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Multiple-Input and Multiple-Output Networks

In Deep Learning Toolbox™, you can define network architectures with multiple inputs (for example, networks trained on multiple sources and types of data) or multiple outputs (for example, networks that predicts both classification and regression responses).

Multiple-Input Networks

Define networks with multiple inputs when the network requires data from multiple sources or in different formats. For example, networks that require image data captured from multiple sensors at different resolutions.

Training

To define and train a deep learning network with multiple inputs, specify the network architecture using a layerGraph object and train using the trainNetwork function with datastore input.

To use a datastore for networks with multiple input layers, use the combine and transform functions to create a datastore that outputs a cell array with (numInputs + 1) columns, where numInputs is the number of network inputs. In this case, the first numInputs columns specify the predictors for each input and the last column specifies the responses. The order of inputs is given by the InputNames property of the layer graph layers.

Tip

If the network also has multiple outputs, then you must use a custom training loop. for more information, see Multiple-Output Networks.

Prediction

To make predictions on a trained deep learning network with multiple inputs, use either the predict or classify function. Specify multiple inputs using one of the following:

  • combinedDatastore object

  • transformedDatastore object

  • multiple numeric arrays

Multiple-Output Networks

Define networks with multiple outputs for tasks requiring multiple responses in different formats. For example, tasks requiring both categorical and numeric output.

Training

To train a deep learning network with multiple outputs, use a custom training loop. For an example, see Train Network with Multiple Outputs.

Prediction

To make predictions using a model function, use the model function directly with the trained parameters. For an example, see Make Predictions Using Model Function.

Alternatively, convert the model function to a DAGNetwork object using the assembleNetwork function. With the assembled network, you can:

  • Make predictions with other data types such as datastores using the predict function for DAGNetwork objects.

  • Specify prediction options such as the mini-batch size using the predict function for DAGNetwork objects.

  • Save the network in a MAT file.

For an example, see Assemble Multiple-Output Network for Prediction.

See Also

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