Deep Network Designer

Create a classification network for numeric feature data.

Open Deep Network Designer.

deepNetworkDesigner

To create a blank network, pause on Blank Network and click New. The app opens a blank canvas where you can drag and drop layers.

From the Layer Library, drag a featureInputLayer onto the canvas. You can use the Layer Library filter to help you find layers. Select the layer. In the Properties pane, set Normalization to "zscore" and InputSize to the number of features in your data. Next, drag a fullyConnectedLayer onto the canvas. To connect the layers, pause on the featureInputLayer and click the out port. Drag the arrow to the in port of the fullyConnectedLayer.

Add a layerNormalizationLayer followed by a reluLayer to the canvas and connect them sequentially.

Finally, add a fullyConnectedLayer followed by a softmaxLayer and connect them. For the last fullyConnectedLayer, select the layer and set the OutputSize to the number of classes in your data.

To automatically align the layers, click Auto Arrange.

To check that the network is ready for training, select Analyze. The Deep Learning Network Analyzer shows that the network has zero warnings or errors.

To export the network to the workspace, click Export. To generate code to recreate the network, click Export > Generate Code without Initial Parameters.

Prepare a network for transfer learning by editing it in Deep Network Designer.

Transfer learning is the process of taking a pretrained deep learning network and fine-tuning it to learn a new task. You can quickly transfer learned features to a new task using a smaller number of training images. Transfer learning is therefore often faster and easier than training a network from scratch. To use a pretrained network for transfer learning, you must change the number of classes to match your new data set.

Deep Network Designer provides a selection of pretrained image and audio networks. To explore the pretrained networks, open Deep Network Designer.

deepNetworkDesigner

For this example, pause on SqueezeNet and click Open.

To prepare the network for transfer learning, edit the last learnable layer. For SqueezeNet, the last learnable layer is the 2-D convolutional layer 'conv10'.

Before R2023b: To edit the layer properties for a new task, you must replace the layers instead of unlocking them.

Check your network by clicking Analyze. The network is ready for training if Deep Learning Network Analyzer reports zero errors. To export the prepared network to the workspace, click Export. To generate MATLAB code to create the network, click Generate Code with Initial Parameters.

For an example showing how to train a network to classify new images, see Prepare Network for Transfer Learning Using Deep Network Designer.

Import a neural network from PyTorch® or TensorFlow™.

For this example, import the MNASNet (Copyright© Soumith Chintala 2016) PyTorch model. Download the mnasnet1_0 file, which is approximately 17 MB in size, from the MathWorks website.

modelfile = matlab.internal.examples.downloadSupportFile("nnet", ...
    "data/PyTorchModels/mnasnet1_0.pt");

To import a network from an external platform, use Deep Network Designer.

deepNetworkDesigner

You can import models from PyTorch® or TensorFlow™.

For PyTorch® models, the From PyTorch icon. In the Import PyTorch Model dialog box, copy the location of the model file and then click Import.

When importing PyTorch networks using Deep Network Designer, you have the option to specify the input sizes of the network. This option is only available when specifying the input sizes might make the import more successful, for example, by reducing the number of custom layers.

The app imports the network and generates an Import Report. The Import Report displays any issues with the network that require attention before you use the network for training or inference. For example, the report shows a warning if the app is unable to infer the input sizes or if you need to complete any placeholder layers.

For an example that shows how to import a PyTorch® network into Deep Network Designer and fix any issues, see Import PyTorch® Model Using Deep Network Designer.

Export a trained network from Deep Network Designer to Simulink®.

Open Deep Network Designer.

deepNetworkDesigner

Pause on SqueezeNet and click Open. To export the network to Simulink, select Export > Export to Simulink. In the Export to Simulink dialog, select a location to save the MAT file with the network parameters and then select Export. During export, if the network is not initialized, then the app attempts to initialize it.

The blocks generated by the app depend on the type of network.

For SqueezeNet, the app generates a Predict block.

The Deep Network Designer app enables you to generate MATLAB® code that recreates the network architecture.

Running the generated script returns the network architecture as a dlnetwork object. For an example of training a network exported from Deep Network Designer, see Get Started with Deep Network Designer. To learn more about generating code, see Generate MATLAB Code from Deep Network Designer.