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Convert to tflite with randomly picked representative dataset
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| > ``` | |
| > $ sudo edgetpu_compiler coral.tflite | |
| > | |
| > Edge TPU Compiler version 2.0.267685300 | |
| > Invalid model: coral.tflite | |
| > Model not quantized | |
| > ``` | |
| > | |
| > P.S. I found solution | |
| > | |
| > ``` | |
| > model = tf.keras.models.Sequential([ | |
| > tf.keras.layers.Flatten(input_shape=(28, 28)), | |
| > tf.keras.layers.Dense(128, activation='relu'), | |
| > tf.keras.layers.Dropout(0.2), | |
| > tf.keras.layers.Dense(10, activation='softmax') | |
| > ]) | |
| > | |
| > model.compile(optimizer='adam', | |
| > loss='sparse_categorical_crossentropy', | |
| > metrics=['accuracy']) | |
| > | |
| > model.fit(x_train, y_train, epochs=1) | |
| > model.evaluate(x_test, y_test, verbose=2) | |
| > | |
| > image_shape = (28, 28) | |
| > def representative_dataset_gen(): | |
| > num_calibration_images = 10 | |
| > for i in range(num_calibration_images): | |
| > image = tf.random.normal([1] + list(image_shape)) | |
| > yield [image] | |
| > | |
| > converter = tf.lite.TFLiteConverter.from_keras_model(model) | |
| > | |
| > converter.optimizations = [tf.lite.Optimize.DEFAULT] | |
| > converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] | |
| > converter.representative_dataset = representative_dataset_gen | |
| > converter.inference_input_type = tf.uint8 | |
| > converter.inference_output_type = tf.uint8 | |
| > tflite_quant_model = converter.convert() | |
| > | |
| > open("coral8.tflite", "wb").write(tflite_quant_model) | |
| > ``` | |
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