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Compute deep learning network output for inference by using a TensorFlow Lite model



Y = predict(net,X) returns the network output Y during inference given the input data X and the network net with a single input and a single output.

To use this function, you must install the Deep Learning Toolbox Interface for TensorFlow Lite support package.

[Y1,...,YN] = predict(net,X) returns the N outputs Y1, …, YN during inference for networks that have N outputs.


For prediction with SeriesNetwork and DAGNetwork objects, see predict.


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Suppose that your current working directory contains a TensorFlow™ Lite Model named mobilenet_v1_0.5_224.tflite.

Load the model by using the loadTFLite function. Inspect the object this function creates.

net = loadTFLiteModel('mobilenet_v1_0.5_224.tflite');
  TFLiteModel with properties:
            ModelName: 'mobilenet_v1_0.5_224.tflite'
            NumInputs: 1
           NumOutputs: 1
            InputSize: {[224 224 3]}
           OutputSize: {[1001 1]}
           NumThreads: 8
                 Mean: 127.5000
    StandardDeviation: 127.5000

Create a MATLAB® function that can perform inference using the object net. This function loads the Mobilenet-V1 model into a persistent network object. Then the function performs prediction by passing the network object to the predict function. Subsequent calls to this function reuse this the persistent object.

function out = tflite_predict(in)
persistent net;
if isempty(net)
    net = loadTFLiteModel('mobilenet_v1_0.5_224.tflite');
out = predict(net,in);

For an example that shows how to generate code for this function and deploy on Raspberry Pi™ hardware, see Generate Code for TensorFlow Lite (TFLite) Model and Deploy on Raspberry Pi.

Input Arguments

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TFLiteModel object that represents the TensorFlow Lite model file.

Image or sequence input to the network, specified as a numeric array.

  • For image classification networks, the input must be of shape (H,W,C,N), where H is height, W is width, C is channel, and N is batch size.

  • For recurrent neural networks, the input must be of shape (D, N, S), where D is channel or feature dimension, N is batch size, and S is timestamp or sequence length.

Output Arguments

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Output data, specified as a numeric array.

When performing inference with quantized TensorFlow Lite models, the output data is normalized in one of these ways:

  • Signed 8-bit integer type outputs are normalized as output[i] = (prediction[i] + 128) / 256.0.

  • Unsigned 8-bit integer type outputs are normalized as output[i] = prediction[i] / 255.0.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

Version History

Introduced in R2022a