importKerasLayers - (To be removed) Import layers from Keras network - MATLAB (original) (raw)

(To be removed) Import layers from Keras network

Syntax

Description

[layers](#mw%5F3d015d1d-ae71-40d6-ad1e-d402f52b2273) = importKerasLayers([modelfile](#d126e136001)) imports the layers of a TensorFlow™-Keras network from a model file. The function returns the layers defined in the HDF5 (.h5) or JSON (.json) file given by the file name modelfile.

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

example

[layers](#mw%5F3d015d1d-ae71-40d6-ad1e-d402f52b2273) = importKerasLayers([modelfile](#d126e136001),[Name,Value](#namevaluepairarguments)) imports the layers from a TensorFlow-Keras network with additional options specified by one or more name-value pair arguments.

For example, importKerasLayers(modelfile,'ImportWeights',true) imports the network layers and the weights from the model filemodelfile.

example

Examples

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

Type importKerasLayers 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 layers from the model file 'digitsDAGnet.h5' at the command line. If the required support package is installed, then the function returns a LayerGraph object.

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

Warning: "importKerasLayers" is not recommended and will be removed in a future release. To import TensorFlow-Keras models, save using the SavedModel format and use importNetworkFromTensorFlow function.

net = LayerGraph with properties:

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

Import the network layers from the model file digitsDAGnet.h5.

modelfile = 'digitsDAGnet.h5'; layers = importKerasLayers(modelfile)

layers = LayerGraph with properties:

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

Plot the network architecture.

Specify the network file to import.

modelfile = 'digitsDAGnet.h5';

Import network layers.

layers = importKerasLayers(modelfile)

layers = LayerGraph with properties:

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

Load a data set for training a classifier to recognize new digits.

folder = fullfile(toolboxdir('nnet'),'nndemos','nndatasets','DigitDataset'); imds = imageDatastore(folder, ... 'IncludeSubfolders',true, ... 'LabelSource','foldernames');

Partition the dataset into training and test sets.

numTrainFiles = 750; [imdsTrain,imdsTest] = splitEachLabel(imds,numTrainFiles,'randomize');

Set the training options.

options = trainingOptions('sgdm', ... 'MaxEpochs',10, ... 'InitialLearnRate',0.001);

Train network using training data.

net = trainNetwork(imdsTrain,layers,options);

Training on single CPU. |========================================================================================| | Epoch | Iteration | Time Elapsed | Mini-batch | Mini-batch | Base Learning | | | | (hh:mm:ss) | Accuracy | Loss | Rate | |========================================================================================| | 1 | 1 | 00:00:00 | 15.63% | 12.6982 | 0.0010 | | 1 | 50 | 00:00:06 | 63.28% | 1.2109 | 0.0010 | | 2 | 100 | 00:00:10 | 85.16% | 0.4196 | 0.0010 | | 3 | 150 | 00:00:15 | 96.09% | 0.1760 | 0.0010 | | 4 | 200 | 00:00:20 | 99.22% | 0.0453 | 0.0010 | | 5 | 250 | 00:00:25 | 100.00% | 0.0374 | 0.0010 | | 6 | 300 | 00:00:30 | 96.88% | 0.1221 | 0.0010 | | 7 | 350 | 00:00:35 | 100.00% | 0.0086 | 0.0010 | | 7 | 400 | 00:00:40 | 100.00% | 0.0166 | 0.0010 | | 8 | 450 | 00:00:45 | 100.00% | 0.0097 | 0.0010 | | 9 | 500 | 00:00:50 | 100.00% | 0.0046 | 0.0010 | | 10 | 550 | 00:00:55 | 100.00% | 0.0031 | 0.0010 | | 10 | 580 | 00:00:58 | 100.00% | 0.0059 | 0.0010 | |========================================================================================|

Run the trained network on the test set that was not used to train the network and predict the image labels (digits).

YPred = classify(net,imdsTest); YTest = imdsTest.Labels;

Calculate the accuracy.

accuracy = sum(YPred == YTest)/numel(YTest)

Specify the network file to import layers and weights from.

modelfile = 'digitsDAGnet.h5';

Import the network architecture and weights from the files you specified. To import the layer weights, specify 'ImportWeights' to be true. The function also imports the layers with their weights from the same HDF5 file.

layers = importKerasLayers(modelfile,'ImportWeights',true)

layers = LayerGraph with properties:

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

View the size of the weights in the second layer.

weights = layers.Layers(2).Weights; size(weights)

The function has imported the weights so the layer weights are non-empty.

Specify the network file to import layers from and the file containing weights.

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

Import the network architecture and weights from the files you specified. The .json file does not include an output layer. Specify the output layer, so that importKerasLayers adds an output layer at the end of the networks architecture.

layers = importKerasLayers(modelfile, ... 'ImportWeights',true, ... 'WeightFile',weights, ... 'OutputLayerType','classification')

layers = LayerGraph with properties:

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

Import layers from a Keras network that has parametric rectified linear unit (PReLU) layers.

A PReLU layer performs a threshold operation, where for each channel, any input value less than zero is multiplied by a scalar. The PReLU operation is given by

f(xi)={xiif xi>0aixiif xi≤0

where xi is the input of the nonlinear activation f on channel i, and ai is the scaling parameter controlling the slope of the negative part. The subscript i in ai indicates that the parameter can be a vector and the nonlinear activation can vary on different channels.

importKerasNetwork and importKerasLayers can import a network that includes PReLU layers. These functions support both scalar-valued and vector-valued scaling parameters. If a scaling parameter is a vector, then the functions replace the vector with the average of the vector elements. You can modify a PReLU layer to have a vector-valued scaling parameter after import.

Specify the network file to import.

modelfile = 'digitsDAGnetwithPReLU.h5';

digitsDAGnetwithPReLU includes two PReLU layers. One has a scalar-valued scaling parameter, and the other has a vector-valued scaling parameter.

Import the network architecture and weights from modelfile.

layers = importKerasLayers(modelfile,'ImportWeights',true);

Warning: Layer 'p_re_lu_1' is a PReLU layer with a vector-valued parameter. The function replaces the parameter with the average of the vector elements. You can change the parameter back to a vector after import.

The importKerasLayers function displays a warning for the PReLu layer p_re_lu_1. The function replaces the vector-valued scaling parameter of p_re_lu_1 with the average of the vector elements. You can change the parameter back to a vector. First, find the index of the PReLU layer by viewing the Layers property.

ans = 13×1 Layer array with layers:

 1   'input_1'                       Image Input             28×28×1 images
 2   'conv2d_1'                      Convolution             20 7×7×1 convolutions with stride [1  1] and padding 'same'
 3   'conv2d_2'                      Convolution             20 3×3×1 convolutions with stride [1  1] and padding 'same'
 4   'p_re_lu_1'                     PReLU                   PReLU layer
 5   'p_re_lu_2'                     PReLU                   PReLU layer
 6   'max_pooling2d_1'               Max Pooling             2×2 max pooling with stride [2  2] and padding 'same'
 7   'max_pooling2d_2'               Max Pooling             2×2 max pooling with stride [2  2] and padding 'same'
 8   'flatten_1'                     Keras Flatten           Flatten activations into 1-D assuming C-style (row-major) order
 9   'flatten_2'                     Keras Flatten           Flatten activations into 1-D assuming C-style (row-major) order
10   'concatenate_1'                 Depth concatenation     Depth concatenation of 2 inputs
11   'dense_1'                       Fully Connected         10 fully connected layer
12   'dense_1_softmax'               Softmax                 softmax
13   'ClassificationLayer_dense_1'   Classification Output   crossentropyex

layers has two PReLU layers. Extract the fourth layer p_re_lu_1, which originally had a vector-valued scaling parameter for a channel dimension.

tempLayer = layers.Layers(4)

tempLayer = PreluLayer with properties:

    Name: 'p_re_lu_1'
RawAlpha: [20×1 single]

Learnable Parameters Alpha: 0.0044

Show all properties

The RawAlpha property contains the vector-valued scaling parameter, and the Alpha property contains a scalar that is an element average of the vector values. Reshape RawAlpha to place the vector values in the third dimension, which corresponds to the channel dimension. Then, replace Alpha with the reshaped RawAlpha values.

tempLayer.Alpha = reshape(tempLayer.RawAlpha,[1,1,numel(tempLayer.RawAlpha)])

tempLayer = PreluLayer with properties:

    Name: 'p_re_lu_1'
RawAlpha: [20×1 single]

Learnable Parameters Alpha: [1×1×20 single]

Show all properties

Replace the p_re_lu_1 layer in layers with tempLayer.

layers = replaceLayer(layers,'p_re_lu_1', tempLayer); layers.Layers(4)

ans = PreluLayer with properties:

    Name: 'p_re_lu_1'
RawAlpha: [20×1 single]

Learnable Parameters Alpha: [1×1×20 single]

Show all properties

Now the p_re_lu_1 layer has a vector-valued scaling parameter.

Input Arguments

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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 can optionally supply the weights using the 'ImportWeights' and'WeightFile' name-value pair arguments. If you supply the weights, then the weights file must be in HDF5 format.

Example: 'digitsnet.h5'

Data Types: char | string

Name-Value Arguments

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

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.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.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]

Indicator to import weights as well as the network architecture, specified as either false or true.

Example: 'ImportWeights',true

Data Types: logical

Weight file name, from which to import weights when modelfile does not include weights, specified as a character vector or a string scalar. To use this name-value pair argument, you also must set 'ImportWeights' to true.

Weight 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.

Example: 'WeightFile','weights.h5'

Data Types: char | string

Output Arguments

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Network architecture, returned as a Layer array object when the Keras network is of type Sequential, or returned as aLayerGraph object when the Keras network is of type Model.

Limitations

More About

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

* For a PReLU layer, importKerasLayers 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.

importKerasLayers 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.

importKerasLayers returns the network architecturelayers as a Layer or LayerGraph object. For code generation, you must first convert the imported Layer orLayerGraph object to a network. Convert a Layer orLayerGraph object to a DAGNetwork orSeriesNetwork object by using assembleNetwork. Convert aLayer or LayerGraph object to adlnetwork object by using dlnetwork. 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.

importKerasLayers does not execute on a GPU. However,importKerasLayers imports the layers of a pretrained neural network for deep learning as a Layer array or LayerGraph 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, see GPU Computing Requirements (Parallel Computing Toolbox).

Tips

Alternative Functionality

References

Version History

Introduced in R2017b

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