def _unblockify_then_matricize(self, vec):
"""Flatten the block dimensions then reshape to a batch matrix."""
# Suppose
# tensor_shape.TensorShape(vec.shape) = [v0, v1, v2, v3],
# self.block_depth = 2.
# Then
# leading shape = [v0, v1]
# block shape = [v2, v3].
# We will reshape vec to
# [v1, v2*v3, v0].
# Un-blockify: Flatten block dimensions. Reshape
# [v0, v1, v2, v3] --> [v0, v1, v2*v3].
if tensor_shape.TensorShape(vec.shape).is_fully_defined():
# vec_shape = [v0, v1, v2, v3]
vec_shape = tensor_shape.TensorShape(vec.shape).as_list()
# vec_leading_shape = [v0, v1]
vec_leading_shape = vec_shape[:-self.block_depth]
# vec_block_shape = [v2, v3]
vec_block_shape = vec_shape[-self.block_depth:]
# flat_shape = [v0, v1, v2*v3]
flat_shape = vec_leading_shape + [np.prod(vec_block_shape)]
else:
vec_shape = array_ops.shape(vec)
vec_leading_shape = vec_shape[:-self.block_depth]
vec_block_shape = vec_shape[-self.block_depth:]
flat_shape = array_ops.concat(
(vec_leading_shape, [math_ops.reduce_prod(vec_block_shape)]), 0)
vec_flat = array_ops.reshape(vec, flat_shape)
# Matricize: Reshape to batch matrix.
# [v0, v1, v2*v3] --> [v1, v2*v3, v0],
# representing a shape [v1] batch of [v2*v3, v0] matrices.
matrix = distribution_util.rotate_transpose(vec_flat, shift=-1)
return matrix
def _shape_tensor(self): # See _ops.TensorShape(self.shape) for explanation of steps s_shape = array_ops.shape(self._spectrum) batch_shape = s_shape[:-self.block_depth] trailing_dims = s_shape[-self.block_depth:] n = math_ops.reduce_prod(trailing_dims) n_x_n = [n, n] return array_ops.concat((batch_shape, n_x_n), 0)
def _shape_tensor(self, spectrum=None):
spectrum = self.spectrum if spectrum is None else spectrum
# See tensor_shape.TensorShape(self.shape) for explanation of steps
s_shape = prefer_static.shape(spectrum)
batch_shape = s_shape[:-self.block_depth]
trailing_dims = s_shape[-self.block_depth:]
n = math_ops.reduce_prod(trailing_dims)
n_x_n = [n, n]
return prefer_static.concat((batch_shape, n_x_n), 0)
def _determinant(self):
axis = [-(i + 1) for i in range(self.block_depth)]
det = math_ops.reduce_prod(self.spectrum, axis=axis)
return _ops.cast(det, self.dtype)
def _determinant(self): return math_ops.reduce_prod(self._get_diag(), axis=[-1])
