Classify data using a trained recurrent neural network and update the network state

You can make predictions using a trained deep learning network 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.

`[`

classifies the data in `updatedNet`

,`YPred`

] = classifyAndUpdateState(`recNet`

,`sequences`

)`sequences`

using the trained recurrent
neural network `recNet`

and updates the network state.

This function supports recurrent neural networks only. The input
`recNet`

must have at least one recurrent layer.

`[`

uses any of the arguments in the previous syntaxes and additional options specified
by one or more `updatedNet`

,`YPred`

] = classifyAndUpdateState(___,`Name,Value`

)`Name,Value`

pair arguments. For example,
`'MiniBatchSize',27`

classifies data using mini-batches of size
27

`[`

uses any of the arguments in the previous syntaxes, returns a matrix of
classification scores, and updates the network state.`updatedNet`

,`YPred`

,`scores`

] = classifyAndUpdateState(___)

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.

[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

`bilstmLayer`

| `classify`

| `gruLayer`

| `lstmLayer`

| `predict`

| `predictAndUpdateState`

| `resetState`

| `sequenceInputLayer`