importKerasNetwork - (To be removed) Import pretrained Keras network and weights - MATLAB (original) (raw)

(To be removed) Import pretrained Keras network and weights

Syntax

Description

[net](#mw%5Fd83e368b-4577-4d5d-ad17-0df8f074d5af) = importKerasNetwork([modelfile](#mw%5F0535e0ab-5c73-45b9-9933-3a04a7989fb2)) imports a pretrained TensorFlow™-Keras network and its weights from modelfile.

This function requires the Deep Learning Toolbox™ Converter for TensorFlow Models support package. If this support package is not installed, the function provides a download link.

example

[net](#mw%5Fd83e368b-4577-4d5d-ad17-0df8f074d5af) = importKerasNetwork([modelfile](#mw%5F0535e0ab-5c73-45b9-9933-3a04a7989fb2),[Name,Value](#namevaluepairarguments)) imports a pretrained TensorFlow-Keras network and its weights with additional options specified by one or more name-value pair arguments.

For example, importKerasNetwork(modelfile,'WeightFile',weights) imports the network from the model file modelfile and weights from the weight file weights. In this case, modelfile can be in HDF5 or JSON format, and the weight file must be in HDF5 format.

Examples

collapse all

Download and install the Deep Learning Toolbox Converter for TensorFlow Models support package.

Type importKerasNetwork at the command line.

If the Deep Learning Toolbox Converter for TensorFlow Models support package is not installed, then the function provides a link to the required support package in the Add-On Explorer. To install the support package, click the link, and then click Install. Check that the installation is successful by importing the network from the model file 'digitsDAGnet.h5' at the command line. If the required support package is installed, then the function returns a DAGNetwork object.

modelfile = 'digitsDAGnet.h5'; net = importKerasNetwork(modelfile)

Warning: Saved Keras networks do not include classes. Classes will be set to categorical(1:N), where N is the number of classes in the classification output layer of the network. To specify classes, use the 'Classes' argument.

net = DAGNetwork with properties:

     Layers: [13×1 nnet.cnn.layer.Layer]
Connections: [13×2 table]
 InputNames: {'input_1'}
OutputNames: {'ClassificationLayer_activation_1'}

Specify the file to import. The file digitsDAGnet.h5 contains a directed acyclic graph convolutional neural network that classifies images of digits.

modelfile = 'digitsDAGnet.h5';

Import the network.

net = importKerasNetwork(modelfile)

Warning: Saved Keras networks do not include classes. Classes will be set to categorical(1:N), where N is the number of classes in the classification output layer of the network. To specify classes, use the 'Classes' argument.

net = DAGNetwork with properties:

     Layers: [13×1 nnet.cnn.layer.Layer]
Connections: [13×2 table]
 InputNames: {'input_1'}
OutputNames: {'ClassificationLayer_activation_1'}

Plot the network architecture.

plot(net) title('DAG Network Architecture')

Specify the network and the weight files to import.

modelfile = 'digitsDAGnet.json'; weights = 'digitsDAGnet.weights.h5';

This is a directed acyclic graph convolutional neural network trained on the digits data.

Import network architecture and import the weights from separate files. The .json file does not have an output layer or information on the cost function. Specify the output layer type when you import the files.

net = importKerasNetwork(modelfile,'WeightFile',weights, ... 'OutputLayerType','classification')

Warning: Saved Keras networks do not include classes. Classes will be set to categorical(1:N), where N is the number of classes in the classification output layer of the network. To specify classes, use the 'Classes' argument.

net = DAGNetwork with properties:

     Layers: [13×1 nnet.cnn.layer.Layer]
Connections: [13×2 table]
 InputNames: {'input_1'}
OutputNames: {'ClassificationLayer_activation_1'}

Specify the model file.

modelfile = 'digitsDAGnet.h5';

Specify class names.

classNames = {'0','1','2','3','4','5','6','7','8','9'};

Import the Keras network with the class names.

net = importKerasNetwork(modelfile,'Classes',classNames);

Read the image to classify.

digitDatasetPath = fullfile(toolboxdir('nnet'),'nndemos','nndatasets','DigitDataset'); I = imread(fullfile(digitDatasetPath,'5','image4009.png'));

Classify the image using the pretrained network.

Display the image and the classification result.

imshow(I) title(['Classification result: ' char(label)])

Input Arguments

collapse all

Name of the model file containing the network architecture, and possibly the weights, specified as a character vector or a string scalar. The file must be in the current folder, in a folder on the MATLAB® path, or you must include a full or relative path to the file.

If modelfile includes

If modelfile includes only the network architecture, then you must supply the weights in an HDF5 file, using the 'WeightFile' name-value pair argument.

Example: 'digitsnet.h5'

Data Types: char | string

Name-Value Arguments

collapse all

Specify optional pairs of arguments asName1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: importKerasNetwork(modelfile,'OutputLayerType','classification','Classes',classes) imports a network from the model file modelfile, adds an output layer for a classification problem at the end of the Keras layers, and specifiesclasses as the classes of the output layer.

Name of file containing weights, specified as a character vector or a string scalar.WeightFile must be in the current folder, in a folder on the MATLAB path, or you must include a full or relative path to the file.

Example: 'WeightFile','weights.h5'

Type of output layer that the function appends to the end of the imported network architecture when modelfile does not specify a loss function, specified as 'classification', 'regression', or'pixelclassification'. Appending a pixelClassificationLayer (Computer Vision Toolbox) object requires Computer Vision Toolbox™.

If a network in modelfile has multiple outputs, then you cannot specify the output layer types using this argument. Use importKerasLayers instead. importKerasLayers inserts placeholder layers for the outputs. After importing, you can find and replace the placeholder layers by using findPlaceholderLayers and replaceLayer, respectively.

Example: 'OutputLayerType','regression'

Size of the input images for the network, specified as a vector of two or three numerical values corresponding to [height,width] for grayscale images and [height,width,channels] for color images, respectively. The network uses this information when the modelfile does not specify the input size.

If a network in modelfile has multiple inputs, then you cannot specify the input sizes using this argument. Use importKerasLayers instead. importKerasLayers inserts placeholder layers for the inputs. After importing, you can find and replace the placeholder layers by using findPlaceholderLayers and replaceLayer, respectively.

Example: 'ImageInputSize',[28 28]

Classes of the output layer, specified as a categorical vector, string array, cell array of character vectors, or 'auto'. If you specify a string array or cell array of character vectors str, then the software sets the classes of the output layer to categorical(str,str). IfClasses is 'auto', then the function sets the classes to categorical(1:N), where N is the number of classes.

Data Types: char | categorical | string | cell

Output Arguments

collapse all

Pretrained Keras network, returned as one of the following:

Limitations

More About

collapse all

importKerasNetwork supports the following TensorFlow-Keras layer types for conversion into built-in MATLAB layers, with some limitations.

* For a PReLU layer, importKerasNetwork replaces a vector-valued scaling parameter with the average of the vector elements. You can change the parameter back to a vector after import. For an example, see Import Keras PReLU Layer.

importKerasNetwork supports the following Keras loss functions:

You can use MATLAB Coder™ or GPU Coder™ together with Deep Learning Toolbox to generate MEX, standalone CPU, CUDA® MEX, or standalone CUDA code for an imported network. For more information, see Generate Code and Deploy Deep Neural Networks.

importKerasNetwork returns the networknet as a DAGNetwork orSeriesNetwork object. Both these objects support code generation. For more information on MATLAB Coder and GPU Coder support for Deep Learning Toolbox objects, see Supported Classes (MATLAB Coder) and Supported Classes (GPU Coder), respectively.

You can generate code for any imported network whose layers support code generation. For lists of the layers that support code generation with MATLAB Coder and GPU Coder, see Supported Layers (MATLAB Coder) and Supported Layers (GPU Coder), respectively. For more information on the code generation capabilities and limitations of each built-in MATLAB layer, see the Extended Capabilities section of the layer. For example, seeCode Generation and GPU Code Generation of imageInputLayer.

importKerasNetwork does not execute on a GPU. However,importKerasNetwork imports a pretrained neural network for deep learning as a DAGNetwork or SeriesNetwork object, which you can use on a GPU.

Using a GPU requires a Parallel Computing Toolbox™ license and a supported GPU device. For information about supported devices, seeGPU Computing Requirements (Parallel Computing Toolbox).

Tips

Alternative Functionality

References

Version History

Introduced in R2017b

collapse all

Starting in R2023b, the importKerasNetwork function warns. Use importNetworkFromTensorFlow instead. TheimportNetworkFromTensorFlow function has these advantages overimportKerasNetwork: