733f571ff1c5297f798d2829bb4a6fc6e3f3170b,deeplift/models.py,Model,_set_scoring_mode_for_target_layer,#Model#Any#,125

Before Change

            deeplift.util.assert_is_type(final_activation_layer,
                                         layers.Activation,
                                         "final_activation_layer")
            final_activation_type = type(final_activation_layer).__name__

            if (final_activation_type == "Sigmoid"):
                scoring_mode=ScoringMode.OneAndZeros
            elif (final_activation_type == "Softmax"):
                //new_W, new_b =\
                // deeplift.util.get_mean_normalised_softmax_weights(
                //  target_layer.W, target_layer.b)
                    //The weights need to be mean normalised before they are
                    //passed in because build_fwd_pass_vars() has already
                    //been called before this function is called,
                    //because get_output_layers() (used in this function)
                    //is updated during the build_fwd_pass_vars()
                    //call - that is why I can"t simply mean-normalise
                    //the weights right here :-( (It is a pain and a
                    //recipe for bugs to rebuild the forward pass
                    //vars after they have already been built - in
                    //particular for a model that branches because where
                    //the branches unify you need really want them to be
                    //using the same symbolic variables - no use having
                    //needlessly complicated/redundant graphs and if a node
                    //is common to two outputs, so should its symbolic vars
                    //TODO: I should put in a "reset_fwd_pass" function and use
                    //it to invalidate the _built_fwd_pass_vars cache and recompile
                //if (np.allclose(target_layer.W, new_W)==False):
                //    print("Consider mean-normalising softmax layer")
                //assert np.allclose(target_layer.b, new_b),\
                //       "Please mean-normalise weights and biases of softmax layer"
                scoring_mode=ScoringMode.OneAndZeros
            else:
                raise RuntimeError("Unsupported final_activation_type: "
                                   +final_activation_type)
        target_layer.set_scoring_mode(scoring_mode)    
    
    def save_to_yaml_only(self, file_name):
        raise NotImplementedError()    

After Change

                **kwargs)

    def _set_scoring_mode_for_target_layer(self, target_layer):
        print("TARGET LAYER SET TO "+str(target_layer.get_name()))
        if (deeplift.util.is_type(target_layer,
                                  layers.Activation)):
            raise RuntimeError("You set the target layer to an"
                  +" activation layer, which is unusual so I am"

Instances