fm_classifier.py 文件源码

def _make_feed_dict(self, X, y):
        # Make the dictionary mapping tensor placeholders to input data.
        if self.is_sparse_:
            x_inds = np.vstack(X.nonzero())
            x_srt = np.lexsort(x_inds[::-1, :])
            x_inds = x_inds[:, x_srt].T.astype(np.int64)
            x_vals = np.squeeze(np.array(
                X[x_inds[:, 0], x_inds[:, 1]])).astype(np.float32)
            x_shape = np.array(X.shape).astype(np.int64)
            feed_dict = {self._x_inds: x_inds,
                         self._x_vals: x_vals,
                         self._x_shape: x_shape}
        else:
            feed_dict = {self._x: X.astype(np.float32)}

        if self._output_size == 1:
            feed_dict[self._y] = y.astype(np.float32)
        else:
            feed_dict[self._y] = y.astype(np.int32)

        return feed_dict