mixture.py 文件源码

def get_split(self, layer,
                  deterministic=True, conv_all_patches=True, **kwargs):

        # Get the patches and the outputs without the non-linearities.
        if type(layer) is L.DenseLayer:
            x, y = get_dense_xy(layer, deterministic)
        elif type(layer) is L.Conv2DLayer:
            if conv_all_patches is True:
                x, y = get_conv_xy_all(layer, deterministic)
            else:
                x, y = get_conv_xy(layer, deterministic)
        else:
            raise ValueError("Unknown layer as input")

        # Create an output dictionary
        outputs = dict()

        for name, fun in subtypes:
            outputs[name] = dict()
            mrk_y = 1.0* T.cast(fun(y), dtype=theano.config.floatX)  # (N,O)
            y_current = y*mrk_y # This has a binary mask
            cnt_y = T.shape_padaxis(T.sum(mrk_y, axis=0), axis=0)  # (1,O)
            norm = T.maximum(cnt_y, 1.)

            # Count how many datapoints are considered
            outputs[name]['cnt'] = cnt_y

            # The mean of the current batch
            outputs[name]['m_y'] = T.shape_padaxis(y_current.sum(axis=0), axis=0) / norm  # (1,O) mean output for batch
            outputs[name]['m_x'] = T.dot(x.T, mrk_y) / norm  # (D,O) mean input for batch

            # The mean of the current batch
            outputs[name]['yty'] = T.shape_padaxis(T.sum(y_current ** 2., axis=0), axis=0) / norm  # (1,O)
            outputs[name]['xty'] = T.dot(x.T, y_current) / norm  # D,O

        return dict_to_list(outputs)