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Mobile machine learning with the Fritz CLI. https://fritz.ai
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| # Convert to mobile formats | |
| import coremltools | |
| import tensorflow as tf | |
| import tempfile | |
| def convert_to_coreml(model): | |
| return coremltools.converters.keras.convert( | |
| model, | |
| input_names=['input'], | |
| output_names=['digit'] | |
| ) | |
| def convert_to_tflite(model): | |
| # save the model to a temp file so we can | |
| # convert it. | |
| keras_file = tempfile.mktemp() | |
| model.save(keras_file, include_optimizer=False) | |
| converter = tf.lite.TFLiteConverter.from_keras_model_file(keras_file) | |
| return converter.convert() | |
| mlmodel = convert_to_coreml(model) | |
| tflite = convert_to_tflite(model) | |
| model_name = "mnist_cnn_lr001_batchsize128" | |
| # Keras | |
| model.save( | |
| model_name + ".h5", | |
| include_optimizer="False" | |
| ) | |
| # Core ML | |
| mlmodel.save(model_name + ".mlmodel") | |
| # TensorFlow Lite | |
| with open(model_name + ".tflite", 'wb') as wfid: | |
| wfid.write(tflite) |
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