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import attr |
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@attr.s |
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class NSTAlgorithmProgress: |
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tracked_metrics: dict = attr.ib(converter=lambda x: {k: [v] for k, v in dict(x).items()}) |
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_callbacks = attr.ib(default=attr.Factory(lambda self: { |
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True: self._append, |
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False: self._set, |
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}, takes_self=True)) |
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def update(self, *args, **kwargs): |
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metrics = args[0].state.metrics |
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for metric_key, value in metrics.items(): |
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self._callbacks[metric_key in self.tracked_metrics](metric_key, value) |
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def _set(self, key, value): |
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self.tracked_metrics[key] = [value] |
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def _append(self, key, value): |
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self.tracked_metrics[key].append(value) |
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# Properties |
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@property |
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def iterations(self): |
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"""Iterations completed.""" |
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return self.tracked_metrics.get('iterations', [None])[-1] |
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@property |
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def duration(self): |
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"""Time in seconds the iterative algorithm has been running.""" |
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return self.tracked_metrics.get('duration', [None])[-1] |
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@property |
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def cost_improvement(self): |
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"""Difference of loss function between the last 2 measurements. |
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Positive value indicates that the loss went down and that the learning |
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process moved towards the (local) minimum (in terms of minimizing the |
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loss/cost function). |
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So roughly, positive values indicate improvement [moving towards (local) |
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minimum] and negative indicate moving away from minimum. |
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Moving refers to the learning parameters. |
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""" |
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if 1 < len(self.tracked_metrics.get('cost', [])): |
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return self.tracked_metrics['cost'][-2] - self.tracked_metrics['cost'][-1] |
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boromir674 /
neural-style-transfer
