function to initialize the bias, specified as one of the following:

• 'unit-forget-gate' – Initialize the forget gate bias with ones and the remaining biases with zeros.

• 'narrow-normal' – Initialize the bias by independently sampling from a normal distribution with zero mean 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 be of the form bias = func(sz), where sz is the size of the bias.

The layer only initializes the bias when the Bias property is empty.

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 eight input weight matrices for the components (gates) in the bidirectional LSTM layer. The eight matrices are concatenated vertically in the following order:

1. Input gate (Forward)

2. Forget gate (Forward)

3. Cell candidate (Forward)

4. Output gate (Forward)

5. Input gate (Backward)

6. Forget gate (Backward)

7. Cell candidate (Backward)

8. Output gate (Backward)

The input weights are learnable parameters. When training a network, if InputWeights is nonempty, then trainNetwork uses the InputWeights property as the initial value. If InputWeights is empty, then trainNetwork uses the initializer specified by InputWeightsInitializer.

At training time, InputWeights is an 8*NumHiddenUnits-by-InputSize matrix.

RecurrentWeights — Recurrent weights

[] (default) | matrix

Recurrent weights, specified as a matrix.

The recurrent weight matrix is a concatenation of the eight recurrent weight matrices for the components (gates) in the bidirectional LSTM layer. The eight matrices are concatenated vertically in the following order:

1. Input gate (Forward)

2. Forget gate (Forward)

3. Cell candidate (Forward)

4. Output gate (Forward)

5. Input gate (Backward)

6. Forget gate (Backward)

7. Cell candidate (Backward)

8. Output gate (Backward)

The recurrent weights are learnable parameters. When training a network, if RecurrentWeights is nonempty, then trainNetwork uses the RecurrentWeights property as the initial value. If RecurrentWeights is empty, then trainNetwork uses the initializer specified by RecurrentWeightsInitializer.

At training time, RecurrentWeights is an 8*NumHiddenUnits-by-NumHiddenUnits matrix.

Bias — Layer biases

[] (default) | numeric vector

Layer biases, specified as a numeric vector.

The bias vector is a concatenation of the eight bias vectors for the components (gates) in the bidirectional LSTM layer. The eight vectors are concatenated vertically in the following order:

1. Input gate (Forward)

2. Forget gate (Forward)

3. Cell candidate (Forward)

4. Output gate (Forward)

5. Input gate (Backward)

6. Forget gate (Backward)

7. Cell candidate (Backward)

8. Output gate (Backward)

The layer biases are learnable parameters. When you train a network, if Bias is nonempty, then trainNetwork uses the Bias property as the initial value. If Bias is empty, then trainNetwork uses the initializer specified by BiasInitializer.

At training time, Bias is an 8*NumHiddenUnits-by-1 numeric vector.

Learning Rate and Regularization

InputWeightsLearnRateFactor — Learning rate factor for input weights

1 (default) | numeric scalar | 1-by-8 numeric vector

Learning rate factor for the input weights, specified as a numeric scalar or a 1-by-8 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 specified with the trainingOptions function.

To control the value of the learning rate factor for the four individual matrices in InputWeights, assign a 1-by-8 vector, where the entries correspond to the learning rate factor of the following:

1. Input gate (Forward)

2. Forget gate (Forward)

3. Cell candidate (Forward)

4. Output gate (Forward)

5. Input gate (Backward)

6. Forget gate (Backward)

7. Cell candidate (Backward)

8. Output gate (Backward)

To specify the same value for all the matrices, specify a nonnegative scalar.

Example: 0.1

RecurrentWeightsLearnRateFactor — Learning rate factor for recurrent weights

1 (default) | numeric scalar | 1-by-8 numeric vector

Learning rate factor for the recurrent weights, specified as a numeric scalar or a 1-by-8 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 specified with the trainingOptions function.

To control the value of the learn rate for the four individual matrices in RecurrentWeights, assign a 1-by-8 vector, where the entries correspond to the learning rate factor of the following:

1. Input gate (Forward)

2. Forget gate (Forward)

3. Cell candidate (Forward)

4. Output gate (Forward)

5. Input gate (Backward)

6. Forget gate (Backward)

7. Cell candidate (Backward)

8. Output gate (Backward)

To specify the same value for all the matrices, specify a nonnegative scalar.

Example: 0.1

Example: [1 2 1 1 1 2 1 1]

BiasLearnRateFactor — Learning rate factor for biases

1 (default) | nonnegative scalar | 1-by-8 numeric vector

Learning rate factor for the biases, specified as a nonnegative scalar or a 1-by-8 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 four individual matrices in Bias, assign a 1-by-8 vector, where the entries correspond to the learning rate factor of the following:

1. Input gate (Forward)

2. Forget gate (Forward)

3. Cell candidate (Forward)

4. Output gate (Forward)

5. Input gate (Backward)

6. Forget gate (Backward)

7. Cell candidate (Backward)

8. Output gate (Backward)

To specify the same value for all the matrices, specify a nonnegative scalar.

Example: 2

Example: [1 2 1 1 1 2 1 1]

InputWeightsL2Factor — L2 regularization factor for input weights

1 (default) | numeric scalar | 1-by-8 numeric vector

L2 regularization factor for the input weights, specified as a numeric scalar or a 1-by-8 numeric vector.

The software multiplies this factor by the global L2 regularization factor to determine the L2 regularization factor for the input weights of the layer. For example, if InputWeightsL2Factor is 2, then the L2 regularization factor for the input weights of the layer is twice the current global L2 regularization factor. The software determines the L2 regularization factor based on the settings specified with the trainingOptions function.

To control the value of the L2 regularization factor for the four individual matrices in InputWeights, assign a 1-by-8 vector, where the entries correspond to the L2 regularization factor of the following:

1. Input gate (Forward)

2. Forget gate (Forward)

3. Cell candidate (Forward)

4. Output gate (Forward)

5. Input gate (Backward)

6. Forget gate (Backward)

7. Cell candidate (Backward)

8. Output gate (Backward)

To specify the same value for all the matrices, specify a nonnegative scalar.

Example: 0.1

Example: [1 2 1 1 1 2 1 1]

RecurrentWeightsL2Factor — L2 regularization factor for recurrent weights

1 (default) | numeric scalar | 1-by-8 numeric vector

L2 regularization factor for the recurrent weights, specified as a numeric scalar or a 1-by-8 numeric vector.

The software multiplies this factor by the global L2 regularization factor to determine the L2 regularization factor for the recurrent weights of the layer. For example, if RecurrentWeightsL2Factor is 2, then the L2 regularization factor for the recurrent weights of the layer is twice the current global L2 regularization factor. The software determines the L2 regularization factor based on the settings specified with the trainingOptions function.

To control the value of the L2 regularization factor for the four individual matrices in RecurrentWeights, assign a 1-by-8 vector, where the entries correspond to the L2 regularization factor of the following:

1. Input gate (Forward)

2. Forget gate (Forward)

3. Cell candidate (Forward)

4. Output gate (Forward)

5. Input gate (Backward)

6. Forget gate (Backward)

7. Cell candidate (Backward)

8. Output gate (Backward)

To specify the same value for all the matrices, specify a nonnegative scalar.

Example: 0.1

Example: [1 2 1 1 1 2 1 1]

BiasL2Factor — L2 regularization factor for biases

0 (default) | nonnegative scalar | 1-by-8 numeric vector

L2 regularization factor for the biases, specified as a nonnegative scalar.

The software multiplies this factor by the global L₂ regularization factor to determine the L₂ regularization for the biases in this layer. For example, if BiasL2Factor is 2, then the L₂ regularization for the biases in this layer is twice the global L₂ regularization factor. You can specify the global L₂ regularization factor using the trainingOptions function.

To control the value of the L2 regularization factor for the four individual matrices in Bias, assign a 1-by-8 vector, where the entries correspond to the L2 regularization factor of the following:

1. Input gate (Forward)

2. Forget gate (Forward)

3. Cell candidate (Forward)

4. Output gate (Forward)

5. Input gate (Backward)

6. Forget gate (Backward)

7. Cell candidate (Backward)

8. Output gate (Backward)

To specify the same value for all the matrices, specify a nonnegative scalar.

Example: 2

Example: [1 2 1 1 1 2 1 1]

Layer

Name — Layer name

'' (default) | character vector | string scalar

Layer name, specified as a character vector or a string scalar. For Layer array input, the trainNetwork, assembleNetwork, layerGraph, and dlnetwork functions automatically assign names to layers with Name set to ''.

Data Types: char | string

NumInputs — Number of inputs

1 | 3

Number of inputs of the layer.

If the HasStateInputs property is 0 (false), then the layer has one input with name 'in', which corresponds to the input data. In this case, the layer uses the HiddenState and CellState properties for the layer operation.

If the HasStateInputs property is 1 (true), then the layer has three inputs with names 'in', 'hidden', and 'cell', which correspond to the input data, hidden state, and cell state respectively. In this case, the layer uses the values passed to these inputs for the layer operation. If HasStateInputs is 1 (true), then the HiddenState and CellState properties must be empty.

Data Types: double

InputNames — Input names

{'in'} | {'in','hidden','cell'}

Input names of the layer.

If the HasStateInputs property is 0 (false), then the layer has one input with name 'in', which corresponds to the input data. In this case, the layer uses the HiddenState and CellState properties for the layer operation.

If the HasStateInputs property is 1 (true), then the layer has three inputs with names 'in', 'hidden', and 'cell', which correspond to the input data, hidden state, and cell state respectively. In this case, the layer uses the values passed to these inputs for the layer operation. If HasStateInputs is 1 (true), then the HiddenState and CellState properties must be empty.

NumOutputs — Number of outputs

1 | 3

Number of outputs of the layer.

If the HasStateOutputs property is 0 (false), then the layer has one output with name 'out', which corresponds to the output data.

If the HasStateOutputs property is 1 (true), then the layer has three outputs with names 'out', 'hidden', and 'cell', which correspond to the output data, hidden state, and cell state, respectively. In this case, the layer also outputs the state values computed during the layer operation.

Data Types: double

OutputNames — Output names

{'out'} | {'out','hidden','cell'}

Output names of the layer.

If the HasStateOutputs property is 0 (false), then the layer has one output with name 'out', which corresponds to the output data.

If the HasStateOutputs property is 1 (true), then the layer has three outputs with names 'out', 'hidden', and 'cell', which correspond to the output data, hidden state, and cell state, respectively. In this case, the layer also outputs the state values computed during the layer operation.

Examples

Create Bidirectional LSTM Layer

Create a bidirectional LSTM layer with the name 'bilstm1' and 100 hidden units.

layer = bilstmLayer(100,'Name','bilstm1')

layer = 
  BiLSTMLayer with properties:

                       Name: 'bilstm1'
                 InputNames: {'in'}
                OutputNames: {'out'}
                  NumInputs: 1
                 NumOutputs: 1
             HasStateInputs: 0
            HasStateOutputs: 0

   Hyperparameters
                  InputSize: 'auto'
             NumHiddenUnits: 100
                 OutputMode: 'sequence'
    StateActivationFunction: 'tanh'
     GateActivationFunction: 'sigmoid'

   Learnable Parameters
               InputWeights: []
           RecurrentWeights: []
                       Bias: []

   State Parameters
                HiddenState: []
                  CellState: []

  Show all properties

Include a bidirectional LSTM layer in a Layer array.

inputSize = 12;
numHiddenUnits = 100;
numClasses = 9;

layers = [ ...
    sequenceInputLayer(inputSize)
    bilstmLayer(numHiddenUnits)
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer]

layers = 
  5x1 Layer array with layers:

     1   ''   Sequence Input          Sequence input with 12 dimensions
     2   ''   BiLSTM                  BiLSTM with 100 hidden units
     3   ''   Fully Connected         9 fully connected layer
     4   ''   Softmax                 softmax
     5   ''   Classification Output   crossentropyex

Algorithms

Layer Input and Output Formats

Layers in a layer array or layer graph pass data specified as formatted dlarray objects.

You can interact with these dlarray objects in automatic differentiation workflows such as when 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 a BiLSTMLayer object and the corresponding output format. If the output of the layer is passed to a custom layer that does not inherit from the nnet.layer.Formattable class, or a FunctionLayer object with the Formattable option set to false, then the layer receives an unformatted dlarray object with dimensions ordered corresponding to the formats outlined in this table.

Input Format	OutputMode	Output Format
"CBT" (channel, batch, time)	"sequence"	"CBT" (channel, batch, time)
	"last"	"CB" (channel, batch)

In dlnetwork objects, BiLSTMLayer objects also support the following input and output format combinations.

Input Format	OutputMode	Output Format
"SCBT" (spatial, channel, batch)	"sequence"	"CBT" (channel, batch, time)
	"last"	"CB" (channel, batch)
"SSCBT" (spatial, spatial, channel, batch, time)	"sequence"	"CBT" (channel, batch, time)
	"last"	"CB" (channel, batch)
"SSSCBT" (spatial, spatial, spatial, channel, batch, time)	"sequence"	"CBT" (channel, batch, time)
	"last"	"CB" (channel, batch)

To use these input formats in trainNetwork workflows, first convert the data to "CBT" (channel, batch, time) format using flattenLayer.

If the HasStateInputs property is 1 (true), then the layer has two additional inputs with 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).

Compatibility Considerations

Default input weights initialization is Glorot

Behavior changed in R2019a

Starting in R2019a, the software, by default, initializes the layer input weights of this layer using the Glorot initializer. This behavior helps stabilize training and usually reduces the training time of deep networks.

In previous releases, the software, by default, initializes the layer input weights using the by sampling from a normal distribution with zero mean and variance 0.01. To reproduce this behavior, set the 'InputWeightsInitializer' option of the layer to 'narrow-normal'.

Default recurrent weights initialization is orthogonal

Behavior changed in R2019a

Starting in R2019a, the software, by default, initializes the layer recurrent weights of this layer with Q, the orthogonal matrix given by the QR decomposition of Z = QR for a random matrix Z sampled from a unit normal distribution. This behavior helps stabilize training and usually reduces the training time of deep networks.

In previous releases, the software, by default, initializes the layer recurrent weights using the by sampling from a normal distribution with zero mean and variance 0.01. To reproduce this behavior, set the 'RecurrentWeightsInitializer' option of the layer to 'narrow-normal'.

References

[1] 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.

[2] He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification." In Proceedings of the 2015 IEEE International Conference on Computer Vision, 1026–1034. Washington, DC: IEEE Computer Vision Society, 2015.

[3] Saxe, Andrew M., James L. McClelland, and Surya Ganguli. "Exact solutions to the nonlinear dynamics of learning in deep linear neural networks." arXiv preprint arXiv:1312.6120 (2013).

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

When generating code with Intel^® MKL-DNN:

• The StateActivationFunction property must be set to 'tanh'.

• The GateActivationFunction property must be set to 'sigmoid'.

• The HasStateInputs and HasStateOutputs properties must be set to 0 (false).

GPU Code Generation

Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Usage notes and limitations:

• For GPU code generation, the StateActivationFunction property must be set to 'tanh'.

• For GPU code generation, the GateActivationFunction property must be set to 'sigmoid'.

• The HasStateInputs and HasStateOutputs properties must be set to 0 (false).