Supported Networks and Layers

Supported Pretrained Networks

GPU Coder™ supports code generation for series and directed acyclic graph (DAG) convolutional neural networks (CNNs or ConvNets). You can generate code for any trained convolutional neural network whose layers are supported for code generation. See Supported Layers. You can train a convolutional neural network on either a CPU, a GPU, or multiple GPUs by using the Deep Learning Toolbox™ or use one of the pretrained networks listed in the table and generate CUDA^® code.

Network Name	Description	cuDNN	TensorRT	ARM^® Compute Library for Mali GPU
`AlexNet`	AlexNet convolutional neural network. For the pretrained AlexNet model, see `alexnet`. The syntax `alexnet('Weights','none')` is not supported for code generation.	Yes	Yes	Yes
`GoogLeNet`	GoogLeNet convolutional neural network. For the pretrained GoogLeNet model, see `googlenet`. The syntax `googlenet('Weights','none')` is not supported for code generation.	Yes	Yes	Yes
`Caffe Network`	Convolutional neural network models from Caffe. For importing a pretrained network from Caffe, see `importCaffeNetwork`.	Yes	Yes	Yes
Darknet-19	Darknet-19 convolutional neural network. For more information, see `darknet19`. The syntax `darknet19('Weights','none')` is not supported for code generation.	Yes	Yes	Yes
Darknet-53	Darknet-53 convolutional neural network. for more information, see `darknet53`. The syntax `darknet53('Weights','none')` is not supported for code generation.	Yes	Yes	Yes
DeepLab v3+	DeepLab v3+ convolutional neural network. For more information, see `deeplabv3plusLayers`.	Yes	Yes	No
DenseNet-201	DenseNet-201 convolutional neural network. For the pretrained DenseNet-201 model, see `densenet201`. The syntax `densenet201('Weights','none')` is not supported for code generation.	Yes	Yes	Yes
Inception-v3	Inception-v3 convolutional neural network. For the pretrained Inception-v3 model, see `inceptionv3`. The syntax `inceptionv3('Weights','none')` is not supported for code generation.	Yes	Yes	Yes
Inception-ResNet-v2	Inception-ResNet-v2 convolutional neural network. For the pretrained Inception-ResNet-v2 model, see `inceptionresnetv2`.	Yes	Yes	No
Mobilenet-v2	MobileNet-v2 convolutional neural network. For the pretrained MobileNet-v2 model, see `mobilenetv2`. The syntax `mobilenetv2('Weights','none')` is not supported for code generation.	Yes	Yes	Yes
NASNet-Large	NASNet-Large convolutional neural network. For the pretrained NASNet-Large model, see `nasnetlarge`.	Yes	Yes	No
NASNet-Mobile	NASNet-Mobile convolutional neural network. For the pretrained NASNet-Mobile model, see `nasnetmobile`.	Yes	Yes	No
`ResNet`	ResNet-18, ResNet-50, and ResNet-101 convolutional neural networks. For the pretrained ResNet models, see `resnet50`, `resnet18`, and `resnet101`. The syntax `resnetXX('Weights','none')` is not supported for code generation.	Yes	Yes	Yes
`SegNet`	Multi-class pixelwise segmentation network. For more information, see `segnetLayers`.	Yes	Yes	No
`SqueezeNet`	Small deep neural network. For the pretrained SqueezeNet models, see `squeezenet`. The syntax `squeezenet('Weights','none')` is not supported for code generation.	Yes	Yes	Yes
`VGG-16`	VGG-16 convolutional neural network. For the pretrained VGG-16 model, see `vgg16`. The syntax `vgg16('Weights','none')` is not supported for code generation.	Yes	Yes	Yes
`VGG-19`	VGG-19 convolutional neural network. For the pretrained VGG-19 model, see `vgg19`. The syntax `vgg19('Weights','none')` is not supported for code generation.	Yes	Yes	Yes
Xception	Xception convolutional neural network. For the pretrained Xception model, see `xception`. The syntax `xception('Weights','none')` is not supported for code generation.	Yes	Yes	Yes
YOLO v2	You only look once version 2 convolutional neural network based object detector. For more information, see `yolov2Layers`	Yes	Yes	Yes

Supported Layers

The following layers are supported for code generation by GPU Coder for the target deep learning libraries specified in the table.

Once you install the support package GPU Coder Interface for Deep Learning Libraries, you can use coder.getDeepLearningLayers to see a list of the layers supported for a specific deep learning library. For example, coder.getDeepLearningLayers('cudnn') shows the list of layers supported for code generation by using the NVIDIA^® cuDNN library.

Input Layers

Layer Name Product Description cuDNN TensorRT ARM Compute Library for Mali GPU

Layer Name	Product	Description	cuDNN	TensorRT	ARM Compute Library for Mali GPU
`imageInputLayer`	Deep Learning Toolbox	An image input layer inputs 2-D images to a network and applies data normalization. Code generation does not support `'Normalization'` specified using a function handle.	Yes	Yes	Yes
`sequenceInputLayer`	Deep Learning Toolbox	A sequence input layer inputs sequence data to a network. For code generation, only vector input types are supported. 2-D and 3-D image sequence input is not supported. Code generation does not support `'Normalization'` specified using a function handle.	Yes	Yes	No

imageInputLayer

Deep Learning Toolbox

An image input layer inputs 2-D images to a network and applies data normalization.

Code generation does not support 'Normalization' specified using a function handle.

Yes

sequenceInputLayer

Deep Learning Toolbox

A sequence input layer inputs sequence data to a network.

For code generation, only vector input types are supported. 2-D and 3-D image sequence input is not supported.

Code generation does not support 'Normalization' specified using a function handle.

Yes

Convolution and Fully Connected Layers

Layer Name	Product	Description	cuDNN	TensorRT	ARM Compute Library for Mali GPU
`convolution2dLayer`	Deep Learning Toolbox	A 2-D convolutional layer applies sliding convolutional filters to the input.	Yes	Yes	Yes
`groupedConvolution2dLayer`	Deep Learning Toolbox	A 2-D grouped convolutional layer separates the input channels into groups and applies sliding convolutional filters. Use grouped convolutional layers for channel-wise separable (also known as depth-wise separable) convolution. Code generation for the ARM Mali GPU is not supported for a 2-D grouped convolution layer that has the `NumGroups` property set as `'channel-wise'` or a value greater than two.	Yes	Yes	Yes
`transposedConv2dLayer`	Deep Learning Toolbox	A transposed 2-D convolution layer upsamples feature maps.	Yes	Yes	Yes
`fullyConnectedLayer`	Deep Learning Toolbox	A fully connected layer multiplies the input by a weight matrix and then adds a bias vector.	Yes	Yes	No

Sequence Layers

Layer Name	Product	Description	cuDNN	TensorRT	ARM Compute Library for Mali GPU
`sequenceInputLayer`	Deep Learning Toolbox	A sequence input layer inputs sequence data to a network. For code generation, only vector input types are supported. 2-D and 3-D image sequence input is not supported. Code generation does not support `'Normalization'` specified using a function handle.	Yes	Yes	No
`lstmLayer`	Deep Learning Toolbox	An LSTM layer learns long-term dependencies between time steps in time series and sequence data. For code generation, the `StateActivationFunction` property must be set to `'tanh'`. For code generation, the `GateActivationFunction` property must be set to `'sigmoid'`.	Yes	Yes	No
`bilstmLayer`	Deep Learning Toolbox	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. For code generation, the `StateActivationFunction` property must be set to `'tanh'`. For code generation, the `GateActivationFunction` property must be set to `'sigmoid'`.	Yes	Yes	No
`flattenLayer`	Deep Learning Toolbox	A flatten layer collapses the spatial dimensions of the input into the channel dimension.	Yes	No	No
`wordEmbeddingLayer`	Text Analytics Toolbox™	A word embedding layer maps word indices to vectors.	Yes	Yes	No

Activation Layers

Layer Name	Product	Description	cuDNN	TensorRT	ARM Compute Library for Mali GPU
`reluLayer`	Deep Learning Toolbox	A ReLU layer performs a threshold operation to each element of the input, where any value less than zero is set to zero.	Yes	Yes	Yes
`leakyReluLayer`	Deep Learning Toolbox	A leaky ReLU layer performs a threshold operation, where any input value less than zero is multiplied by a fixed scalar.	Yes	Yes	Yes
`clippedReluLayer`	Deep Learning Toolbox	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.	Yes	Yes	Yes
`eluLayer`	Deep Learning Toolbox	An ELU activation layer performs the identity operation on positive inputs and an exponential nonlinearity on negative inputs.	Yes	Yes	No
`tanhLayer`	Deep Learning Toolbox	A hyperbolic tangent (tanh) activation layer applies the tanh function on the layer inputs.	Yes	Yes	Yes

Normalization, Dropout, and Cropping Layers

Layer Name	Product	Description	cuDNN	TensorRT	ARM Compute Library for Mali GPU
`batchNormalizationLayer`	Deep Learning Toolbox	A batch normalization layer normalizes each input channel across a mini-batch.	Yes	Yes	Yes
`crossChannelNormalizationLayer`	Deep Learning Toolbox	A channel-wise local response (cross-channel) normalization layer carries out channel-wise normalization.	Yes	Yes	Yes
`dropoutLayer`	Deep Learning Toolbox	A dropout layer randomly sets input elements to zero with a given probability.	Yes	Yes	Yes
`crop2dLayer`	Deep Learning Toolbox	A 2-D crop layer applies 2-D cropping to the input.	Yes	Yes	Yes

Pooling and Unpooling Layers

Layer Name	Product	Description	cuDNN	TensorRT	ARM Compute Library for Mali GPU
`averagePooling2dLayer`	Deep Learning Toolbox	An average pooling layer performs down-sampling by dividing the input into rectangular pooling regions and computing the average values of each region.	Yes	Yes	Yes
`globalAveragePooling2dLayer`	Deep Learning Toolbox	A global average pooling layer performs down-sampling by computing the mean of the height and width dimensions of the input.	Yes	Yes	Yes
`maxPooling2dLayer`	Deep Learning Toolbox	A max pooling layer performs down-sampling by dividing the input into rectangular pooling regions, and computing the maximum of each region.	Yes	Yes	Yes
`globalMaxPooling2dLayer`	Deep Learning Toolbox	A global max pooling layer performs down-sampling by computing the maximum of the height and width dimensions of the input.	Yes	Yes	Yes
`maxUnpooling2dLayer`	Deep Learning Toolbox	A max unpooling layer unpools the output of a max pooling layer.	Yes	Yes	No

Combination Layers

Layer Name	Product	Description	cuDNN	TensorRT	ARM Compute Library for Mali GPU
`additionLayer`	Deep Learning Toolbox	An addition layer adds inputs from multiple neural network layers element-wise.	Yes	Yes	Yes
`depthConcatenationLayer`	Deep Learning Toolbox	A depth concatenation layer takes inputs that have the same height and width and concatenates them along the third dimension (the channel dimension).	Yes	Yes	Yes
`concatenationLayer`	Deep Learning Toolbox	A concatenation layer takes inputs and concatenates them along a specified dimension.	Yes	Yes	No

Object Detection Layers

Layer Name	Product	Description	cuDNN	TensorRT	ARM Compute Library for Mali GPU
`anchorBoxLayer`	Computer Vision Toolbox™	An anchor box layer stores anchor boxes for a feature map used in object detection networks.	Yes	Yes	Yes
`ssdMergeLayer`	Computer Vision Toolbox	An SSD merge layer merges the outputs of feature maps for subsequent regression and classification loss computation.	Yes	Yes	No
`YOLOv2OutputLayer`	Computer Vision Toolbox	Create output layer for YOLO v2 object detection network.	Yes	Yes	Yes
`YOLOv2ReorgLayer`	Computer Vision Toolbox	Create reorganization layer for YOLO v2 object detection network.	Yes	Yes	Yes
`YOLOv2TransformLayer`	Computer Vision Toolbox	Create transform layer for YOLO v2 object detection network.	Yes	Yes	Yes

Output Layers

Layer Name	Product	Description	cuDNN	TensorRT	ARM Compute Library for Mali GPU
`softmaxLayer`	Deep Learning Toolbox	A softmax layer applies a softmax function to the input.	Yes	Yes	Yes
`classificationLayer`	Deep Learning Toolbox	A classification layer computes the cross entropy loss for multi-class classification problems with mutually exclusive classes.	Yes	Yes	Yes
`regressionLayer`	Deep Learning Toolbox	A regression layer computes the half-mean-squared-error loss for regression problems.	Yes	Yes	Yes
`pixelClassificationLayer`	Computer Vision Toolbox	A pixel classification layer provides a categorical label for each image pixel or voxel.	Yes	Yes	Yes
`dicePixelClassificationLayer`	Computer Vision Toolbox	A Dice pixel classification layer provides a categorical label for each image pixel or voxel using generalized Dice loss.	Yes	Yes	Yes
`Output Layer`	Deep Learning Toolbox	All output layers including custom classification or regression output layers created by using `nnet.layer.ClassificationLayer` or `nnet.layer.RegressionLayer`. For an example showing how to define a custom classification output layer and specify a loss function, see Define Custom Classification Output Layer (Deep Learning Toolbox). For an example showing how to define a custom regression output layer and specify a loss function, see Define Custom Regression Output Layer (Deep Learning Toolbox).	Yes	Yes	Yes

Keras and ONNX Layers

Layer Name	Product	Description	cuDNN	TensorRT	ARM Compute Library for Mali GPU
`nnet.keras.layer.FlattenCStyleLayer`	Deep Learning Toolbox	Flatten activations into 1-D assuming C-style (row-major) order.	Yes	Yes	Yes
`nnet.keras.layer.GlobalAveragePooling2dLayer`	Deep Learning Toolbox	Global average pooling layer for spatial data.	Yes	Yes	Yes
`nnet.keras.layer.SigmoidLayer`	Deep Learning Toolbox	Sigmoid activation layer.	Yes	Yes	Yes
`nnet.keras.layer.TanhLayer`	Deep Learning Toolbox	Hyperbolic tangent activation layer.	Yes	Yes	Yes
`nnet.keras.layer.ZeroPadding2dLayer`	Deep Learning Toolbox	Zero padding layer for 2-D input.	Yes	Yes	Yes
`nnet.onnx.layer.ElementwiseAffineLayer`	Deep Learning Toolbox	Layer that performs element-wise scaling of the input followed by an addition.	Yes	Yes	Yes
`nnet.onnx.layer.FlattenLayer`	Deep Learning Toolbox	Flattens the spatial dimensions of the input tensor to the channel dimensions.	Yes	Yes	Yes
`nnet.onnx.layer.IdentityLayer`	Deep Learning Toolbox	Layer that implements ONNX identity operator.	Yes	Yes	Yes

Supported Classes

The following classes are supported for code generation by GPU Coder for the target deep learning libraries specified in the table.

Name Product Description cuDNN TensorRT ARM Compute Library for Mali GPU

Name	Product	Description	cuDNN	TensorRT	ARM Compute Library for Mali GPU
`yolov2ObjectDetector`	Computer Vision Toolbox	Detect objects using YOLO v2 object detector Only the `detect` method of the `yolov2ObjectDetector` is supported for code generation. The `roi` argument to the `detect` method must be a codegen constant (`coder.const()`) and a 1x4 vector. Only the `Threshold`, `SelectStrongest`, `MinSize`, `MaxSize`, and `MiniBatchSize` Name-Value pairs are supported. The height, width, channel, and batch size of the input image must be fixed size. The minimum batch size value passed to detect method must be fixed size. The labels output is returned as a cell array of character vectors, such as {'car','bus'}.	Yes	Yes	Yes
`ssdObjectDetector`	Computer Vision Toolbox	Object to detect objects using the SSD-based detector. Only the `detect` method of the `ssdObjectDetector` is supported for code generation. The `roi` argument to the `detect` method must be a codegen constant (`coder.const()`) and a 1x4 vector. Only the `Threshold`, `SelectStrongest`, `MinSize`, `MaxSize`, and `MiniBatchSize` Name-Value pairs are supported. All Name-Value pairs must be compile-time constants. The channel and batch size of the input image must be fixed size. The `labels` output is returned as a categorical array. In the generated code, the input is rescaled to the size of the input layer of the network. But the bounding box that the `detect` method returns is in reference to the original input size. The bounding boxes might not numerically match the simulation results.	Yes	Yes	No

yolov2ObjectDetector

Computer Vision Toolbox

Detect objects using YOLO v2 object detector

Only the detect method of the yolov2ObjectDetector is supported for code generation.
The roi argument to the detect method must be a codegen constant (coder.const()) and a 1x4 vector.
Only the Threshold, SelectStrongest, MinSize, MaxSize, and MiniBatchSize Name-Value pairs are supported.
The height, width, channel, and batch size of the input image must be fixed size.
The minimum batch size value passed to detect method must be fixed size.
The labels output is returned as a cell array of character vectors, such as {'car','bus'}.

Yes

ssdObjectDetector

Computer Vision Toolbox

Object to detect objects using the SSD-based detector.

Only the detect method of the ssdObjectDetector is supported for code generation.
The roi argument to the detect method must be a codegen constant (coder.const()) and a 1x4 vector.
Only the Threshold, SelectStrongest, MinSize, MaxSize, and MiniBatchSize Name-Value pairs are supported. All Name-Value pairs must be compile-time constants.
The channel and batch size of the input image must be fixed size.
The labels output is returned as a categorical array.
In the generated code, the input is rescaled to the size of the input layer of the network. But the bounding box that the detect method returns is in reference to the original input size.
The bounding boxes might not numerically match the simulation results.

Yes

Documentation