The documentation example View Autogenerated Custom Layers Using Deep Network Designer shows how to import a model from TensorFlow and view the custom layer that is generated by the importTensorFlowNetwork function in the Deep Network Designer app. To learn more about this scenario, see our previous blog post Importing Models from TensorFlow, PyTorch, and ONNX.įigure: Imported networks from TensorFlow, PyTorch, or ONNX might contain autogenerated custom layers.Īnd now you can view custom layers, autogenerated or created programmatically, in Deep Network Designer! As shown in the following figure, you can view the custom layer properties and even click on “Edit Layer Code” to open the file that contains the custom layer code.įigure: View custom layer in the Deep Network Designer app. The import function might generate a custom layer in place of a layer that cannot be converted to a built-in MATLAB layer. If there is not a built-in layer that you need for your task, then you can define you own custom deep learning layer.Īnother case where a network can include custom layers is when the network is imported from an external deep learning platform, such as TensorFlow™, PyTorch®, or ONNX™.
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