SingleShot single = new SingleShot(model_path, in_info, out_info) If there is an invalid parameter, ArgumentException is raised: /* Create SingleShot instance with model information */ The remaining two parameters are the input and the output TensorsInfo instances. The first parameter is the absolute path to the neural network model file. You can load the neural network model from storage and configure a runtime environment with the class. String model_path = ResourcePath + "models/mobilenet_v1_1.0_224_quant.tflite"
Since the model file is located in the resource directory of your own application, you need to get its absolute path: string ResourcePath = .DirectoryInfo.Resource Loading Neural Network Model and Configuring Runtime Environment In_info.AddTensorInfo(TensorType.UInt8, new int ) Then, you can add the tensor information such as datatype, dimension, and name (optional) as shown in the following code: /* Input Dimension: 3 * 224 * 224 */ To configure the tensor information, you need to create a new instance of the class. The output data type of the model is the same as the input datatype but the output dimension is 1001 X 1 X 1 X 1. The input data type of the model is specified as bit width of each Tensor and its input dimension is 3 X 224 X 224. This model is used for image classification. In the example mentioned in this page, the MobileNet v1 model for TensorFlow Lite is used. If the model file you want to use is located in the media storage or the external storage, the application has to request permission by adding the following privileges to the tizen-manifest.xml file: To use the methods and properties of the class or its related classes such as and, include the namespace in your application: using To enable your application to use the Machine Learning Inference API functionality: You can fetch the inference result after invoking the respective model. Invoking the neural network model with input dataĪfter setting up the SingleShot instance with its required information, you can invoke the model with the input data and get the inference output result.įetching the inference result after invoking You can load the neural network model from storage and configure a runtime environment. Loading a neural network model and configuring a runtime environment You can configure the input and output Tensor Information such as its name, data type and dimension. Managing tensor information, which is the metadata: dimensions and types of tensors
However, this feature is not available in. This feature is available in Native APIs from Tizen 5.5. You can also use the Pipeline feature to manage the topology of data and the interconnection between processors and models. Then, you can get the inference output result. After loading the model, you can invoke it with a single instance of input data. You can use the class, to load the existing neural network model or your own specific model from the storage. You can use the following machine learning feature in your.
Machine learning (ML) inference feature introduces how you can easily invoke the neural network model and get the inference output result effortlessly and efficiently. You can find the detailed guide for this at the below link.
TIZEN STUDIO ANDROID API INSTALL
In order to use this functionality, you need to install Visual Studio Tools for Tizen and Tizen SDK.
TIZEN STUDIO ANDROID API TV
Since Tizen 5.5, Machine Learning Inference functionality has been provided on Mobile, Wearable and TV profile. NET Application with Machine Learning APIs.
TIZEN STUDIO ANDROID API HOW TO
This document provides you how to write a Tizen.
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