def _streaming_false_positives(predictions, labels, weights=None,
metrics_collections=None,
updates_collections=None,
name=None):
"""Sum the weights of false positives.
If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
Args:
predictions: The predicted values, a `bool` `Tensor` of arbitrary
dimensions.
labels: The ground truth values, a `bool` `Tensor` whose dimensions must
match `predictions`.
weights: An optional `Tensor` whose shape is broadcastable to `predictions`.
metrics_collections: An optional list of collections that the metric
value variable should be added to.
updates_collections: An optional list of collections that the metric update
ops should be added to.
name: An optional variable_scope name.
Returns:
value_tensor: A tensor representing the current value of the metric.
update_op: An operation that accumulates the error from a batch of data.
Raises:
ValueError: If `predictions` and `labels` have mismatched shapes, or if
`weights` is not `None` and its shape doesn't match `predictions`, or if
either `metrics_collections` or `updates_collections` are not a list or
tuple.
"""
with variable_scope.variable_scope(
name, 'false_positives', [predictions, labels]):
predictions.get_shape().assert_is_compatible_with(labels.get_shape())
is_false_positive = math_ops.logical_and(math_ops.equal(labels, 0),
math_ops.equal(predictions, 1))
return _count_condition(is_false_positive, weights, metrics_collections,
updates_collections)
python类equal()的实例源码
def _streaming_false_negatives(predictions, labels, weights=None,
metrics_collections=None,
updates_collections=None,
name=None):
"""Computes the total number of false positives.
If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
Args:
predictions: The predicted values, a `bool` `Tensor` of arbitrary
dimensions.
labels: The ground truth values, a `bool` `Tensor` whose dimensions must
match `predictions`.
weights: An optional `Tensor` whose shape is broadcastable to `predictions`.
metrics_collections: An optional list of collections that the metric
value variable should be added to.
updates_collections: An optional list of collections that the metric update
ops should be added to.
name: An optional variable_scope name.
Returns:
value_tensor: A tensor representing the current value of the metric.
update_op: An operation that accumulates the error from a batch of data.
Raises:
ValueError: If `weights` is not `None` and its shape doesn't match `values`,
or if either `metrics_collections` or `updates_collections` are not a list
or tuple.
"""
with variable_scope.variable_scope(
name, 'false_negatives', [predictions, labels]):
predictions.get_shape().assert_is_compatible_with(labels.get_shape())
is_false_negative = math_ops.logical_and(math_ops.equal(labels, 1),
math_ops.equal(predictions, 0))
return _count_condition(is_false_negative, weights, metrics_collections,
updates_collections)
def _select_class_id(ids, selected_id):
"""Filter all but `selected_id` out of `ids`.
Args:
ids: `int64` `Tensor` or `SparseTensor` of IDs.
selected_id: Int id to select.
Returns:
`SparseTensor` of same dimensions as `ids`, except for the last dimension,
which might be smaller. This contains only the entries equal to
`selected_id`.
"""
if isinstance(ids, (ops.SparseTensor, ops.SparseTensorValue)):
return sparse_ops.sparse_retain(
ids, math_ops.equal(ids.values, selected_id))
# TODO(ptucker): Make this more efficient, maybe add a sparse version of
# tf.equal and tf.reduce_any?
# Shape of filled IDs is the same as `ids` with the last dim collapsed to 1.
ids_shape = array_ops.shape(ids)
ids_last_dim = array_ops.size(ids_shape) - 1
filled_selected_id_shape = math_ops.reduced_shape(
ids_shape, array_ops.reshape(ids_last_dim, [1]))
# Intersect `ids` with the selected ID.
filled_selected_id = array_ops.fill(
filled_selected_id_shape, math_ops.to_int64(selected_id))
return set_ops.set_intersection(filled_selected_id, ids)
def _check_labels_and_scores(boolean_labels, scores, check_shape):
"""Check the rank of labels/scores, return tensor versions."""
with ops.name_scope('_check_labels_and_scores',
values=[boolean_labels, scores]):
boolean_labels = ops.convert_to_tensor(boolean_labels,
name='boolean_labels')
scores = ops.convert_to_tensor(scores, name='scores')
if boolean_labels.dtype != dtypes.bool:
raise ValueError(
'Argument boolean_labels should have dtype bool. Found: %s',
boolean_labels.dtype)
if check_shape:
labels_rank_1 = control_flow_ops.Assert(
math_ops.equal(1, array_ops.rank(boolean_labels)),
['Argument boolean_labels should have rank 1. Found: ',
boolean_labels.name, array_ops.shape(boolean_labels)])
scores_rank_1 = control_flow_ops.Assert(
math_ops.equal(1, array_ops.rank(scores)),
['Argument scores should have rank 1. Found: ', scores.name,
array_ops.shape(scores)])
with ops.control_dependencies([labels_rank_1, scores_rank_1]):
return boolean_labels, scores
else:
return boolean_labels, scores
def accuracy(predictions, labels, weights=None):
"""Computes the percentage of times that predictions matches labels.
Args:
predictions: the predicted values, a `Tensor` whose dtype and shape
matches 'labels'.
labels: the ground truth values, a `Tensor` of any shape and
bool, integer, or string dtype.
weights: None or `Tensor` of float values to reweight the accuracy.
Returns:
Accuracy `Tensor`.
Raises:
ValueError: if dtypes don't match or
if dtype is not bool, integer, or string.
"""
if not (labels.dtype.is_integer or
labels.dtype in (dtypes.bool, dtypes.string)):
raise ValueError(
'Labels should have bool, integer, or string dtype, not %r' %
labels.dtype)
if not labels.dtype.is_compatible_with(predictions.dtype):
raise ValueError('Dtypes of predictions and labels should match. '
'Given: predictions (%r) and labels (%r)' %
(predictions.dtype, labels.dtype))
with ops.name_scope('accuracy', values=[predictions, labels]):
is_correct = math_ops.cast(
math_ops.equal(predictions, labels), dtypes.float32)
if weights is not None:
is_correct = math_ops.mul(is_correct, weights)
return math_ops.reduce_mean(is_correct)
def _introspect_ndims(self, ndims):
"""Helper to establish some properties of input ndims args."""
if self._is_all_constant_helper(ndims):
return (tensor_util.constant_value(ndims),
tensor_util.constant_value(ndims) == 0)
return None, math_ops.equal(ndims, 0)
def _expand_sample_shape(self, sample_shape):
"""Helper to `sample` which ensures sample_shape is 1D."""
sample_shape_static_val = tensor_util.constant_value(sample_shape)
ndims = sample_shape.get_shape().ndims
if sample_shape_static_val is None:
if ndims is None or not sample_shape.get_shape().is_fully_defined():
ndims = array_ops.rank(sample_shape)
expanded_shape = distribution_util.pick_vector(
math_ops.equal(ndims, 0),
np.array((1,), dtype=dtypes.int32.as_numpy_dtype()),
array_ops.shape(sample_shape))
sample_shape = array_ops.reshape(sample_shape, expanded_shape)
total = math_ops.reduce_prod(sample_shape) # reduce_prod([]) == 1
else:
if ndims is None:
raise ValueError(
"Shouldn't be here; ndims cannot be none when we have a "
"tf.constant shape.")
if ndims == 0:
sample_shape_static_val = np.reshape(sample_shape_static_val, [1])
sample_shape = ops.convert_to_tensor(
sample_shape_static_val,
dtype=dtypes.int32,
name="sample_shape")
total = np.prod(sample_shape_static_val,
dtype=dtypes.int32.as_numpy_dtype())
return sample_shape, total
def _all_equal(tensor0, tensor1):
with ops.name_scope('all_equal', values=[tensor0, tensor1]) as scope:
return math_ops.reduce_all(
math_ops.equal(tensor0, tensor1, name='equal'), name=scope)
def _is_rank(expected_rank, actual_tensor):
"""Returns whether actual_tensor's rank is expected_rank.
Args:
expected_rank: Integer defining the expected rank, or tensor of same.
actual_tensor: Tensor to test.
Returns:
New tensor.
"""
with ops.name_scope('is_rank', values=[actual_tensor]) as scope:
expected = ops.convert_to_tensor(expected_rank, name='expected')
actual = array_ops.rank(actual_tensor, name='actual')
return math_ops.equal(expected, actual, name=scope)
def _check_multiple_of(value, multiple_of):
"""Checks that value `value` is a non-zero multiple of `multiple_of`.
Args:
value: an int32 scalar Tensor.
multiple_of: an int or int32 scalar Tensor.
Returns:
new_value: an int32 scalar Tensor matching `value`, but which includes an
assertion that `value` is a multiple of `multiple_of`.
"""
assert isinstance(value, ops.Tensor)
with ops.control_dependencies([
control_flow_ops.Assert(
math_ops.logical_and(
math_ops.equal(math_ops.mod(value, multiple_of), 0),
math_ops.not_equal(value, 0)),
[string_ops.string_join(
["Tensor %s should be a multiple of: " % value.name,
string_ops.as_string(multiple_of),
", but saw value: ",
string_ops.as_string(value),
". Consider setting pad=True."])])]):
new_value = array_ops.identity(
value, name="multiple_of_checked")
return new_value
def sequence_count(self):
"""An int32 vector, length `batch_size`: the sequence count of each entry.
When an input is split up, the number of splits is equal to:
`padded_length / num_unroll`. This is the sequence_count.
Returns:
An int32 vector `Tensor`.
"""
return self._state_saver._received_sequence_count
def _apply_transform(self, input_tensors, **kwargs):
"""Applies the transformation to the `transform_input`.
Args:
input_tensors: a list of Tensors representing the input to
the Transform.
**kwargs: Additional keyword arguments, unused here.
Returns:
A namedtuple of Tensors representing the transformed output.
"""
d = input_tensors[0]
if self.strip_value is np.nan:
strip_hot = math_ops.is_nan(d)
else:
strip_hot = math_ops.equal(d,
array_ops.constant([self.strip_value],
dtype=d.dtype))
keep_hot = math_ops.logical_not(strip_hot)
length = array_ops.reshape(array_ops.shape(d), [])
indices = array_ops.boolean_mask(math_ops.range(length), keep_hot)
values = array_ops.boolean_mask(d, keep_hot)
sparse_indices = array_ops.reshape(
math_ops.cast(indices, dtypes.int64), [-1, 1])
shape = math_ops.cast(array_ops.shape(d), dtypes.int64)
# pylint: disable=not-callable
return self.return_type(
sparse_tensor.SparseTensor(sparse_indices, values, shape))
def sparse_boolean_mask(sparse_tensor, mask, name="sparse_boolean_mask"):
"""Boolean mask for `SparseTensor`s.
Args:
sparse_tensor: a `SparseTensor`.
mask: a 1D boolean dense`Tensor` whose length is equal to the 0th dimension
of `sparse_tensor`.
name: optional name for this operation.
Returns:
A `SparseTensor` that contains row `k` of `sparse_tensor` iff `mask[k]` is
`True`.
"""
# TODO(jamieas): consider mask dimension > 1 for symmetry with `boolean_mask`.
with ops.name_scope(name, values=[sparse_tensor, mask]):
mask = ops.convert_to_tensor(mask)
mask_rows = array_ops.where(mask)
first_indices = array_ops.squeeze(array_ops.slice(sparse_tensor.indices,
[0, 0], [-1, 1]))
# Identify indices corresponding to the rows identified by mask_rows.
sparse_entry_matches = functional_ops.map_fn(
lambda x: math_ops.equal(first_indices, x),
mask_rows,
dtype=dtypes.bool)
# Combine the rows of index_matches to form a mask for the sparse indices
# and values.
to_retain = array_ops.reshape(
functional_ops.foldl(math_ops.logical_or, sparse_entry_matches), [-1])
return sparse_ops.sparse_retain(sparse_tensor, to_retain)
def get_stats(self, session):
num_nodes = self.variables.end_of_tree.eval(session=session) - 1
num_leaves = array_ops.where(
math_ops.equal(array_ops.squeeze(array_ops.slice(
self.variables.tree, [0, 0], [-1, 1])), constants.LEAF_NODE)
).eval(session=session).shape[0]
return TreeStats(num_nodes, num_leaves)
def _streaming_true_positives(predictions, labels, weights=None,
metrics_collections=None,
updates_collections=None,
name=None):
"""Sum the weights of true_positives.
If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
Args:
predictions: The predicted values, a `bool` `Tensor` of arbitrary
dimensions.
labels: The ground truth values, a `bool` `Tensor` whose dimensions must
match `predictions`.
weights: An optional `Tensor` whose shape is broadcastable to `predictions`.
metrics_collections: An optional list of collections that the metric
value variable should be added to.
updates_collections: An optional list of collections that the metric update
ops should be added to.
name: An optional variable_scope name.
Returns:
value_tensor: A tensor representing the current value of the metric.
update_op: An operation that accumulates the error from a batch of data.
Raises:
ValueError: If `predictions` and `labels` have mismatched shapes, or if
`weights` is not `None` and its shape doesn't match `predictions`, or if
either `metrics_collections` or `updates_collections` are not a list or
tuple.
"""
with variable_scope.variable_scope(
name, 'true_positives', [predictions, labels]):
predictions.get_shape().assert_is_compatible_with(labels.get_shape())
is_true_positive = math_ops.logical_and(math_ops.equal(labels, 1),
math_ops.equal(predictions, 1))
return _count_condition(is_true_positive, weights, metrics_collections,
updates_collections)
def _streaming_false_positives(predictions, labels, weights=None,
metrics_collections=None,
updates_collections=None,
name=None):
"""Sum the weights of false positives.
If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
Args:
predictions: The predicted values, a `bool` `Tensor` of arbitrary
dimensions.
labels: The ground truth values, a `bool` `Tensor` whose dimensions must
match `predictions`.
weights: An optional `Tensor` whose shape is broadcastable to `predictions`.
metrics_collections: An optional list of collections that the metric
value variable should be added to.
updates_collections: An optional list of collections that the metric update
ops should be added to.
name: An optional variable_scope name.
Returns:
value_tensor: A tensor representing the current value of the metric.
update_op: An operation that accumulates the error from a batch of data.
Raises:
ValueError: If `predictions` and `labels` have mismatched shapes, or if
`weights` is not `None` and its shape doesn't match `predictions`, or if
either `metrics_collections` or `updates_collections` are not a list or
tuple.
"""
with variable_scope.variable_scope(
name, 'false_positives', [predictions, labels]):
predictions.get_shape().assert_is_compatible_with(labels.get_shape())
is_false_positive = math_ops.logical_and(math_ops.equal(labels, 0),
math_ops.equal(predictions, 1))
return _count_condition(is_false_positive, weights, metrics_collections,
updates_collections)
def _select_class_id(ids, selected_id):
"""Filter all but `selected_id` out of `ids`.
Args:
ids: `int64` `Tensor` or `SparseTensor` of IDs.
selected_id: Int id to select.
Returns:
`SparseTensor` of same dimensions as `ids`. This contains only the entries
equal to `selected_id`.
"""
if isinstance(
ids, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
return sparse_ops.sparse_retain(
ids, math_ops.equal(ids.values, selected_id))
# TODO(ptucker): Make this more efficient, maybe add a sparse version of
# tf.equal and tf.reduce_any?
# Shape of filled IDs is the same as `ids` with the last dim collapsed to 1.
ids_shape = array_ops.shape(ids, out_type=dtypes.int64)
ids_last_dim = array_ops.size(ids_shape) - 1
filled_selected_id_shape = math_ops.reduced_shape(
ids_shape, array_ops.reshape(ids_last_dim, [1]))
# Intersect `ids` with the selected ID.
filled_selected_id = array_ops.fill(
filled_selected_id_shape, math_ops.to_int64(selected_id))
result = set_ops.set_intersection(filled_selected_id, ids)
return sparse_tensor.SparseTensor(
indices=result.indices, values=result.values, shape=ids_shape)
def _check_labels_and_scores(boolean_labels, scores, check_shape):
"""Check the rank of labels/scores, return tensor versions."""
with ops.name_scope('_check_labels_and_scores',
values=[boolean_labels, scores]):
boolean_labels = ops.convert_to_tensor(boolean_labels,
name='boolean_labels')
scores = ops.convert_to_tensor(scores, name='scores')
if boolean_labels.dtype != dtypes.bool:
raise ValueError(
'Argument boolean_labels should have dtype bool. Found: %s',
boolean_labels.dtype)
if check_shape:
labels_rank_1 = control_flow_ops.Assert(
math_ops.equal(1, array_ops.rank(boolean_labels)),
['Argument boolean_labels should have rank 1. Found: ',
boolean_labels.name, array_ops.shape(boolean_labels)])
scores_rank_1 = control_flow_ops.Assert(
math_ops.equal(1, array_ops.rank(scores)),
['Argument scores should have rank 1. Found: ', scores.name,
array_ops.shape(scores)])
with ops.control_dependencies([labels_rank_1, scores_rank_1]):
return boolean_labels, scores
else:
return boolean_labels, scores
def _introspect_ndims(self, ndims):
"""Helper to establish some properties of input ndims args."""
if self._is_all_constant_helper(ndims):
return (tensor_util.constant_value(ndims),
tensor_util.constant_value(ndims) == 0)
return None, math_ops.equal(ndims, 0)
def _expand_sample_shape(self, sample_shape):
"""Helper to `sample` which ensures sample_shape is 1D."""
sample_shape_static_val = tensor_util.constant_value(sample_shape)
ndims = sample_shape.get_shape().ndims
if sample_shape_static_val is None:
if ndims is None or not sample_shape.get_shape().is_fully_defined():
ndims = array_ops.rank(sample_shape)
expanded_shape = distribution_util.pick_vector(
math_ops.equal(ndims, 0),
np.array((1,), dtype=dtypes.int32.as_numpy_dtype()),
array_ops.shape(sample_shape))
sample_shape = array_ops.reshape(sample_shape, expanded_shape)
total = math_ops.reduce_prod(sample_shape) # reduce_prod([]) == 1
else:
if ndims is None:
raise ValueError(
"Shouldn't be here; ndims cannot be none when we have a "
"tf.constant shape.")
if ndims == 0:
sample_shape_static_val = np.reshape(sample_shape_static_val, [1])
sample_shape = ops.convert_to_tensor(
sample_shape_static_val,
dtype=dtypes.int32,
name="sample_shape")
total = np.prod(sample_shape_static_val,
dtype=dtypes.int32.as_numpy_dtype())
return sample_shape, total
