c60e2dfbdbb49b383b159fba7f46810c7dcbbf34,keras/layers/convolutional.py,Convolution1D,get_config,#Convolution1D#,145

Before Change

        return output

    def get_config(self):
        return {"name": self.__class__.__name__,
                "nb_filter": self.nb_filter,
                "filter_length": self.filter_length,
                "init": self.init.__name__,
                "activation": self.activation.__name__,
                "border_mode": self.border_mode,
                "subsample_length": self.subsample_length,
                "W_regularizer": self.W_regularizer.get_config() if self.W_regularizer else None,
                "b_regularizer": self.b_regularizer.get_config() if self.b_regularizer else None,
                "activity_regularizer": self.activity_regularizer.get_config() if self.activity_regularizer else None,
                "W_constraint": self.W_constraint.get_config() if self.W_constraint else None,
                "b_constraint": self.b_constraint.get_config() if self.b_constraint else None}


class Convolution2D(Layer):
    input_ndim = 4

After Change

        return output

    def get_config(self):
        config = {"name": self.__class__.__name__,
                  "nb_filter": self.nb_filter,
                  "filter_length": self.filter_length,
                  "init": self.init.__name__,
                  "activation": self.activation.__name__,
                  "border_mode": self.border_mode,
                  "subsample_length": self.subsample_length,
                  "W_regularizer": self.W_regularizer.get_config() if self.W_regularizer else None,
                  "b_regularizer": self.b_regularizer.get_config() if self.b_regularizer else None,
                  "activity_regularizer": self.activity_regularizer.get_config() if self.activity_regularizer else None,
                  "W_constraint": self.W_constraint.get_config() if self.W_constraint else None,
                  "b_constraint": self.b_constraint.get_config() if self.b_constraint else None,
                  "input_dim": self.input_dim,
                  "input_length": self.input_length}
        base_config = super(Convolution1D, self).get_config()
        return dict(base_config.items() + config.items())


class Convolution2D(Layer):
    input_ndim = 4

Instances