Classify data using a trained deep learning neural network

You can make predictions using a trained neural network for deep learning on
either a CPU or GPU. Using a GPU requires
Parallel
Computing Toolbox™ and a CUDA^{®} enabled NVIDIA^{®} GPU with compute capability 3.0 or higher. Specify the hardware requirements using the
`ExecutionEnvironment`

name-value pair argument.

For networks with multiple outputs, use the `predict`

ans
set the `'ReturnCategorial'`

option to `true`

.

`[`

predicts class labels with additional options specified by one or more name-value
pair arguments.`YPred`

,`scores`

]
= classify(___,`Name,Value`

)

When making predictions with sequences of different lengths, the mini-batch size can impact the amount of padding added to the input data which can result in different predicted values. Try using different values to see which works best with your network. To specify mini-batch size and padding options, use the `'MiniBatchSize'`

and `'SequenceLength'`

options.

All functions for deep learning training,
prediction, and validation in Deep Learning
Toolbox™ perform computations using single-precision, floating-point arithmetic. Functions
for deep learning include `trainNetwork`

, `predict`

, `classify`

, and
`activations`

. The
software uses single-precision arithmetic when you train networks using both CPUs and
GPUs.

For networks with multiple outputs, use the `predict`

and
set the `'ReturnCategorial'`

option to `true`

.

You can compute the predicted scores from a trained network using `predict`

.

You can also compute the activations from a network layer using `activations`

.

For sequence-to-label and sequence-to-sequence classification networks, you can make
predictions and update the network state using `classifyAndUpdateState`

and `predictAndUpdateState`

.

[1] M. Kudo, J. Toyama, and M. Shimbo. "Multidimensional Curve Classification Using Passing-Through Regions." *Pattern Recognition Letters*. Vol. 20, No. 11–13, pages 1103–1111.

[2] *UCI Machine Learning Repository: Japanese Vowels
Dataset*.
https://archive.ics.uci.edu/ml/datasets/Japanese+Vowels

`activations`

| `classifyAndUpdateState`

| `predict`

| `predictAndUpdateState`