5dd8c5c10ccc286a67ba4846a81701207b606e0d,keras/layers/convolutional.py,ZeroPadding2D,__init__,#ZeroPadding2D#Any#Any#,1501

Before Change

                self.left_pad = padding.get("left_pad", 0)
                self.right_pad = padding.get("right_pad", 0)
            else:
                raise ValueError("Unexpected key is found in the padding argument. "
                                 "Keys have to be in {"top_pad", "bottom_pad", "left_pad", "right_pad"}")
        except AttributeError:
            padding = tuple(padding)
            if len(padding) == 2:
                self.top_pad = padding[0]

After Change

            dim_ordering = K.image_dim_ordering()

        self.padding = padding
        if isinstance(padding, dict):
            if set(padding.keys()) <= {"top_pad", "bottom_pad", "left_pad", "right_pad"}:
                self.top_pad = padding.get("top_pad", 0)
                self.bottom_pad = padding.get("bottom_pad", 0)
                self.left_pad = padding.get("left_pad", 0)
                self.right_pad = padding.get("right_pad", 0)
            else:
                raise ValueError("Unexpected key found in `padding` dictionary. "
                                 "Keys have to be in {"top_pad", "bottom_pad", "
                                 ""left_pad", "right_pad"}."
                                 "Found: " + str(padding.keys()))
        else:
            padding = tuple(padding)
            if len(padding) == 2:
                self.top_pad = padding[0]
                self.bottom_pad = padding[0]
                self.left_pad = padding[1]
                self.right_pad = padding[1]
            elif len(padding) == 4:
                self.top_pad = padding[0]
                self.bottom_pad = padding[1]
                self.left_pad = padding[2]
                self.right_pad = padding[3]
            else:
                raise TypeError("`padding` should be tuple of int "
                                "of length 2 or 4, or dict. "
                                "Found: " + str(padding))

        assert dim_ordering in {"tf", "th"}, "`dim_ordering` must be in {"tf", "th"}."
        self.dim_ordering = dim_ordering
        self.input_spec = [InputSpec(ndim=4)]

Instances