squeezenet - (Not recommended) SqueezeNet convolutional neural network - MATLAB (original) (raw)

squeezenet is not recommended. Use the imagePretrainedNetwork function instead.

There are no plans to remove support for the squeezenet function. However, the imagePretrainedNetwork function has additional functionality that helps with transfer learning workflows. For example, you can specify the number of classes in your data using thenumClasses option, and the function returns a network that is ready for retraining without the need for modification.

The imagePretrainedNetwork function returns the network as adlnetwork object, which does not store the class names, To get the class names of the pretrained network, use the second output argument of theimagePretrainedNetwork function.

This table shows some typical usages of the squeezenet function and how to update your code to use theimagePretrainedNetwork function instead.

The imagePretrainedNetwork returns a dlnetwork object, which also has these advantages:

• dlnetwork objects are a unified data type that supports network building, prediction, built-in training, visualization, compression, verification, and custom training loops.
• dlnetwork objects support a wider range of network architectures that you can create or import from external platforms.
• The trainnet function supports dlnetwork objects, which enables you to easily specify loss functions. You can select from built-in loss functions or specify a custom loss function.
• Training and prediction with dlnetwork objects is typically faster than LayerGraph and trainNetwork workflows.

To train a neural network specified as a dlnetwork object, use the trainnet function.