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
.
For an example showing how to train a network with both image and feature input, see Train Network on Image and Feature Data.
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
objecttransformedDatastore
objectmultiple 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:
For an example, see Assemble Multiple-Output Network for Prediction.