1068e173eafd15e5aa9f40b9f73590a7f5be4a84,keras/layers/convolutional_recurrent.py,ConvRNN2D,compute_output_shape,#ConvRNN2D#Any#,150

Before Change

        if self.return_state:
            output_shape = [output_shape]
            if cell.data_format == "channels_first":
                output_shape += [(input_shape[0], cell.filters, rows, cols)
                                 for _ in range(2)]
            elif cell.data_format == "channels_last":
                output_shape += [(input_shape[0], rows, cols, cell.filters)
                                 for _ in range(2)]
        return output_shape

    def build(self, input_shape):
        // Note input_shape will be list of shapes of initial states and

After Change

        if self.return_state:
            output_shape = [output_shape]
            base = (input_shape[0], rows, cols, cell.filters)
            base = transpose_shape(base, cell.data_format, spatial_axes=(1, 2))
            output_shape += [base[:] for _ in range(2)]
        return output_shape

    def build(self, input_shape):

Instances