AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |
Back to Blog
Caffe swish activation9/1/2023 ![]() Input LayersĪ bidirectional LSTM (BiLSTM) layer learns bidirectional The following layers are supported for code generation by GPU Coder for the target deep learning libraries specified in the table. For more information, see yolov2Layers (Computer Vision Toolbox) You only look once version 2 convolutional neural network based object detector. The syntax xception('Weights','none') is not supported for code generation. For the pretrained Xception model, see xception (Deep Learning Toolbox). The syntax vgg19('Weights','none') is not supported for code generation. For the pretrained VGG-19 model, see vgg19 (Deep Learning Toolbox). The syntax vgg16('Weights','none') is not supported for code generation. For the pretrained VGG-16 model, see vgg16 (Deep Learning Toolbox). The syntax squeezenet('Weights','none') is not supported for code generation. For the pretrained SqueezeNet models, see squeezenet (Deep Learning Toolbox). For more information, see segnetLayers (Computer Vision Toolbox). Multi-class pixelwise segmentation network. The syntax resnetXX('Weights','none') is not supported for code generation. ![]() For the pretrained ResNet models, see resnet50 (Deep Learning Toolbox), resnet18 (Deep Learning Toolbox), and resnet101 (Deep Learning Toolbox). ResNet-18, ResNet-50, and ResNet-101 convolutional neural networks. For the pretrained NASNet-Mobile model, see nasnetmobile (Deep Learning Toolbox). NASNet-Mobile convolutional neural network. For the pretrained NASNet-Large model, see nasnetlarge (Deep Learning Toolbox). NASNet-Large convolutional neural network. The syntax mobilenetv2('Weights','none') is not supported for code generation. For the pretrained MobileNet-v2 model, see mobilenetv2 (Deep Learning Toolbox). MobileNet-v2 convolutional neural network. The syntax inceptionv3('Weights','none') is not supported for code generation. For the pretrained Inception-v3 model, see inceptionv3 (Deep Learning Toolbox). Inception-v3 convolutional neural network. For the pretrained Inception-ResNet-v2 model, see inceptionresnetv2 (Deep Learning Toolbox). Inception-ResNet-v2 convolutional neural network. The syntax googlenet('Weights','none') is not supported for code generation. For the pretrained GoogLeNet model, see googlenet (Deep Learning Toolbox). ![]() Pretrained EfficientNet-b0 model, see efficientnetb0 (Deep Learning Toolbox). The syntax densenet201('Weights','none') is not supported for code generation.ĮfficientNet-b0 convolutional neural network. For the pretrained DenseNet-201 model, see densenet201 (Deep Learning Toolbox). For more information, see deeplabv3plusLayers (Computer Vision Toolbox).ĭenseNet-201 convolutional neural network. The syntax darknet53('Weights','none') is not supported for code generation.ĭeepLab v3+ convolutional neural network. for more information, see darknet53 (Deep Learning Toolbox). The syntax darknet19('Weights','none') is not supported for code generation.ĭarknet-53 convolutional neural network. For more information, see darknet19 (Deep Learning Toolbox). For importing a pretrained network from Caffe, see importCaffeNetwork (Deep Learning Toolbox).ĭarknet-19 convolutional neural network. The syntax alexnet('Weights','none') is not supported for code generation.Ĭonvolutional neural network models from Caffe. For the pretrained AlexNet model, see alexnet (Deep Learning Toolbox). The simplicity of Swish and its similarity to ReLU make itĮasy for practitioners to replace ReLUs with Swish units in any neural network.AlexNet convolutional neural network. ![]() For example, simply replacing ReLUs with Swish units improves top-1Ĭlassification accuracy on ImageNet by 0.9\% for Mobile NASNet-A and 0.6\% for To work better than ReLU on deeper models across a number of challengingĭatasets. Our experiments show that the best discovered activationįunction, $f(x) = x \cdot \text(\beta x)$, which we name Swish, tends The searches by conducting an empirical evaluation with the best discoveredĪctivation function. Using a combination of exhaustive and reinforcement learning-based search, weĭiscover multiple novel activation functions. To leverage automatic search techniques to discover new activation functions. Have managed to replace it due to inconsistent gains. Currently, the most successfulĪnd widely-used activation function is the Rectified Linear Unit (ReLU).Īlthough various hand-designed alternatives to ReLU have been proposed, none On the training dynamics and task performance. Download a PDF of the paper titled Searching for Activation Functions, by Prajit Ramachandran and 2 other authors Download PDF Abstract: The choice of activation functions in deep networks has a significant effect ![]()
0 Comments
Read More
Leave a Reply. |