本文整理汇总了Python中tensorflow.python.platform.tf_logging.warning方法的典型用法代码示例。如果您正苦于以下问题:Python tf_logging.warning方法的具体用法?Python tf_logging.warning怎么用?Python tf_logging.warning使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.python.platform.tf_logging
的用法示例。
在下文中一共展示了tf_logging.warning方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _verify_model_fn_args
# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import warning [as 别名]
def _verify_model_fn_args(model_fn, params):
"""Verifies model fn arguments."""
args = set(_model_fn_args(model_fn))
if 'features' not in args:
raise ValueError('model_fn (%s) must include features argument.' % model_fn)
if 'labels' not in args:
raise ValueError('model_fn (%s) must include labels argument.' % model_fn)
if params is not None and 'params' not in args:
raise ValueError('model_fn (%s) does not include params argument, '
'but params (%s) is passed to Estimator.' % (model_fn,
params))
if params is None and 'params' in args:
logging.warning('Estimator\'s model_fn (%s) includes params '
'argument, but params are not passed to Estimator.',
model_fn)
if tf_inspect.ismethod(model_fn):
if 'self' in args:
args.remove('self')
non_valid_args = list(args - _VALID_MODEL_FN_ARGS)
if non_valid_args:
raise ValueError('model_fn (%s) has following not expected args: %s' %
(model_fn, non_valid_args))
示例2: _MakeShape
# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import warning [as 别名]
def _MakeShape(v, arg_name):
"""Convert v into a TensorShapeProto."""
# Args:
# v: A TensorShapeProto, a list of ints, or a tensor_shape.TensorShape.
# arg_name: String, for error messages.
# Returns:
# A TensorShapeProto.
if isinstance(v, tensor_shape_pb2.TensorShapeProto):
for d in v.dim:
if d.name:
logging.warning("Warning: TensorShapeProto with a named dimension: %s",
str(v))
break
return v
return tensor_shape.as_shape(v).as_proto()
示例3: strip_name_scope
# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import warning [as 别名]
def strip_name_scope(name, export_scope):
"""Removes name scope from a name.
Args:
name: A `string` name.
export_scope: Optional `string`. Name scope to remove.
Returns:
Name with name scope removed, or the original name if export_scope
is None.
"""
if export_scope:
try:
# Strips export_scope/, export_scope///,
# ^export_scope/, loc:@export_scope/.
str_to_replace = r"([\^]|loc:@|^)" + export_scope + r"[\/]+(.*)"
return re.sub(str_to_replace, r"\1\2", compat.as_str(name), count=1)
except TypeError as e:
# If the name is not of a type we can process, simply return it.
logging.warning(e)
return name
else:
return name
示例4: prepend_name_scope
# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import warning [as 别名]
def prepend_name_scope(name, import_scope):
"""Prepends name scope to a name.
Args:
name: A `string` name.
import_scope: Optional `string`. Name scope to add.
Returns:
Name with name scope added, or the original name if import_scope
is None.
"""
if import_scope:
try:
str_to_replace = r"([\^]|loc:@|^)(.*)"
return re.sub(str_to_replace, r"\1" + import_scope + r"/\2",
compat.as_str(name))
except TypeError as e:
# If the name is not of a type we can process, simply return it.
logging.warning(e)
return name
else:
return name
# pylint: disable=g-doc-return-or-yield
示例5: _extract_metric_update_ops
# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import warning [as 别名]
def _extract_metric_update_ops(self, eval_dict):
"""Separate update operations from metric value operations."""
update_ops = []
value_ops = {}
for name, metric_ops in six.iteritems(eval_dict):
if isinstance(metric_ops, (list, tuple)):
if len(metric_ops) == 2:
value_ops[name] = metric_ops[0]
update_ops.append(metric_ops[1])
else:
logging.warning(
'Ignoring metric {}. It returned a list|tuple with len {}, '
'expected 2'.format(name, len(metric_ops)))
value_ops[name] = metric_ops
else:
value_ops[name] = metric_ops
if update_ops:
update_ops = control_flow_ops.group(*update_ops)
else:
update_ops = None
return update_ops, value_ops
示例6: _clip_sparse
# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import warning [as 别名]
def _clip_sparse(self, grad, var):
assert isinstance(grad, ops.IndexedSlices)
clip_dims = self._vars_to_clip_dims[var]
if 0 in clip_dims:
logging.warning("Clipping norm across dims %s for %s is inefficient "
"when including sparse dimension 0.", clip_dims,
var.op.name)
return self._clip_dense(var)
with ops.colocate_with(var):
var_subset = array_ops.gather(var, grad.indices)
with self._maybe_colocate_with(var):
normalized_var_subset = clip_ops.clip_by_norm(
var_subset, self._max_norm, clip_dims)
delta = ops.IndexedSlices(
var_subset - normalized_var_subset, grad.indices, grad.dense_shape)
with ops.colocate_with(var):
return var.scatter_sub(delta, use_locking=self._use_locking)
示例7: _serve_runs
# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import warning [as 别名]
def _serve_runs(self, unused_query_params):
"""Return a JSON object about runs and tags.
Returns a mapping from runs to tagType to list of tags for that run.
Returns:
{runName: {images: [tag1, tag2, tag3],
audio: [tag4, tag5, tag6],
scalars: [tagA, tagB, tagC],
histograms: [tagX, tagY, tagZ],
firstEventTimestamp: 123456.789}}
"""
runs = self._multiplexer.Runs()
for run_name, run_data in runs.items():
try:
run_data['firstEventTimestamp'] = self._multiplexer.FirstEventTimestamp(
run_name)
except ValueError:
logging.warning('Unable to get first event timestamp for run %s',
run_name)
run_data['firstEventTimestamp'] = None
self.respond(runs, 'application/json')
示例8: _load_library
# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import warning [as 别名]
def _load_library(name, op_list=None):
"""Loads a .so file containing the specified operators.
Args:
name: The name of the .so file to load.
op_list: A list of names of operators that the library should have. If None
then the .so file's contents will not be verified.
Raises:
NameError if one of the required ops is missing.
"""
try:
filename = resource_loader.get_path_to_datafile(name)
library = load_library.load_op_library(filename)
for expected_op in (op_list or []):
for lib_op in library.OP_LIST.op:
if lib_op.name == expected_op:
break
else:
raise NameError('Could not find operator %s in dynamic library %s' %
(expected_op, name))
except errors.NotFoundError:
logging.warning('%s file could not be loaded.', name)
示例9: _validate_input_pipeline
# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import warning [as 别名]
def _validate_input_pipeline(self):
"""Validates the input pipeline.
Perform some sanity checks to log user friendly information. We should
error out to give users better error message. But, if
_WRAP_INPUT_FN_INTO_WHILE_LOOP is False (legacy behavior), we cannot break
user code, so, log a warning.
Raises:
RuntimeError: If the validation failed.
"""
if ops.get_default_graph().get_collection(ops.GraphKeys.QUEUE_RUNNERS):
err_msg = ('Input pipeline contains one or more QueueRunners. '
'It could be slow and not scalable. Please consider '
'converting your input pipeline to use `tf.data` instead (see '
'https://www.tensorflow.org/guide/datasets for '
'instructions.')
if _WRAP_INPUT_FN_INTO_WHILE_LOOP:
raise RuntimeError(err_msg)
else:
logging.warn(err_msg)
示例10: create_cpu_hostcall
# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import warning [as 别名]
def create_cpu_hostcall(host_calls):
"""Runs on the host_call on CPU instead of TPU when use_tpu=False."""
_OutfeedHostCall.validate(host_calls)
ret = {}
for name, host_call in host_calls.items():
host_fn, tensors = host_call
if isinstance(tensors, (tuple, list)):
ret[name] = host_fn(*tensors)
else:
# Must be dict.
try:
ret[name] = host_fn(**tensors)
except TypeError as e:
logging.warning(
'Exception while calling %s: %s. It is likely the tensors '
'(%s[1]) do not match the '
'function\'s arguments', name, e, name)
raise e
return ret
示例11: _default_global_step_tensor
# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import warning [as 别名]
def _default_global_step_tensor(self):
"""Returns the global_step from the default graph.
Returns:
The global step `Tensor` or `None`.
"""
try:
gs = ops.get_default_graph().get_tensor_by_name("global_step:0")
if gs.dtype.base_dtype in [dtypes.int32, dtypes.int64]:
return gs
else:
logging.warning("Found 'global_step' is not an int type: %s", gs.dtype)
return None
except KeyError:
return None
示例12: _get_features_from_input_fn
# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import warning [as 别名]
def _get_features_from_input_fn(self, input_fn):
result = input_fn()
if not ops.get_default_graph().get_collection(ops.GraphKeys.QUEUE_RUNNERS):
logging.warning('Input graph does not contain a QueueRunner. '
'That means predict yields forever. '
'This is probably a mistake.')
if isinstance(result, (list, tuple)):
return result[0]
return result
示例13: after_run
# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import warning [as 别名]
def after_run(self, run_context, run_values):
if np.isnan(run_values.results):
failure_message = "Model diverged with loss = NaN."
if self._fail_on_nan_loss:
logging.error(failure_message)
raise NanLossDuringTrainingError
else:
logging.warning(failure_message)
# We don't raise an error but we request stop without an exception.
run_context.request_stop()
示例14: _ready
# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import warning [as 别名]
def _ready(op, sess, msg):
"""Checks if the model is ready or not, as determined by op.
Args:
op: An op, either _ready_op or _ready_for_local_init_op, which defines the
readiness of the model.
sess: A `Session`.
msg: A message to log to warning if not ready
Returns:
A tuple (is_ready, msg), where is_ready is True if ready and False
otherwise, and msg is `None` if the model is ready, a `String` with the
reason why it is not ready otherwise.
"""
if op is None:
return True, None
else:
try:
ready_value = sess.run(op)
# The model is considered ready if ready_op returns an empty 1-D tensor.
# Also compare to `None` and dtype being int32 for backward
# compatibility.
if (ready_value is None or ready_value.dtype == np.int32 or
ready_value.size == 0):
return True, None
else:
# TODO(sherrym): If a custom ready_op returns other types of tensor,
# or strings other than variable names, this message could be
# confusing.
non_initialized_varnames = ", ".join(
[i.decode("utf-8") for i in ready_value])
return False, "Variables not initialized: " + non_initialized_varnames
except errors.FailedPreconditionError as e:
if "uninitialized" not in str(e):
logging.warning("%s : error [%s]", msg, str(e))
raise e
return False, str(e)
示例15: VARIABLES
# 需要导入模块: from tensorflow.python.platform import tf_logging [as 别名]
# 或者: from tensorflow.python.platform.tf_logging import warning [as 别名]
def VARIABLES(cls): # pylint: disable=no-self-argument
logging.warning("VARIABLES collection name is deprecated, "
"please use GLOBAL_VARIABLES instead; "
"VARIABLES will be removed after 2017-03-02.")
return cls.GLOBAL_VARIABLES