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TensorFlow for Mobile and IoT and TensorFlow.js
Once the model is converted it can be copied onto the specific device. Of course,
this step is different for each different device. Then the model can run by using
the language you prefer. For instance, in Java the invocation happens with the
following code snippet:
try (Interpreter interpreter = new Interpreter(tensorflow_lite_model_
file)) {
interpreter.run(input, output);
}
Again, pretty simple! What is very useful is that the same steps can be followed for
a heterogeneous collection of Mobile and IoT devices.
Using GPUs and accelerators
Modern phones frequently have accelerators on board that allow floating-point
matrix operations to be performed faster. In this case, the interpreter can use the
concept of Delegate, and specifically GpuDelegate(), to use GPUs. Let's look at
an example in Java:
GpuDelegate delegate = new GpuDelegate();
Interpreter.Options options = (new Interpreter.Options()).
addDelegate(delegate);
Interpreter interpreter = new Interpreter(tensorflow_lite_model_file,
options);
try {
interpreter.run(input, output);
}
Again, the code is self-commenting. A new GpuDelegate() is created and then it is
used by the Interpreter to run the model on a GPU.
An example of application
In this section, we are going to use TensorFlow Lite for building an example
application that is later deployed on Android. We will use Android Studio
(https://developer.android.com/studio/) to compile the code. The first step is
to clone the repo with:
git clone https://github.com/tensorflow/examples
Then we open an existing project (see Figure 3) with the path examples/lite/
examples/image_classification/android.
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