Open the app and select a pretrained network. You can also load a pretrained network by selecting the Designer tab and clicking New. If you need to download the network, then click Install to open the Add-On Explorer. Deep Network Designer has pretrained networks suitable for image and audio tasks. Loading pretrained audio networks requires Audio Toolbox™.

In the Designer pane, visualize and explore the network. For a list of available pretrained image classification networks and how to compare them, see Pretrained Deep Neural Networks.

For information on constructing networks using Deep Network Designer, see Build Networks with 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.

Open Deep Network Designer with SqueezeNet.

deepNetworkDesigner(squeezenet)

To prepare the network for transfer learning, replace the last learnable layer and the final classification layer. For SqueezeNet, the last learnable layer is a 2-D convolutional layer named 'conv10'.

Next, delete the classification output layer. Then, drag a new classificationLayer onto the canvas and connect it instead. The default settings for the output layer mean the network learns the number of classes during training.

Check your network by clicking Analyze in the Designer tab. The network is ready for training if Deep Learning Network Analyzer reports zero errors. For an example showing how to train a network to classify new images, see Transfer Learning with Deep Network Designer.

On the Designer pane, select a layer to view and edit the properties. Click the help icon next to the layer name for more information about the properties of the layer.

For more information about layer properties, see List of Deep Learning Layers.

In Deep Network Designer, you can build a network by dragging built-in layers from the Layer Library to the Designer pane and connecting them. You can also add custom layers from the workspace to a network in the Designer pane. Suppose that you have a custom layer stored in the variable myCustomLayer.

The app adds the custom layer to the top of the Designer pane. To see the new layer, zoom-in using a mouse or click Zoom in.

Connect myCustomLayer to the network in the Designer pane. For an example showing how build a network with a custom layer in Deep Network Designer, see Import Custom Layer into Deep Network Designer.

You can also combine networks in Deep Network Designer. For example, you can create a semantic segmentation network by combining a pretrained network with a decoder subnetwork.

To view or edit the custom layer class definition, select the layer, and then click Edit Layer Code. For more information, see View Autogenerated Custom Layers Using Deep Network Designer. (since R2023a)

You can use the Data tab of Deep Network Designer to import training and validation data. Deep Network Designer supports the import of image data and datastore objects. Select an import method based on the type of task.

Using Deep Network Designer, you can train a network using image data or any datastore object that works with the trainNetwork function. For example, you can train a semantic segmentation network or a multi-input network using a CombinedDatastore object. For more information about importing data into Deep Network Designer, see Import Data into Deep Network Designer.

To train a network on data imported into Deep Network Designer, on the Training tab, click Train. The app displays an animated plot of the training progress. The plot shows mini-batch loss and accuracy, validation loss and accuracy, and additional information on the training progress. The plot has a stop button in the top-right corner. Click the button to stop training and return the current state of the network.

For more information, see Train Networks Using Deep Network Designer.

If you require greater control over the training, click Training Options to select the training settings. For more information about selecting training options, see trainingOptions.

For an example showing how to train an image classification network, see Transfer Learning with Deep Network Designer. For an example showing how to train a sequence-to-sequence LSTM network, see Train Network for Time Series Forecasting Using Deep Network Designer.

To train a network on data not supported by Deep Network Designer, select the Designer tab, and click Export to export the initial network architecture. You can then programmatically train the network, for example, using a custom training loop.

For an example showing how to export a network from Deep Network Designer to Simulink, see Export Image Classification Network from Deep Network Designer to Simulink.

To recreate a network that you construct and train in Deep Network Designer, generate MATLAB code.

After generating a script, you can perform the following tasks.

For more information, see Generate MATLAB Code from Deep Network Designer.

You can also use Deep Network Designer to create deep learning experiments which sweep through a range of hyperparameter values or use Bayesian optimization to find optimal training options. For an example showing how to use Experiment Manager to tune the hyperparameters of a network trained in Deep Network Designer, see Generate Experiment Using Deep Network Designer.