Larq Compute Engine Converter¶

The LCE converter allows you to convert a Keras model built with larq to an LCE-compatible TensorFlow Lite FlatBuffer format for inference.

Installation¶

Before installing the LCE converter, please install:

Python version 3.6, 3.7, or 3.8
Tensorflow version 1.14, 1.15, 2.0, 2.1, 2.2 or 2.3 (recommended):
```
pip install tensorflow  # or tensorflow-gpu
```

You can install the LCE converter with Python's pip package manager:

pip install larq-compute-engine

API documentation¶

[source]

convert_keras_model¶

larq_compute_engine.convert_keras_model(
    model,
    *,
    inference_input_type=tf.float32,
    inference_output_type=tf.float32,
    experimental_default_int8_range=None,
    experimental_enable_bitpacked_activations=False
)

Converts a Keras model to TFLite flatbuffer.

Example

tflite_model = convert_keras_model(model)
with open("/tmp/my_model.tflite", "wb") as f:
    f.write(tflite_model)

Arguments

model tf.keras.Model: The model to convert.
inference_input_type tf.dtypes.DType: Data type of the input layer. Defaults to tf.float32, must be either tf.float32 or tf.int8.
inference_output_type tf.dtypes.DType: Data type of the output layer. Defaults to tf.float32, must be either tf.float32 or tf.int8.
experimental_default_int8_range Optional[Tuple[float, float]]: Tuple of integers representing (min, max) range values for all arrays without a specified range. Intended for experimenting with quantization via "dummy quantization". (default None)
experimental_enable_bitpacked_activations bool: Enable an experimental converter optimisation that attempts to reduce intermediate activation memory usage by bitpacking the activation tensor between consecutive binary convolutions where possible.

Returns

The converted data in serialized format.