def func_body_augmented(iteration, chosen_ids):
# find a new facility location to add
# based on the clustering score and the NMI score
candidate_ids = array_ops.setdiff1d(all_ids, chosen_ids)[0]
new_chosen_idx = _find_loss_augmented_facility_idx(
pairwise_distances, labels, chosen_ids, candidate_ids,
margin_multiplier, margin_type)
chosen_ids = array_ops.concat([chosen_ids, [new_chosen_idx]], 0)
return iteration + 1, chosen_ids
def func_body_augmented(iteration, chosen_ids):
# find a new facility location to add
# based on the clustering score and the NMI score
candidate_ids = array_ops.setdiff1d(all_ids, chosen_ids)[0]
new_chosen_idx = _find_loss_augmented_facility_idx(pairwise_distances,
labels, chosen_ids,
candidate_ids,
margin_multiplier,
margin_type)
chosen_ids = array_ops.concat([chosen_ids, [new_chosen_idx]], 0)
return iteration + 1, chosen_ids
def _ProdGrad(op, grad):
"""Gradient for Prod."""
# The gradient can be expressed by dividing the product by each entry of the
# input tensor, but this approach can't deal with zeros in the input.
# Here, we avoid this problem by composing the output as a product of two
# cumprod operations.
input_shape = array_ops.shape(op.inputs[0])
# Reshape reduction indices for the case where the parameter is a scalar
reduction_indices = array_ops.reshape(op.inputs[1], [-1])
# Expand grad to full input shape
output_shape_kept_dims = math_ops.reduced_shape(input_shape, op.inputs[1])
tile_scaling = _safe_shape_div(input_shape, output_shape_kept_dims)
grad = array_ops.reshape(grad, output_shape_kept_dims)
grad = array_ops.tile(grad, tile_scaling)
# Pack all reduced dimensions into a single one, so we can perform the
# cumprod ops. If the reduction dims list is empty, it defaults to float32,
# so we need to cast here. We put all the shape-related ops on CPU to avoid
# copying back and forth, and since listdiff is CPU only.
with ops.device("/cpu:0"):
rank = array_ops.rank(op.inputs[0])
reduction_indices = (reduction_indices + rank) % rank
reduced = math_ops.cast(reduction_indices, dtypes.int32)
idx = math_ops.range(0, rank)
other, _ = array_ops.setdiff1d(idx, reduced)
perm = array_ops.concat([reduced, other], 0)
reduced_num = math_ops.reduce_prod(
array_ops.gather(input_shape, reduced))
other_num = math_ops.reduce_prod(array_ops.gather(input_shape, other))
permuted = array_ops.transpose(op.inputs[0], perm)
permuted_shape = array_ops.shape(permuted)
reshaped = array_ops.reshape(permuted, (reduced_num, other_num))
# Calculate product, leaving out the current entry
left = math_ops.cumprod(reshaped, axis=0, exclusive=True)
right = math_ops.cumprod(reshaped, axis=0, exclusive=True, reverse=True)
# For complex inputs, the gradient is in the conjugate direction.
y = array_ops.reshape(
math_ops.conj(left) * math_ops.conj(right), permuted_shape)
# Invert the transpose and reshape operations.
# Make sure to set the statically known shape information through a reshape.
out = grad * array_ops.transpose(y, array_ops.invert_permutation(perm))
return array_ops.reshape(out, input_shape), None
def _ProdGrad(op, grad):
"""Gradient for Prod."""
# The gradient can be expressed by dividing the product by each entry of the
# input tensor, but this approach can't deal with zeros in the input.
# Here, we avoid this problem by composing the output as a product of two
# cumprod operations.
input_shape = array_ops.shape(op.inputs[0])
# Reshape reduction indices for the case where the parameter is a scalar
reduction_indices = array_ops.reshape(op.inputs[1], [-1])
# Expand grad to full input shape
output_shape_kept_dims = math_ops.reduced_shape(input_shape, op.inputs[1])
tile_scaling = _safe_shape_div(input_shape, output_shape_kept_dims)
grad = array_ops.reshape(grad, output_shape_kept_dims)
grad = array_ops.tile(grad, tile_scaling)
# Pack all reduced dimensions into a single one, so we can perform the
# cumprod ops. If the reduction dims list is empty, it defaults to float32,
# so we need to cast here. We put all the shape-related ops on CPU to avoid
# copying back and forth, and since listdiff is CPU only.
with ops.device("/cpu:0"):
rank = array_ops.rank(op.inputs[0])
reduction_indices = (reduction_indices + rank) % rank
reduced = math_ops.cast(reduction_indices, dtypes.int32)
idx = math_ops.range(0, rank)
other, _ = array_ops.setdiff1d(idx, reduced)
perm = array_ops.concat([reduced, other], 0)
reduced_num = math_ops.reduce_prod(array_ops.gather(input_shape, reduced))
other_num = math_ops.reduce_prod(array_ops.gather(input_shape, other))
permuted = array_ops.transpose(op.inputs[0], perm)
permuted_shape = array_ops.shape(permuted)
reshaped = array_ops.reshape(permuted, (reduced_num, other_num))
# Calculate product, leaving out the current entry
left = math_ops.cumprod(reshaped, axis=0, exclusive=True)
right = math_ops.cumprod(reshaped, axis=0, exclusive=True, reverse=True)
# For complex inputs, the gradient is in the conjugate direction.
y = array_ops.reshape(math_ops.conj(left) * math_ops.conj(right),
permuted_shape)
# Invert the transpose and reshape operations.
# Make sure to set the statically known shape information through a reshape.
out = grad * array_ops.transpose(y, array_ops.invert_permutation(perm))
return array_ops.reshape(out, input_shape), None
def _embedding_lookup_with_distributed_aggregation(params,
ids,
partition_strategy="mod",
name=None,
max_norm=None,
weights=None,
idx=None,
segment_ids=None):
"""Lookup helper for embedding_lookup_sparse_with_distributed_aggregation."""
if params is None or params == []: # pylint: disable=g-explicit-bool-comparison
raise ValueError("Need at least one param")
if isinstance(params, variables.PartitionedVariable):
params = list(params) # Iterate to get the underlying Variables.
if not isinstance(params, list):
params = [params]
def maybe_normalize(x):
if max_norm is not None:
if x.get_shape().ndims is not None:
ndims = x.get_shape().ndims
else:
ndims = array_ops.size(array_ops.shape(x))
return clip_ops.clip_by_norm(x, max_norm, axes=list(range(1, ndims)))
return x
with ops.name_scope(name, "embedding_lookup_with_distributed_aggregation",
params + [ids]) as name:
np = len(params) # Number of partitions
# Preserve the resource variable status to avoid accidental dense reads.
if not any(
isinstance(p, resource_variable_ops.ResourceVariable) for p in params):
params = ops.convert_n_to_tensor_or_indexed_slices(params, name="params")
if np == 1:
with ops.colocate_with(params[0]):
ret = maybe_normalize(_do_gather(params[0], ids))
ignore_weights = weights is None
if not ignore_weights:
if weights.dtype != ret.dtype:
weights = math_ops.cast(weights, ret.dtype)
# Reshape to allow broadcast
ones = array_ops.fill(
array_ops.expand_dims(array_ops.rank(ret) - 1, 0), 1)
bcast_weights_shape = array_ops.concat(
[array_ops.shape(weights), ones], 0)
orig_weights_shape = weights.get_shape()
weights = array_ops.reshape(weights, bcast_weights_shape)
# Set weights shape after reshape
if ret.get_shape().ndims is not None:
weights.set_shape(
orig_weights_shape.concatenate(
[1 for _ in range(ret.get_shape().ndims - 1)]))
ret *= weights
return math_ops.segment_sum(ret, segment_ids, name=name)
else:
return math_ops.sparse_segment_sum(ret, idx, segment_ids, name=name)
else:
ids = ops.convert_to_tensor(ids, name="ids")
flat_ids = array_ops.reshape(ids, [-1])
original_indices = math_ops.range(array_ops.size(flat_ids))
# Create p_assignments and set new_ids depending on the strategy.
if partition_strategy == "mod":
p_assignments = flat_ids % np
new_ids = flat_ids // np
elif partition_strategy == "div":
# Compute num_total_ids as the sum of dim-0 of params, then assign to
# partitions based on a constant number of ids per partition. Optimize
# if we already know the full shape statically.
dim_0_size = params[0].get_shape()[0]
for p in xrange(1, np):
dim_0_size += params[p].get_shape()[0]
if dim_0_size.value:
num_total_ids = constant_op.constant(dim_0_size.value, flat_ids.dtype)
else:
dim_0_sizes = []
for p in xrange(np):
if params[p].get_shape()[0].value is not None:
dim_0_sizes.append(params[p].get_shape()[0].value)
else:
with ops.colocate_with(params[p]):
dim_0_sizes.append(array_ops.shape(params[p])[0])
num_total_ids = math_ops.reduce_sum(
math_ops.cast(array_ops.stack(dim_0_sizes), flat_ids.dtype))
ids_per_partition = num_total_ids // np
extras = num_total_ids % np
p_assignments = math_ops.maximum(flat_ids // (ids_per_partition + 1), (
flat_ids - extras) // ids_per_partition)
# Emulate a conditional using a boolean indicator tensor
is_in_first_extras_partitions = math_ops.cast(p_assignments < extras,
flat_ids.dtype)
new_ids = (is_in_first_extras_partitions * (flat_ids %
(ids_per_partition + 1)) +
(1 - is_in_first_extras_partitions) * (
(flat_ids - extras) % ids_per_partition))
else:
raise ValueError("Unrecognized partition strategy: " +
partition_strategy)
# Cast partition assignments to int32 for use in dynamic_partition.
# There really should not be more than 2^32 partitions.
p_assignments = math_ops.cast(p_assignments, dtypes.int32)
# Partition list of ids based on assignments into np separate lists
gather_ids = data_flow_ops.dynamic_partition(new_ids, p_assignments, np)
# Similarly, partition the original indices.
pindices = data_flow_ops.dynamic_partition(original_indices,
p_assignments, np)
# Do np separate lookups, finding embeddings for plist[p] in params[p]
partitioned_result = []
for p in xrange(np):
with ops.colocate_with(params[p]):
partitioned_result.append(_do_gather(params[p], gather_ids[p]))
ignore_weights = weights is None
if not ignore_weights:
# Partition weights according to pindices.
partitioned_weight = []
for p in xrange(np):
partitioned_weight.append(array_ops.gather(weights, pindices[p]))
# Reshape each partition result.
element_shape = params[0].get_shape()[1:]
for p in params[1:]:
element_shape = element_shape.merge_with(p.get_shape()[1:])
if element_shape.is_fully_defined():
for p in xrange(np):
with ops.colocate_with(params[p]):
partitioned_result[p] = array_ops.reshape(
partitioned_result[p],
array_ops.concat([array_ops.shape(pindices[p]), element_shape],
0))
else:
with ops.colocate_with(params[0]):
params_shape = array_ops.shape(params[0])
for p in xrange(np):
with ops.colocate_with(params[p]):
partitioned_result[p] = array_ops.reshape(
partitioned_result[p],
array_ops.concat([
array_ops.shape(pindices[p]), array_ops.slice(
params_shape, [1], [-1])
], 0))
# Normalize each partition result.
for p in xrange(np):
with ops.colocate_with(params[p]):
partitioned_result[p] = maybe_normalize(partitioned_result[p])
if not ignore_weights:
# Multiply each partition result with partition weights.
for p in xrange(np):
with ops.colocate_with(params[p]):
if partitioned_weight[p].dtype != partitioned_result[p].dtype:
partitioned_weight[p] = math_ops.cast(partitioned_weight[p],
partitioned_result[p].dtype)
# Reshape partition weights.
ones = array_ops.fill(
array_ops.expand_dims(
array_ops.rank(partitioned_result[p]) - 1, 0), 1)
bcast_weights_shape = array_ops.concat(
[array_ops.shape(partitioned_weight[p]), ones], 0)
orig_weights_shape = partitioned_weight[p].get_shape()
partitioned_weight[p] = array_ops.reshape(partitioned_weight[p],
bcast_weights_shape)
if partitioned_result[p].get_shape().ndims is not None:
partitioned_weight[p].set_shape(
orig_weights_shape.concatenate([
1
for _ in range(partitioned_result[p].get_shape().ndims -
1)
]))
partitioned_result[p] *= partitioned_weight[p]
partitioned_segment_ids = []
for p in xrange(np):
if not ignore_weights:
# Partition segment_ids according to pindices.
p_segment_ids = array_ops.gather(segment_ids, pindices[p])
# Number the p_segment_ids to meet segment_sum's requirements. Note
# that unique_p_segment_ids contains unique segment ids of this
# partition and these ids' order is unchanged.
unique_p_segment_ids, unique_p_segment_idx = array_ops.unique(
p_segment_ids)
partitioned_segment_ids.append(unique_p_segment_ids)
# segment_sum this partition's result.
with ops.colocate_with(params[p]):
partitioned_result[p] = math_ops.segment_sum(
partitioned_result[p], unique_p_segment_idx)
else:
# When ignore weights, we need to get indexs of elements in idx and
# segment_ids.
_, exclude_idx = array_ops.setdiff1d(idx, pindices[p])
all_idx = math_ops.range(array_ops.shape(idx)[0])
_, include_idx = array_ops.setdiff1d(all_idx, exclude_idx)
# Gather segment_ids and idx according to indexs.
p_segment_ids = array_ops.gather(segment_ids, include_idx)
p_idx = array_ops.gather(idx, include_idx)
# Number the p_segment_ids, same as ignore_weights case above.
unique_p_segment_ids, unique_p_segment_idx = array_ops.unique(
p_segment_ids)
_, unique_p_idx_idx = array_ops.unique(p_idx)
partitioned_segment_ids.append(unique_p_segment_ids)
with ops.colocate_with(params[p]):
partitioned_result[p] = math_ops.sparse_segment_sum(
partitioned_result[p], unique_p_idx_idx, unique_p_segment_idx)
# Concat each partition's segment_ids and result for final segment_sum.
concat_segment_ids = array_ops.concat(partitioned_segment_ids, 0)
concat_partitioned_result = array_ops.concat(partitioned_result, 0)
return math_ops.unsorted_segment_sum(
concat_partitioned_result,
concat_segment_ids,
math_ops.reduce_max(concat_segment_ids) + 1,
name=name)
def norm(tensor,
ord='euclidean',
axis=None,
keepdims=None,
name=None,
keep_dims=None):
r"""Computes the norm of vectors, matrices, and tensors.
This function can compute several different vector norms (the 1-norm, the
Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0) and
matrix norms (Frobenius, 1-norm, 2-norm and inf-norm).
Args:
tensor: `Tensor` of types `float32`, `float64`, `complex64`, `complex128`
ord: Order of the norm. Supported values are 'fro', 'euclidean',
`1`, `2`, `np.inf` and any positive real number yielding the corresponding
p-norm. Default is 'euclidean' which is equivalent to Frobenius norm if
`tensor` is a matrix and equivalent to 2-norm for vectors.
Some restrictions apply:
a) The Frobenius norm `fro` is not defined for vectors,
b) If axis is a 2-tuple (matrix norm), only 'euclidean', 'fro', `1`,
`2`, `np.inf` are supported.
See the description of `axis` on how to compute norms for a batch of
vectors or matrices stored in a tensor.
axis: If `axis` is `None` (the default), the input is considered a vector
and a single vector norm is computed over the entire set of values in the
tensor, i.e. `norm(tensor, ord=ord)` is equivalent to
`norm(reshape(tensor, [-1]), ord=ord)`.
If `axis` is a Python integer, the input is considered a batch of vectors,
and `axis` determines the axis in `tensor` over which to compute vector
norms.
If `axis` is a 2-tuple of Python integers it is considered a batch of
matrices and `axis` determines the axes in `tensor` over which to compute
a matrix norm.
Negative indices are supported. Example: If you are passing a tensor that
can be either a matrix or a batch of matrices at runtime, pass
`axis=[-2,-1]` instead of `axis=None` to make sure that matrix norms are
computed.
keepdims: If True, the axis indicated in `axis` are kept with size 1.
Otherwise, the dimensions in `axis` are removed from the output shape.
name: The name of the op.
keep_dims: Deprecated alias for `keepdims`.
Returns:
output: A `Tensor` of the same type as tensor, containing the vector or
matrix norms. If `keepdims` is True then the rank of output is equal to
the rank of `tensor`. Otherwise, if `axis` is none the output is a scalar,
if `axis` is an integer, the rank of `output` is one less than the rank
of `tensor`, if `axis` is a 2-tuple the rank of `output` is two less
than the rank of `tensor`.
Raises:
ValueError: If `ord` or `axis` is invalid.
@compatibility(numpy)
Mostly equivalent to numpy.linalg.norm.
Not supported: ord <= 0, 2-norm for matrices, nuclear norm.
Other differences:
a) If axis is `None`, treats the flattened `tensor` as a vector
regardless of rank.
b) Explicitly supports 'euclidean' norm as the default, including for
higher order tensors.
@end_compatibility
"""
keepdims = deprecation.deprecated_argument_lookup('keepdims', keepdims,
'keep_dims', keep_dims)
if keepdims is None:
keepdims = False
is_matrix_norm = ((isinstance(axis, tuple) or isinstance(axis, list))
and len(axis) == 2)
if is_matrix_norm:
axis = tuple(axis)
if (not isinstance(axis[0], int) or not isinstance(axis[1], int)
or axis[0] == axis[1]):
raise ValueError(
"'axis' must be None, an integer, or a tuple of 2 unique integers"
)
supported_matrix_norms = ['euclidean', 'fro', 1, 2, np.inf]
if ord not in supported_matrix_norms:
raise ValueError(
"'ord' must be a supported matrix norm in %s, got %s" %
(supported_matrix_norms, ord))
else:
if not (isinstance(axis, int) or axis is None):
raise ValueError(
"'axis' must be None, an integer, or a tuple of 2 unique integers"
)
supported_vector_norms = ['euclidean', 1, 2, np.inf]
if (not np.isreal(ord)
or ord <= 0) and ord not in supported_vector_norms:
raise ValueError("'ord' must be a supported vector norm, got %s" %
ord)
if axis is not None:
axis = (axis, )
with ops.name_scope(name, 'norm', [tensor]):
tensor = ops.convert_to_tensor(tensor)
if ord in ['fro', 'euclidean', 2, 2.0]:
if is_matrix_norm and ord in [2, 2.0]:
rank = array_ops.rank(tensor)
positive_axis = map_fn.map_fn(
lambda i: control_flow_ops.cond(i >= 0, lambda: i, lambda:
i + rank),
ops.convert_to_tensor(axis))
axes = math_ops.range(rank)
perm_before = array_ops.concat([
array_ops.setdiff1d(axes, positive_axis)[0], positive_axis
],
axis=0)
perm_after = map_fn.map_fn(
lambda i: math_ops.cast(array_ops.squeeze(
array_ops.where_v2(math_ops.equal(perm_before, i))),
dtype=dtypes.int32), axes)
permed = array_ops.transpose(tensor, perm=perm_before)
matrix_2_norm = array_ops.expand_dims(math_ops.reduce_max(
math_ops.abs(
gen_linalg_ops.svd(permed, compute_uv=False)[0]),
axis=-1,
keepdims=True),
axis=-1)
result = array_ops.transpose(matrix_2_norm, perm=perm_after)
else:
result = math_ops.sqrt(
math_ops.reduce_sum(tensor * math_ops.conj(tensor),
axis,
keepdims=True))
# TODO(rmlarsen): Replace with the following, once gradients are defined
# result = math_ops.reduce_euclidean_norm(tensor, axis, keepdims=True)
else:
result = math_ops.abs(tensor)
if ord == 1:
sum_axis = None if axis is None else axis[0]
result = math_ops.reduce_sum(result, sum_axis, keepdims=True)
if is_matrix_norm:
result = math_ops.reduce_max(result,
axis[-1],
keepdims=True)
elif ord == np.inf:
if is_matrix_norm:
result = math_ops.reduce_sum(result,
axis[1],
keepdims=True)
max_axis = None if axis is None else axis[0]
result = math_ops.reduce_max(result, max_axis, keepdims=True)
else:
# General p-norms (positive p only)
result = math_ops.pow(
math_ops.reduce_sum(math_ops.pow(result, ord),
axis,
keepdims=True), 1.0 / ord)
if not keepdims:
result = array_ops.squeeze(result, axis)
return result
def norm(tensor,
ord='euclidean',
axis=None,
keepdims=None,
name=None,
keep_dims=None):
r"""Computes the norm of vectors, matrices, and tensors.
This function can compute several different vector norms (the 1-norm, the
Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0) and
matrix norms (Frobenius, 1-norm, 2-norm and inf-norm).
Args:
tensor: `Tensor` of types `float32`, `float64`, `complex64`, `complex128`
ord: Order of the norm. Supported values are 'fro', 'euclidean',
`1`, `2`, `np.inf` and any positive real number yielding the corresponding
p-norm. Default is 'euclidean' which is equivalent to Frobenius norm if
`tensor` is a matrix and equivalent to 2-norm for vectors.
Some restrictions apply:
a) The Frobenius norm `fro` is not defined for vectors,
b) If axis is a 2-tuple (matrix norm), only 'euclidean', 'fro', `1`,
`2`, `np.inf` are supported.
See the description of `axis` on how to compute norms for a batch of
vectors or matrices stored in a tensor.
axis: If `axis` is `None` (the default), the input is considered a vector
and a single vector norm is computed over the entire set of values in the
tensor, i.e. `norm(tensor, ord=ord)` is equivalent to
`norm(reshape(tensor, [-1]), ord=ord)`.
If `axis` is a Python integer, the input is considered a batch of vectors,
and `axis` determines the axis in `tensor` over which to compute vector
norms.
If `axis` is a 2-tuple of Python integers it is considered a batch of
matrices and `axis` determines the axes in `tensor` over which to compute
a matrix norm.
Negative indices are supported. Example: If you are passing a tensor that
can be either a matrix or a batch of matrices at runtime, pass
`axis=[-2,-1]` instead of `axis=None` to make sure that matrix norms are
computed.
keepdims: If True, the axis indicated in `axis` are kept with size 1.
Otherwise, the dimensions in `axis` are removed from the output shape.
name: The name of the op.
keep_dims: Deprecated alias for `keepdims`.
Returns:
output: A `Tensor` of the same type as tensor, containing the vector or
matrix norms. If `keepdims` is True then the rank of output is equal to
the rank of `tensor`. Otherwise, if `axis` is none the output is a scalar,
if `axis` is an integer, the rank of `output` is one less than the rank
of `tensor`, if `axis` is a 2-tuple the rank of `output` is two less
than the rank of `tensor`.
Raises:
ValueError: If `ord` or `axis` is invalid.
@compatibility(numpy)
Mostly equivalent to numpy.linalg.norm.
Not supported: ord <= 0, 2-norm for matrices, nuclear norm.
Other differences:
a) If axis is `None`, treats the flattened `tensor` as a vector
regardless of rank.
b) Explicitly supports 'euclidean' norm as the default, including for
higher order tensors.
@end_compatibility
"""
keepdims = deprecation.deprecated_argument_lookup('keepdims', keepdims,
'keep_dims', keep_dims)
if keepdims is None:
keepdims = False
is_matrix_norm = ((isinstance(axis, tuple) or isinstance(axis, list)) and
len(axis) == 2)
if is_matrix_norm:
axis = tuple(axis)
if (not isinstance(axis[0], int) or not isinstance(axis[1], int) or
axis[0] == axis[1]):
raise ValueError(
"'axis' must be None, an integer, or a tuple of 2 unique integers")
supported_matrix_norms = ['euclidean', 'fro', 1, 2, np.inf]
if ord not in supported_matrix_norms:
raise ValueError("'ord' must be a supported matrix norm in %s, got %s" %
(supported_matrix_norms, ord))
else:
if not (isinstance(axis, int) or axis is None):
raise ValueError(
"'axis' must be None, an integer, or a tuple of 2 unique integers")
supported_vector_norms = ['euclidean', 1, 2, np.inf]
if (not np.isreal(ord) or ord <= 0) and ord not in supported_vector_norms:
raise ValueError("'ord' must be a supported vector norm, got %s" % ord)
if axis is not None:
axis = (axis,)
with ops.name_scope(name, 'norm', [tensor]):
tensor = ops.convert_to_tensor(tensor)
if ord in ['fro', 'euclidean', 2, 2.0]:
if is_matrix_norm and ord in [2, 2.0]:
rank = array_ops.rank(tensor)
positive_axis = map_fn.map_fn(
lambda i: control_flow_ops.cond(i >= 0, lambda: i, lambda: i + rank),
ops.convert_to_tensor(axis))
axes = math_ops.range(rank)
perm_before = array_ops.concat(
[array_ops.setdiff1d(axes, positive_axis)[0], positive_axis],
axis=0)
perm_after = map_fn.map_fn(
lambda i: math_ops.cast(
array_ops.squeeze(
array_ops.where(math_ops.equal(perm_before, i))),
dtype=dtypes.int32), axes)
permed = array_ops.transpose(tensor, perm=perm_before)
matrix_2_norm = array_ops.expand_dims(
math_ops.reduce_max(
math_ops.abs(gen_linalg_ops.svd(permed, compute_uv=False)[0]),
axis=-1,
keepdims=True),
axis=-1)
result = array_ops.transpose(matrix_2_norm, perm=perm_after)
else:
result = math_ops.sqrt(
math_ops.reduce_sum(
tensor * math_ops.conj(tensor), axis, keepdims=True))
else:
result = math_ops.abs(tensor)
if ord == 1:
sum_axis = None if axis is None else axis[0]
result = math_ops.reduce_sum(result, sum_axis, keepdims=True)
if is_matrix_norm:
result = math_ops.reduce_max(result, axis[-1], keepdims=True)
elif ord == np.inf:
if is_matrix_norm:
result = math_ops.reduce_sum(result, axis[1], keepdims=True)
max_axis = None if axis is None else axis[0]
result = math_ops.reduce_max(result, max_axis, keepdims=True)
else:
# General p-norms (positive p only)
result = math_ops.pow(
math_ops.reduce_sum(math_ops.pow(result, ord), axis, keepdims=True),
1.0 / ord)
if not keepdims:
result = array_ops.squeeze(result, axis)
return result
