Pytorch_CNN-RNN/original_model/innvestigate/analyzer/misc.py

# Get Python six functionality:
from __future__ import\
    absolute_import, print_function, division, unicode_literals


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from .base import AnalyzerNetworkBase
from .. import layers as ilayers
from .. import utils as iutils


__all__ = ["Random", "Input"]


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class Input(AnalyzerNetworkBase):
    """Returns the input.

    Returns the input as analysis.

    :param model: A Keras model.
    """

    def _create_analysis(self, model, stop_analysis_at_tensors=[]):
        tensors_to_analyze = [x for x in iutils.to_list(model.inputs)
                              if x not in stop_analysis_at_tensors]
        return [ilayers.Identity()(x) for x in tensors_to_analyze]


class Random(AnalyzerNetworkBase):
    """Returns noise.

    Returns the Gaussian noise as analysis.

    :param model: A Keras model.
    :param stddev: The standard deviation of the noise.
    """

    def __init__(self, model, stddev=1, **kwargs):
        self._stddev = stddev

        super(Random, self).__init__(model, **kwargs)

    def _create_analysis(self, model, stop_analysis_at_tensors=[]):
        noise = ilayers.TestPhaseGaussianNoise(stddev=self._stddev)
        tensors_to_analyze = [x for x in iutils.to_list(model.inputs)
                              if x not in stop_analysis_at_tensors]
        return [noise(x) for x in tensors_to_analyze]

    def _get_state(self):
        state = super(Random, self)._get_state()
        state.update({"stddev": self._stddev})
        return state

    @classmethod
    def _state_to_kwargs(clazz, state):
        stddev = state.pop("stddev")
        kwargs = super(Random, clazz)._state_to_kwargs(state)
        kwargs.update({"stddev": stddev})
        return kwargs