tanhLayer

Hyperbolic tangent (tanh) layer

Description

A hyperbolic tangent (tanh) activation layer applies the tanh function on the layer inputs.

Creation

Syntax

layer = tanhLayer

layer = tanhLayer('Name',Name)

Description

layer = tanhLayer creates a hyperbolic tangent layer.

layer = tanhLayer('Name',Name) additionally specifies the optional Name property. For example, tanhLayer('Name','tanh1') creates a tanh layer with the name 'tanh1'.

Properties

Name — Layer name

"" (default) | character vector | string scalar

Layer name, specified as a character vector or string scalar. For Layer array input, the trainnet and dlnetwork functions automatically assign names to layers with the name "".

The TanhLayer object stores this property as a character vector.

Data Types: char | string

NumInputs — Number of inputs

1 (default)

This property is read-only.

Number of inputs to the layer, returned as 1. This layer accepts a single input only.

Data Types: double

InputNames — Input names

{'in'} (default)

This property is read-only.

Input names, returned as {'in'}. This layer accepts a single input only.

Data Types: cell

NumOutputs — Number of outputs

1 (default)

This property is read-only.

Number of outputs from the layer, returned as 1. This layer has a single output only.

Data Types: double

OutputNames — Output names

{'out'} (default)

This property is read-only.

Output names, returned as {'out'}. This layer has a single output only.

Data Types: cell

Examples

Create Hyperbolic Tangent Layer

Create a hyperbolic tangent (tanh) layer with the name 'tanh1'.

layer = tanhLayer('Name','tanh1')

layer = 
  TanhLayer with properties:

    Name: 'tanh1'

   Learnable Parameters
    No properties.

   State Parameters
    No properties.

Use properties method to see a list of all properties.

Include a tanh layer in a Layer array.

layers = [
    imageInputLayer([28 28 1])
    convolution2dLayer(3,16)
    batchNormalizationLayer
    tanhLayer
    
    maxPooling2dLayer(2,'Stride',2)
    convolution2dLayer(3,32)
    batchNormalizationLayer
    tanhLayer
    
    fullyConnectedLayer(10)
    softmaxLayer]

layers = 
  10x1 Layer array with layers:

     1   ''   Image Input           28x28x1 images with 'zerocenter' normalization
     2   ''   2-D Convolution       16 3x3 convolutions with stride [1  1] and padding [0  0  0  0]
     3   ''   Batch Normalization   Batch normalization
     4   ''   Tanh                  Hyperbolic tangent
     5   ''   2-D Max Pooling       2x2 max pooling with stride [2  2] and padding [0  0  0  0]
     6   ''   2-D Convolution       32 3x3 convolutions with stride [1  1] and padding [0  0  0  0]
     7   ''   Batch Normalization   Batch normalization
     8   ''   Tanh                  Hyperbolic tangent
     9   ''   Fully Connected       10 fully connected layer
    10   ''   Softmax               softmax

Algorithms

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, you can describe 2-D image data that is represented as a 4-D 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, as having the format "SSCB" (spatial, spatial, channel, batch).

TanhLayer objects apply an element-wise operation and supports input data of any format. The layer and does not add or remove any dimensions, so it outputs data with the same format as its inputs.

Complex Numbers

TanhLayer objects support complex-valued input and outputs. (since R2024a) The layer applies the same underlying operation to complex-valued input as it does to real-valued input and outputs complex-valued data where applicable.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

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

Version History

Introduced in R2019a

R2024a: Support for complex-valued inputs and outputs

TanhLayer objects support complex-valued input and outputs. The layer applies the same underlying operation to complex-valued input as it does to real-valued input and outputs complex-valued data where applicable.