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- # Copyright (C) 2019 Intel Corporation. All rights reserved.
- # SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
- import tensorflow as tf
- import numpy as np
- import pathlib
- model = tf.keras.Sequential([
- tf.keras.layers.InputLayer(input_shape=[5, 5, 1]),
- tf.keras.layers.AveragePooling2D(
- pool_size=(5, 5), strides=None, padding="valid", data_format=None)
- ])
- def representative_dataset():
- for _ in range(1000):
- data = np.random.randint(0, 25, (1, 5, 5, 1))
- yield [data.astype(np.float32)]
- converter = tf.lite.TFLiteConverter.from_keras_model(model)
- converter.optimizations = [tf.lite.Optimize.DEFAULT]
- converter.representative_dataset = representative_dataset
- converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
- converter.inference_input_type = tf.uint8 # or tf.int8
- converter.inference_output_type = tf.uint8 # or tf.int8
- tflite_model = converter.convert()
- tflite_models_dir = pathlib.Path("./")
- tflite_model_file = tflite_models_dir / "quantized_model.tflite"
- tflite_model_file.write_bytes(tflite_model)
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