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