exportNetworkToTensorFlow
Description
exportNetworkToTensorFlow(
exports the MATLAB® deep learning network net
,modelPackage
)net
and saves it as a TensorFlow™ model in the Python® package modelPackage
. For information on how to load the
TensorFlow model in Python, see Load Exported TensorFlow Model.
The exportNetworkToTensorFlow
function requires the Deep Learning Toolbox™ Converter for TensorFlow Models. If this support package is not installed, then
exportNetworkToTensorFlow
provides a download link.
exportNetworkToTensorFlow(
exports the MATLAB deep learning layer graph lgraph
,modelPackage
)lgraph
and saves it as a
TensorFlow model in the Python package modelPackage
.
If the MATLAB network or layer graph contains a custom or built-in MATLAB layer that exportNetworkToTensorFlow
cannot convert to a TensorFlow layer, the exportNetworkToTensorFlow
function exports this layer as a
custom TensorFlow layer. For more information on which MATLAB layers exportNetworkToTensorFlow
can convert to TensorFlow layers, see Layers Supported for Exporting to TensorFlow. For an example, see Export Layer Graph with Custom Layer to TensorFlow.
Examples
Export Network to TensorFlow
Save a MATLAB deep learning network as a TensorFlow model by using the exportNetworkToTensorFlow
function.
Download and install the Deep Learning Toolbox Converter for TensorFlow Models support package. You can enter exportNetworkToTensorFlow
at the command prompt to check whether the support package is installed. If the 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.
Load the pretrained squeezenet
convolutional neural network as a DAGNetwork
object.
net = squeezenet
net = DAGNetwork with properties: Layers: [68x1 nnet.cnn.layer.Layer] Connections: [75x2 table] InputNames: {'data'} OutputNames: {'ClassificationLayer_predictions'}
Export the network net
to TensorFlow. The exportNetworkToTensorFlow
function saves the TensorFlow model in the Python package myModel
.
exportNetworkToTensorFlow(net,"myModel")
Run this code in Python to load the exported TensorFlow model from the myModel
package.
import myModel model = myModel.load_model()
Save the exported model in the TensorFlow SavedModel
format. Saving model
in SavedModel
format is optional. You can perform deep learning workflows directly with model
. For an example that shows how to classify an image with the exported TensorFlow model, see Export Network to TensorFlow and Classify Image.
model.save("myModelTF")
Export Network to TensorFlow and Classify Image
Use a MATLAB network to classify an image. Save the network as a TensorFlow model and use the TensorFlow model to classify the same image.
Classify Image in MATLAB
Load the pretrained squeezenet
convolutional network as a DAGNetwork
object.
net = squeezenet
net = DAGNetwork with properties: Layers: [68x1 nnet.cnn.layer.Layer] Connections: [75x2 table] InputNames: {'data'} OutputNames: {'ClassificationLayer_predictions'}
Specify the class names.
ClassNames = net.Layers(end).Classes;
Read the image you want to classify. Resize the image to the input size of the network.
Im = imread("peppers.png");
InputSize = net.Layers(1).InputSize;
Im = imresize(Im,InputSize(1:2));
Predict class labels and classification scores.
[label,score] = classify(net,Im);
Show the image with the classification label.
imshow(Im) title(ClassNames(label),FontSize=12)
Export Network and Image Data
Export the network net
to TensorFlow. The exportNetworkToTensorFlow
function saves the TensorFlow model in the Python package myModel
.
exportNetworkToTensorFlow(net,"myModel")
Permute the 2-D image data from the Deep Learning Toolbox™ ordering (HWCN
) to the TensorFlow ordering (NHWC
), where H
, W
, and C
are the height, width, and number of channels of the image, respectively, and N
is the number of images. Save the image in a MAT file.
ImTF = permute(Im,[4,1,2,3]); filename = "peppers.mat"; save(filename,"ImTF")
Classify Image with Exported TensorFlow Model
Run this code in Python to load the exported TensorFlow model and use the model for image classification.
Load the exported model from the Python package myModel
.
import myModel model = myModel.load_model()
Classify the image with the exported model. For more information on how to compare prediction results between MATLAB and TensorFlow, see Inference Comparison Between TensorFlow and Imported Networks for Image Classification.
score_tf = model.predict(ImTF)
Export Untrained Layer Graph to TensorFlow
Export an untrained layer graph to TensorFlow and train the exported TensorFlow model.
Create Layer Graph
Create a long short-term memory (LSTM) network to classify sequence data. An LSTM network takes sequence data as input and makes predictions based on the individual time steps of the sequence data.
inputSize = 12;
numHiddenUnits = 100;
numClasses = 9;
layers = [
sequenceInputLayer(inputSize)
bilstmLayer(numHiddenUnits,OutputMode="last")
fullyConnectedLayer(numClasses)
softmaxLayer];
lgraph = layerGraph(layers);
Create Training Data Set
Load the Japanese Vowels training data set. XTrain
is a cell array containing 270 sequences of dimension 12 and varying length. TTrain
is a categorical vector of labels "1","2",..."9", which correspond to the nine speakers.
load JapaneseVowelsTrainData
Prepare the sequence data in XTrain
for padding. For more information, see Sequence Classification Using Deep Learning.
numObservations = numel(XTrain); for i=1:numObservations sequence = XTrain{i}; sequenceLengths(i) = size(sequence,2); end [sequenceLengths,idx] = sort(sequenceLengths); XTrain = XTrain(idx); TTrain = TTrain(idx);
Pad XTrain
along the second dimension.
XTrain = padsequences(XTrain,2);
Permute the sequence data from the Deep Learning Toolbox™ ordering (CSN
) to the TensorFlow ordering (NSC
), where C
is the number of features of the sequence, S
is the sequence length, and N
is the number of sequence observations. Save the training data to a MAT file.
XTrain = permute(XTrain,[3,2,1]); TTrain = double(TTrain)-1; filename = "training_data.mat"; save(filename,"XTrain","TTrain")
Export Layer Graph to TensorFlow
Export the layer graph lgraph
to TensorFlow. The exportNetworkToTensorFlow
function saves the TensorFlow model in the Python package myModel
.
exportNetworkToTensorFlow(lgraph,"myModel")
Train Exported TensorFlow Model
Run this code in Python to load the exported model from the Python package myModel
. You can compile and train the exported model in Python. To train model
, use the training data in training_data.mat
. For more information on how to load data from a MAT file into Python, see Inference Comparison Between TensorFlow and Imported Networks for Image Classification.
import myModel model = myModel.load_model()
Export Layer Graph with Custom Layer to TensorFlow
Export a layer graph, which contains a MATLAB custom layer, to TensorFlow.
Create Layer Graph
Create a PReLU layer by defining the custom layer preluLayer
. Display the definition of the custom layer.
type preluLayer.m
classdef preluLayer < nnet.layer.Layer % Example custom PReLU layer. properties (Learnable) % Layer learnable parameters. % Scaling coefficient. Alpha end methods function layer = preluLayer(args) % layer = preluLayer creates a PReLU layer. % % layer = preluLayer(numChannels,Name=name) also specifies the % layer name. arguments args.Name = ""; end % Set layer name. layer.Name = args.Name; % Set layer description. layer.Description = "PReLU"; end function layer = initialize(layer,layout) % layer = initialize(layer,layout) initializes the learnable % parameters of the layer for the specified input layout. % Skip initialization of nonempty parameters. if ~isempty(layer.Alpha) return end % Input data size. sz = layout.Size; ndims = numel(sz); % Find number of channels. idx = finddim(layout,"C"); numChannels = sz(idx); % Initialize Alpha. szAlpha = ones(1,ndims); szAlpha(idx) = numChannels; layer.Alpha = rand(szAlpha); end function Z = predict(layer, X) % Z = predict(layer, X) forwards the input data X through the % layer and outputs the result Z. Z = max(0, X) + layer.Alpha .* min(0, X); end end end
Create a layer graph.
layers = [ imageInputLayer([31 53 3],Name="image",Normalization="none") preluLayer(Name="prelu") regressionLayer]; lgraph = layerGraph(layers);
Export Layer Graph to TensorFlow
Export the layer graph lgraph
to TensorFlow. The exportNetworkToTensorFlow
function saves the TensorFlow model in the Python package myModel
and the definition of the custom layer in the customLayers
folder of the myModel
package.
exportNetworkToTensorFlow(lgraph,"myModel")
Warning: Layer 'prelu': Layer class 'preluLayer' was exported into an incomplete TensorFlow custom layer file. The custom layer definition must be completed or the file must be replaced before the model can be loaded into TensorFlow.
Display the definition of the TensorFlow custom layer preluLayer.py
.
type ./myModel/customLayers/preluLayer.py
# This file was created by # MATLAB Deep Learning Toolbox Converter for TensorFlow Models. # 19-Aug-2023 11:53:16 import tensorflow as tf import sys # Remove this line after completing the layer definition. class preluLayer(tf.keras.layers.Layer): # Add any additional layer hyperparameters to the constructor's # argument list below. def __init__(self, Alpha_Shape_=None, name=None): super(preluLayer, self).__init__(name=name) # Learnable parameters: These have been exported from MATLAB and will be loaded automatically from the weight file: self.Alpha = tf.Variable(name="Alpha", initial_value=tf.zeros(Alpha_Shape_), trainable=True) def call(self, input1): # Add code to implement the layer's forward pass here. # The input tensor format(s) are: BSSC # The output tensor format(s) are: BSSC # where B=batch, C=channels, T=time, S=spatial(in order of height, width, depth,...) # Remove the following 3 lines after completing the custom layer definition: print("Warning: load_model(): Before you can load the model, you must complete the definition of custom layer preluLayer in the customLayers folder.") print("Exiting...") sys.exit("See the warning message above.") return output1
Load Exported Layer Graph
This section describes the steps that you must perform in Python to load the exported TensorFlow model.
Edit the definition of preluLayer.py
by implementing the forward computation in call
.
def call(self, input1): output1 = tf.math.maximum(input1,0.0) + self.Alpha * tf.math.minimum(0.0,input1) return output1
Delete the lines in preluLayer.py
, as instructed by the comments in the file. View the updated custom layer preluLayer.py
.
import tensorflow as tf class preluLayer(tf.keras.layers.Layer): # Add any additional layer hyperparameters to the constructor's # argument list below. def __init__(self, Alpha_Shape_=None, name=None): super(preluLayer, self).__init__(name=name) # Learnable parameters: These have been exported from MATLAB and will be loaded automatically from the weight file: self.Alpha = tf.Variable(name="Alpha", initial_value=tf.zeros(Alpha_Shape_), trainable=True) def call(self, input1): output1 = tf.math.maximum(input1,0.0) + self.Alpha * tf.math.minimum(0.0,input1) return output1
In this example, you only have to edit preluLayer.py
. In other cases, you might have to edit model.py
to pass arguments to custom layer calls.
Before loading the model, you might have to restart your Python kernel for the changes to take effect. Load the model from the Python package myModel
.
import myModel model = myModel.load_model()
Input Arguments
net
— Deep Learning Toolbox network
SeriesNetwork
object | DAGNetwork
object | dlnetwork
object
Deep Learning Toolbox network, specified as a SeriesNetwork
object, DAGNetwork
object, or dlnetwork
object.
You can get a trained network by:
Using a Deep Learning Toolbox function to load a pretrained network. For example, use the
efficientnetb0
function.Downloading a pretrained network from the MATLAB Deep Learning Model Hub.
Training your own network. Use
trainNetwork
to train aDAGNetwork
orSeriesNetwork
object. Usetrainnet
or a custom training loop to train adlnetwork
object.
You can also export an initialized dlnetwork
object to TensorFlow.
modelPackage
— Name of Python package containing exported model
string scalar | character vector
Name of the Python package containing the exported TensorFlow model, specified as a string scalar or character vector. The
modelPackage
package contains:
The
_init_.py
file, which defines themodelPackage
folder as a regular Python package.The
model.py
file, which contains the code that defines the untrained TensorFlow-Keras model.The
README.txt
file, which provides instructions on how to load the TensorFlow model and save it inHDF5
orSavedModel
format. For more details, see Load Exported TensorFlow Model and Save Exported TensorFlow Model in Standard Format.The
weights.h5
file, which contains the model weights inHDF5
format.The
customLayers
folder, which contains one file for each exported custom layer. Each file is an incomplete definition of a TensorFlow custom layer. You must edit or replace each of these files before you can load the model in Python. The software creates thecustomLayers
folder only when the MATLAB network or layer graph contains a custom or built-in MATLAB layer thatexportNetworkToTensorFlow
cannot convert to a TensorFlow layer.
Example: "myModel"
lgraph
— Deep Learning Toolbox layer graph
LayerGraph
object | Layer
array
Deep Learning Toolbox layer graph, specified as a LayerGraph
object or Layer
array.
Limitations
To load an exported TensorFlow model, you must have:
TensorFlow version r2.0 or later
Python version 3.0 or later
The TensorFlow module
tfa
for a MATLAB network or layer graph that contains one or more of the following layers:groupNormalizationLayer
instanceNormalizationLayer
layerNormalizationLayer
withOperationDimension
set to"batch-excluded"
More About
Layers Supported for Exporting to TensorFlow
The exportNetworkToTensorFlow
function supports these Deep Learning Toolbox layers for export as TensorFlow layers.
Deep Learning Toolbox Layers
Convolution and Fully Connected Layers convolution1dLayer
convolution2dLayer
convolution3dLayer
groupedConvolution2dLayer
fullyConnectedLayer
transposedConv2dLayer
transposedConv3dLayer
*
exportNetworkToTensorFlow
exports anlstmProjectedLayer
to TensorFlow as a standard lstm layer, which means that the function exports the full-rank learnable matrices, and not the factored lower-rank matrices. This behavior does not impact the prediction performance of the layer.Activation Layers clippedReluLayer
eluLayer
geluLayer
functionLayer
leakyReluLayer
reluLayer
swishLayer
tanhLayer
Normalization, Dropout, and Cropping Layers batchNormalizationLayer
crop2dLayer
crop3dLayer
crossChannelNormalizationLayer
dropoutLayer
groupNormalizationLayer
instanceNormalizationLayer
layerNormalizationLayer
Computer Vision Toolbox™ Layers
patchEmbeddingLayer
(Computer Vision Toolbox)Image Processing Toolbox™ Layers
depthToSpace2dLayer
(Image Processing Toolbox)resize2dLayer
(Image Processing Toolbox)resize3dLayer
(Image Processing Toolbox)spaceToDepthLayer
(Image Processing Toolbox)Lidar Toolbox™ Layers
pointCloudInputLayer
(Lidar Toolbox)
Load Exported TensorFlow Model
This section describes how to load a TensorFlow model in Python from the package modelPackage
, which the
exportNetworkToTensorFlow
creates. For an example, see Export Network to TensorFlow.
Load the exported TensorFlow model with weights.
import modelPackage model = modelPackage.load_model()
Load the exported TensorFlow model without weights.
import modelPackage model = modelPackage.load_model(load_weights=False)
Save Exported TensorFlow Model in Standard Format
Optionally, you can save the exported TensorFlow model in SavedModel
or HDF5
format.
You must first load the exported TensorFlow model by following the instructions in Load Exported TensorFlow Model. For an example that
shows how to save an exported model to SavedModel
format, see Export Network to TensorFlow.
Save the loaded TensorFlow model in SavedModel
format.
model.save("modelName")
Save the loaded TensorFlow model in HDF5
format.
model.save("modelName",save_format="h5")
Tips
MATLAB uses one-based indexing, whereas Python uses zero-based indexing. In other words, the first element in an array has an index of 1 and 0 in MATLAB and Python, respectively. For more information about MATLAB indexing, see Array Indexing. In MATLAB, to use an array of indices (
ind
) created in Python, convert the array toind+1
.
Version History
Introduced in R2022b
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