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Interpreter interface for running TensorFlow Lite models.

Used in the notebooks

Used in the guide Used in the tutorials

Models obtained from TfLiteConverter can be run in Python with Interpreter.

As an example, lets generate a simple Keras model and convert it to TFLite (TfLiteConverter also supports other input formats with from_saved_model and from_concrete_function)

x = np.array([[1.], [2.]])
y = np.array([[2.], [4.]])
model = tf.keras.models.Sequential([
          tf.keras.layers.Dense(units=1, input_shape=[1])
model.compile(optimizer='sgd', loss='mean_squared_error'), y, epochs=1)
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()

tflite_model can be saved to a file and loaded later, or directly into the Interpreter. Since TensorFlow Lite pre-plans tensor allocations to optimize inference, the user needs to call allocate_tensors() before any inference.

interpreter = tf.lite.Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()  # Needed before execution!

Sample execution:

output = interpreter.get_output_details()[0]  # Model has single output.
input = interpreter.get_input_details()[0]  # Model has single input.
input_data = tf.constant(1., shape=[1, 1])
interpreter.set_tensor(input['index'], input_data)
(1, 1)

Use get_signature_runner() for a more user-friendly inference API.

model_path Path to TF-Lite Flatbuffer file.
model_content Content of model.
experimental_delegates Experimental. Subject to change. List of TfLiteDelegate objects returned by lite.load_delegate().
num_threads Sets the number of threads used by the interpreter and available to CPU kernels. If not set, the interpreter will use an implementation-dependent default number of threads. Currently, only a subset of kernels, such as conv, support multi-threading. num_threads should be >= -1. Setting num_threads to 0 has the effect to disable multithreading, which is equivalent to setting num_threads to 1. If set to the value -1, the number of threads used will be implementation-defined and platform-dependent.
experimental_op_resolver_type The op resolver used by the interpreter. It must be an instance of OpResolverType. By default, we use the built-in op resolver which corresponds to tflite::ops::builtin::BuiltinOpResolver in C++.
experimental_preserve_all_tensors If true, then intermediate tensors used during computation are preserved for inspection, and if the passed op resolver type is AUTO or BUILTIN, the type will be changed to BUILTIN_WITHOUT_DEFAULT_DELEGATES so that no Tensorflow Lite default delegates are applied. If false, getting intermediate tensors could result in undefined values or None, especially when the graph is successfully modified by the Tensorflow Lite default delegate.

ValueError If the interpreter was unable to create.



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