Source code for platypush.message.response.tensorflow

from typing import Dict, List, Union, Optional

import numpy as np
from tensorflow.keras.models import Model

from platypush.message.response import Response

[docs]class TensorflowResponse(Response): """ Generic Tensorflow response. """
[docs] def __init__(self, *args, model: Model, model_name: Optional[str] = None, **kwargs): """ :param model: Name of the model. """ super().__init__(*args, output={ 'model': model_name or, }, **kwargs) self.model = model
[docs]class TensorflowTrainResponse(TensorflowResponse): """ Tensorflow model fit/train response. """
[docs] def __init__(self, *args, epochs: List[int], history: Dict[str, List[Union[int, float]]], **kwargs): """ :param epochs: List of epoch indexes the model has been trained on. :param history: Train history, as a ``metric -> [values]`` dictionary where each value in ``values`` is the value for of that metric on a specific epoch. """ super().__init__(*args, **kwargs) self.output['epochs'] = epochs self.output['history'] = history
[docs]class TensorflowPredictResponse(TensorflowResponse): """ Tensorflow model prediction response. """
[docs] def __init__(self, *args, prediction: np.ndarray, output_labels: Optional[List[str]] = None, **kwargs): super().__init__(*args, **kwargs) if output_labels and len(output_labels) == self.model.outputs[-1].shape[-1]: self.output['outputs'] = [ {output_labels[i]: value for i, value in enumerate(p)} for p in prediction ] else: self.output['outputs'] = prediction if self.model.__class__.__name__ != 'LinearModel': prediction = [int(np.argmax(p)) for p in prediction] if output_labels: self.output['predictions'] = [output_labels[p] for p in prediction] else: self.output['predictions'] = prediction
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