List of Deep Learning Layer Blocks and Subsystems - MATLAB & Simulink (original) (raw)

This page provides a list of deep learning layer blocks and subsystems in Simulink®. To export a MATLAB® object-based network to a Simulink model that uses deep learning layer blocks and subsystems, use the exportNetworkToSimulink function. Use layer blocks for networks that have a small number of learnable parameters and that you intend to deploy to embedded hardware.

Deep Learning Layer Blocks

The exportNetworkToSimulink function generates these blocks and subsystems to represent layers in a network. Each block and subsystem corresponds to a layer object in MATLAB. For each layer in a network, the function generates the corresponding block or subsystem. If no corresponding block or subsystem exists, then the function generates a placeholder subsystem that contains an Assertion (Simulink) block.

Some layer blocks and subsystems have reduced functionality compared to the corresponding layer objects. The Limitations column in the tables in this section lists conditions where the blocks and subsystems do not have parity with the corresponding layer objects. Unless otherwise specified in the Limitations column, theexportNetworkToSimulink function throws an error for layer objects that have unsupported configurations.

For a list of deep learning layer objects in MATLAB, see List of Deep Learning Layers.

Activation Layers

Block	Corresponding Layer Object	Description	Limitations
Clipped ReLU Layer	clippedReluLayer	A clipped ReLU layer performs a threshold operation, where any input value less than zero is set to zero and any value above the clipping ceiling is set to that clipping ceiling.
Leaky ReLU Layer	leakyReluLayer	A leaky ReLU layer performs a threshold operation, where any input value less than zero is multiplied by a fixed scalar.
ReLU Layer	reluLayer	A ReLU layer performs a threshold operation to each element of the input, where any value less than zero is set to zero.
Sigmoid Layer	sigmoidLayer	A sigmoid layer applies a sigmoid function to the input such that the output is bounded in the interval (0,1).
Softmax Layer	softmaxLayer	A softmax layer applies a softmax function to the input.	If you specify a data format that contains spatial (S) dimensions, the spatial dimensions of the input data must be singleton dimensions.
Tanh Layer	tanhLayer	A hyperbolic tangent (tanh) activation layer applies the tanh function on the layer inputs.

Combination Layers

Block	Corresponding Layer Object	Description	Limitations
Addition Layer	additionLayer	An addition layer adds inputs from multiple neural network layers element-wise.	The additionLayer object accepts scalar and vector inputs and expands those inputs to have the same dimensions as the matrix inputs, but the Addition Layer block supports expanding only scalar inputs.
Concatenation Layer	concatenationLayer	A concatenation layer takes inputs and concatenates them along a specified dimension. The inputs must have the same size in all dimensions except the concatenation dimension.
Depth Concatenation Layer	depthConcatenationLayer	A depth concatenation layer takes inputs that have the same height and width and concatenates them along the channel dimension.
Multiplication Layer	multiplicationLayer	A multiplication layer multiplies inputs from multiple neural network layers element-wise.	The multiplicationLayer object accepts scalar and vector inputs and expands those inputs to have the same dimensions as the matrix inputs, but the Multiplication Layer block supports expanding only scalar inputs.

Convolution and Fully Connected Layers

Block	Corresponding Layer Object	Description	Limitations
Convolution 1D Layer	convolution1dLayer	A 1-D convolutional layer applies sliding convolutional filters to 1-D input.	The Layer parameter has limited support for the 'manual' padding mode and does not support the 'causal' padding mode. It is recommended to use a convolution layer object that has thePaddingMode property set to'same'.The Layer parameter does not support convolution layer objects that have thePaddingValue property set to"symmetric-exclude-edge". If you specify an object that uses that padding value, the block produces a warning and uses the value"symmetric-include-edge" instead.The Layer parameter does not support convolution layer objects that have theDilationFactor property set to a value other than 1.
Convolution 2D Layer	convolution2dLayer	A 2-D convolutional layer applies sliding convolutional filters to 2-D input.
Convolution 3D Layer	convolution3dLayer	A 3-D convolutional layer applies sliding cuboidal convolution filters to 3-D input.
Fully Connected Layer	fullyConnectedLayer	A fully connected layer multiplies the input by a weight matrix and then adds a bias vector.

Input Layers

For input layer objects that have the Normalization property set to "none", the exportNetworkToSimulink function generates an Inport (Simulink) block.

Block	Corresponding Layer Object	Description	Limitations
Rescale-Symmetric 1D	featureInputLayer or sequenceInputLayer that has theNormalization property set to"rescale-symmetric"	The Rescale-Symmetric 1D block inputs 1-dimensional data to a neural network and rescales the input to be in the range [-1, 1].	The Layer parameter does not support objects that have theSplitComplexInputs property set to 1 (true).The 2D and 3D blocks support only input data that has 1 or 3 channels corresponding to grayscale or RGB image data, respectively.
Rescale-Symmetric 2D	imageInputLayer that has theNormalization property set to"rescale-symmetric"	The Rescale-Symmetric 2D block inputs 2-dimensional image data to a neural network and rescales the input to be in the range [-1, 1].
Rescale-Symmetric 3D	image3dInputLayer that has theNormalization property set to"rescale-symmetric"	The Rescale-Symmetric 3D block inputs 3-dimensional image data to a neural network and rescales the input to be in the range [-1, 1].
Rescale-Zero-One 1D	featureInputLayer or sequenceInputLayer that has theNormalization property set to"rescale-zero-one"	The Rescale-Zero-One 1D block inputs 1-dimensional data to a neural network and rescales the input to be in the range [0, 1].
Rescale-Zero-One 2D	imageInputLayer that has theNormalization property set to"rescale-zero-one"	The Rescale-Zero-One 2D block inputs 2-dimensional image data to a neural network and rescales the input to be in the range [0, 1].
Rescale-Zero-One 3D	image3dInputLayer that has theNormalization property set to"rescale-zero-one"	The Rescale-Zero-One 3D block inputs 3-dimensional image data to a neural network and rescales the input to be in the range [0, 1].
Zerocenter 1D	featureInputLayer or sequenceInputLayer that has theNormalization property set to"zerocenter"	The Zerocenter 1D block inputs 1-dimensional data to a neural network and rescales the input by subtracting the value of the Mean property of the layer object that you pass into the block.
Zerocenter 2D	imageInputLayer that has theNormalization property set to"zerocenter"	The Zerocenter 2D block inputs 2-dimensional image data to a neural network and rescales the input by subtracting the value of the Mean property of the layer object that you pass into the block.
Zerocenter 3D	image3dInputLayer that has theNormalization property set to"zerocenter"	The Zerocenter 3D block inputs 3-dimensional image data to a neural network and rescales the input by subtracting the value of the Mean property of the layer object that you pass into the block.
Zscore 1D	featureInputLayer or sequenceInputLayer that has theNormalization property set to"zscore"	The Zscore 1D block inputs 1-dimensional data to a neural network and rescales the input by subtracting the value of the Mean property of the layer object that you pass into the block, then dividing by the value of the StandardDeviation property.
Zscore 2D	imageInputLayer that has theNormalization property set to"zscore"	The Zscore 2D block inputs 2-dimensional image data to a neural network and rescales the input by subtracting the value of the Mean property of the layer object that you pass into the block, then dividing by the value of the StandardDeviation property.
Zscore 3D	image3dInputLayer that has theNormalization property set to"zscore"	The Zscore 3D block inputs 3-dimensional image data to a neural network and rescales the input by subtracting the value of the Mean property of the layer object that you pass into the block, then dividing by the value of the StandardDeviation property.

Exporting networks with input layer objects that have theSplitComplexInputs property set to 1 (true) is not supported.

Normalization Layers

Block	Corresponding Layer Object	Description	Limitations
Batch Normalization Layer	batchNormalizationLayer	A batch normalization layer normalizes a mini-batch of data for each channel independently.
Layer Normalization Layer	layerNormalizationLayer	A layer normalization layer normalizes a mini-batch of data across all channels.	If you set the Data format parameter to SSC orSSSC, theLayer parameter does not supportlayerNormalizationLayer objects that have the OperationDimension set to'channel-only'.

Pooling Layers

Block	Corresponding Layer Object	Description	Limitations
Average Pooling 1D Layer	averagePooling1dLayer	A 1-D average pooling layer performs downsampling by dividing the input into 1-D pooling regions, then computing the average of each region.	The Layer parameter has limited support for the 'manual' padding mode. It is recommended to use an average pooling layer object that has the PaddingMode property set to 'same'.The Layer parameter does not support average pooling layer objects that have thePaddingValue property set to"mean". If you specify an object that uses that padding value, the block produces a warning and uses the value 0 instead.
Average Pooling 2D Layer	averagePooling2dLayer	A 2-D average pooling layer performs downsampling by dividing the input into rectangular pooling regions, then computing the average of each region.
Average Pooling 3D Layer	averagePooling3dLayer	A 3-D average pooling layer performs downsampling by dividing three-dimensional input into cuboidal pooling regions, then computing the average values of each region.
Global Average Pooling 1D Layer	globalAveragePooling1dLayer	A 1-D global average pooling layer performs downsampling by outputting the average of the time or spatial dimensions of the input.
Global Average Pooling 2D Layer	globalAveragePooling2dLayer	A 2-D global average pooling layer performs downsampling by computing the mean of the height and width dimensions of the input.
Global Average Pooling 3D Layer	globalAveragePooling3dLayer	A 3-D global average pooling layer performs downsampling by computing the mean of the height, width, and depth dimensions of the input.
Global Max Pooling 1D Layer	globalMaxPooling1dLayer	A 1-D global max pooling layer performs downsampling by outputting the maximum of the time or spatial dimensions of the input.
Global Max Pooling 2D Layer	globalMaxPooling2dLayer	A 2-D global max pooling layer performs downsampling by computing the maximum of the height and width dimensions of the input.
Global Max Pooling 3D Layer	globalMaxPooling3dLayer	A 3-D global max pooling layer performs downsampling by computing the maximum of the height, width, and depth dimensions of the input.
Max Pooling 1D Layer	maxPooling1dLayer	A 1-D max pooling layer performs downsampling by dividing the input into 1-D pooling regions, then computing the maximum of each region.	The Layer parameter has limited support for the 'manual' padding mode. It is recommended to use a max pooling layer object that has the PaddingMode property set to'same'.
Max Pooling 2D Layer	maxPooling2dLayer	A 2-D max pooling layer performs downsampling by dividing the input into rectangular pooling regions, then computing the maximum of each region.
Max Pooling 3D Layer	maxPooling3dLayer	A 3-D global max pooling layer performs downsampling by computing the maximum of the height, width, and depth dimensions of the input.

Sequence Layers

Block	Corresponding Layer Object	Description	Limitations
Flatten Layer	flattenLayer	A flatten layer collapses the spatial dimensions of the input into the channel dimension.
GRU Layer	gruLayer	A GRU layer is an RNN layer that learns dependencies between time steps in time-series and sequence data.	The Layer parameter does not acceptgruLayer objects that have theHasStateInputs orHasStateOutputs properties set to1 (true).
GRU Projected Layer	gruProjectedLayer	A GRU projected layer is an RNN layer that learns dependencies between time steps in time-series and sequence data using projected learnable weights.	The Layer parameter does not acceptgruProjectedLayer objects that have theHasStateInputs orHasStateOutputs properties set to1 (true).
LSTM Layer	lstmLayer	An LSTM layer is an RNN layer that learns long-term dependencies between time steps in time-series and sequence data. The layer performs additive interactions, which can help improve gradient flow over long sequences during training.	The Layer parameter does not accept lstmLayer orlstmProjectedLayer objects that have theHasStateInputs orHasStateOutputs properties set to1 (true).
LSTM Projected Layer	lstmProjectedLayer	An LSTM projected layer is an RNN layer that learns long-term dependencies between time steps in time-series and sequence data using projected learnable weights.

Utility Layers

Block	Corresponding Layer Object	Description	Limitations
Dropout Layer	dropoutLayer	At training time, a dropout layer randomly sets input elements to zero with a given probability. At prediction time, the output of a dropout layer is equal to its input.Because deep learning layer blocks can be used only for prediction, this block has no effect and serves only as a conversion of dropoutLayer objects in the output of the exportNetworkToSimulink function.

Neural ODE Layers

Subsystem	Corresponding Layer Object	Description	Limitations
Integrator block as ODE solver and ODE network represented as layer blocks	neuralODELayer	A neural ODE layer learns to represent dynamic behavior as a system of ODEs.	The subsystem supports continuous-time integration only. For discrete time integration (for example, for fixed-point conversion applications), replace the integrator block in the subsystem with a discrete-time integrator block.