Method set_params

numpy_ml/neural_nets/layers/layers.py:88–128

Set the layer parameters from a dictionary of values. Parameters ---------- summary_dict : dict A dictionary of layer parameters and hyperparameters. If a required parameter or hyperparameter is not included within `summary_dict`,

(self, summary_dict)

        self.flush_gradients()

    def set_params(self, summary_dict):
        """
        Set the layer parameters from a dictionary of values.

        Parameters
        ----------
        summary_dict : dict
            A dictionary of layer parameters and hyperparameters. If a required
            parameter or hyperparameter is not included within `summary_dict`,
            this method will use the value in the current layer's
            :meth:`summary` method.

        Returns
        -------
        layer : :doc:`Layer <numpy_ml.neural_nets.layers>` object
            The newly-initialized layer.
        """
        layer, sd = self, summary_dict

        # collapse `parameters` and `hyperparameters` nested dicts into a single
        # merged dictionary
        flatten_keys = ["parameters", "hyperparameters"]
        for k in flatten_keys:
            if k in sd:
                entry = sd[k]
                sd.update(entry)
                del sd[k]

        for k, v in sd.items():
            if k in self.parameters:
                layer.parameters[k] = v
            if k in self.hyperparameters:
                if k == "act_fn":
                    layer.act_fn = ActivationInitializer(v)()
                elif k == "optimizer":
                    layer.optimizer = OptimizerInitializer(sd[k])()
                elif k == "wrappers":
                    layer = init_wrappers(layer, sd[k])
                elif k not in ["wrappers", "optimizer"]:
                    setattr(layer, k, v)
        return layer

    def summary(self):
        """Return a dict of the layer parameters, hyperparameters, and ID."""