dlfeval
Description
Use dlfeval
to evaluate custom deep learning models for
custom training loops.
Tip
For most deep learning tasks, you can use a pretrained neural network and adapt it to your own
data. For an example showing how to use transfer learning to retrain a convolutional neural
network to classify a new set of images, see Train Deep Learning Network to Classify New Images. Alternatively, you can create and train
neural networks from scratch using the trainnet
,
trainNetwork
, and trainingOptions
functions.
If the trainingOptions
function does not provide the training options that you need for your task, then you can create a custom training loop using automatic differentiation. To learn more, see Define Deep Learning Network for Custom Training Loops.
Examples
Input Arguments
Output Arguments
Tips
A
dlgradient
call must be inside a function. To obtain a numeric value of a gradient, you must evaluate the function usingdlfeval
, and the argument to the function must be adlarray
. See Use Automatic Differentiation In Deep Learning Toolbox.To enable the correct evaluation of gradients, the function
fun
must use only supported functions fordlarray
. See List of Functions with dlarray Support.
Algorithms
Extended Capabilities
Version History
Introduced in R2019b