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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.

```
[YPred,scores]
= classify(net,X)
```

```
[YPred,scores]
= classify(net,sequences)
```

```
[YPred,scores]
= classify(___,Name,Value)
```

`[`

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

,`scores`

]
= classify(___,`Name,Value`

)

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.

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`