# exportNetworkToTensorFlow

## Description

`exportNetworkToTensorFlow(`

exports the MATLAB`net`

,`modelPackage`

)^{®} deep learning network `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 a`DAGNetwork`

or`SeriesNetwork`

object. Use`trainnet`

or a custom training loop to train a`dlnetwork`

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 the`modelPackage`

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 in`HDF5`

or`SavedModel`

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 in`HDF5`

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 the`customLayers`

folder only when the MATLAB network or layer graph contains a custom or built-in MATLAB layer that`exportNetworkToTensorFlow`

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`

with`OperationDimension`

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 an`lstmProjectedLayer`

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 to`ind+1`

.

## Version History

**Introduced in R2022b**

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