forked from platypush/platypush
1f1fefca9d
TODO: Extend neural network implementation to work also with e.g. input from images, sounds or binary
34 lines
1,014 B
Python
34 lines
1,014 B
Python
from typing import Dict, List, Union
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from platypush.message.response import Response
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class TensorflowResponse(Response):
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"""
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Generic Tensorflow response.
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"""
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def __init__(self, *args, model: str, **kwargs):
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"""
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:param model: Name of the model.
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"""
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super().__init__(*args, output={
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'model': model,
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}, **kwargs)
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class TensorflowTrainResponse(TensorflowResponse):
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"""
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Tensorflow model fit/train response.
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"""
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def __init__(self, *args, epochs: List[int], history: Dict[str, List[Union[int, float]]], **kwargs):
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"""
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:param epochs: List of epoch indexes the model has been trained on.
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:param history: Train history, as a ``metric -> [values]`` dictionary where each value in ``values`` is
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the value for of that metric on a specific epoch.
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"""
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super().__init__(*args, **kwargs)
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self.output['epochs'] = epochs
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self.output['history'] = history
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# vim:sw=4:ts=4:et:
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