Python源码示例：tensorflow.python.ops.state.is_variable_initialized()
示例1
def initialized_value(self):
"""Returns the value of the initialized variable.
You should use this instead of the variable itself to initialize another
variable with a value that depends on the value of this variable.
```python
# Initialize 'v' with a random tensor.
v = tf.Variable(tf.truncated_normal([10, 40]))
# Use `initialized_value` to guarantee that `v` has been
# initialized before its value is used to initialize `w`.
# The random values are picked only once.
w = tf.Variable(v.initialized_value() * 2.0)
```
Returns:
A `Tensor` holding the value of this variable after its initializer
has run.
"""
with ops.control_dependencies(None):
return control_flow_ops.cond(is_variable_initialized(self),
self.read_value,
lambda: self.initial_value)
示例2
def testUninitializedRefIdentity(self):
with self.test_session() as sess:
v = gen_state_ops._variable(shape=[1], dtype=tf.float32,
name="v", container="", shared_name="")
inited = state_ops.is_variable_initialized(v)
v_f, v_t = control_flow_ops.ref_switch(v, inited)
# Both v_f and v_t are uninitialized references. However, an actual use
# of the reference in the 'true' branch in the 'tf.identity' op will
# not 'fire' when v is uninitialized, so this is a valid construction.
# This test tests that _ref_identity allows uninitialized ref as input
# so that this construction is allowed.
v_f_op = gen_array_ops._ref_identity(v_f)
v_t_op = gen_array_ops._ref_identity(v_t)
with tf.control_dependencies([v_f_op]):
assign_v = tf.assign(v, [1.0])
with tf.control_dependencies([v_t_op]):
orig_v = tf.identity(v)
merged_op = control_flow_ops.merge([assign_v, orig_v])
self.assertAllEqual([1.0], sess.run(merged_op.output))
示例3
def initialized_value(self):
"""Returns the value of the initialized variable.
You should use this instead of the variable itself to initialize another
variable with a value that depends on the value of this variable.
```python
# Initialize 'v' with a random tensor.
v = tf.Variable(tf.truncated_normal([10, 40]))
# Use `initialized_value` to guarantee that `v` has been
# initialized before its value is used to initialize `w`.
# The random values are picked only once.
w = tf.Variable(v.initialized_value() * 2.0)
```
Returns:
A `Tensor` holding the value of this variable after its initializer
has run.
"""
with ops.control_dependencies(None):
return control_flow_ops.cond(is_variable_initialized(self),
self.read_value,
lambda: self.initial_value)
示例4
def is_variable_initialized(variable):
"""Tests if a variable has been initialized.
Args:
variable: A `Variable`.
Returns:
Returns a scalar boolean Tensor, `True` if the variable has been
initialized, `False` otherwise.
"""
return state_ops.is_variable_initialized(variable)
示例5
def report_uninitialized_variables(var_list=None,
name="report_uninitialized_variables"):
"""Adds ops to list the names of uninitialized variables.
When run, it returns a 1-D tensor containing the names of uninitialized
variables if there are any, or an empty array if there are none.
Args:
var_list: List of `Variable` objects to check. Defaults to the
value of `global_variables() + local_variables()`
name: Optional name of the `Operation`.
Returns:
A 1-D tensor containing names of the uninitialized variables, or an empty
1-D tensor if there are no variables or no uninitialized variables.
"""
if var_list is None:
var_list = global_variables() + local_variables()
# Backwards compatibility for old-style variables. TODO(touts): remove.
if not var_list:
var_list = []
for op in ops.get_default_graph().get_operations():
if op.type in ["Variable", "VariableV2", "AutoReloadVariable"]:
var_list.append(op.outputs[0])
with ops.name_scope(name):
if not var_list:
# Return an empty tensor so we only need to check for returned tensor
# size being 0 as an indication of model ready.
return array_ops.constant([], dtype=dtypes.string)
else:
# Get a 1-D boolean tensor listing whether each variable is initialized.
variables_mask = math_ops.logical_not(
array_ops.stack(
[state_ops.is_variable_initialized(v) for v in var_list]))
# Get a 1-D string tensor containing all the variable names.
variable_names_tensor = array_ops.constant([s.op.name for s in var_list])
# Return a 1-D tensor containing all the names of uninitialized variables.
return array_ops.boolean_mask(variable_names_tensor, variables_mask)
# pylint: disable=protected-access
示例6
def is_variable_initialized(variable):
"""Tests if a variable has been initialized.
Args:
variable: A `Variable`.
Returns:
Returns a scalar boolean Tensor, `True` if the variable has been
initialized, `False` otherwise.
"""
return state_ops.is_variable_initialized(variable)
示例7
def report_uninitialized_variables(var_list=None,
name="report_uninitialized_variables"):
"""Adds ops to list the names of uninitialized variables.
When run, it returns a 1-D tensor containing the names of uninitialized
variables if there are any, or an empty array if there are none.
Args:
var_list: List of `Variable` objects to check. Defaults to the
value of `global_variables() + local_variables()`
name: Optional name of the `Operation`.
Returns:
A 1-D tensor containing names of the uninitialized variables, or an empty
1-D tensor if there are no variables or no uninitialized variables.
"""
if var_list is None:
var_list = global_variables() + local_variables()
# Backwards compatibility for old-style variables. TODO(touts): remove.
if not var_list:
var_list = []
for op in ops.get_default_graph().get_operations():
if op.type in ["Variable", "VariableV2", "AutoReloadVariable"]:
var_list.append(op.outputs[0])
with ops.name_scope(name):
if not var_list:
# Return an empty tensor so we only need to check for returned tensor
# size being 0 as an indication of model ready.
return array_ops.constant([], dtype=dtypes.string)
else:
# Get a 1-D boolean tensor listing whether each variable is initialized.
variables_mask = math_ops.logical_not(
array_ops.stack(
[state_ops.is_variable_initialized(v) for v in var_list]))
# Get a 1-D string tensor containing all the variable names.
variable_names_tensor = array_ops.constant([s.op.name for s in var_list])
# Return a 1-D tensor containing all the names of uninitialized variables.
return array_ops.boolean_mask(variable_names_tensor, variables_mask)
# pylint: disable=protected-access
示例8
def is_variable_initialized(variable):
"""Tests if a variable has been initialized.
Args:
variable: A `Variable`.
Returns:
Returns a scalar boolean Tensor, `True` if the variable has been
initialized, `False` otherwise.
"""
return state_ops.is_variable_initialized(variable)
示例9
def report_uninitialized_variables(var_list=None,
name="report_uninitialized_variables"):
"""Adds ops to list the names of uninitialized variables.
When run, it returns a 1-D tensor containing the names of uninitialized
variables if there are any, or an empty array if there are none.
Args:
var_list: List of `Variable` objects to check. Defaults to the
value of `global_variables() + local_variables()`
name: Optional name of the `Operation`.
Returns:
A 1-D tensor containing names of the uninitialized variables, or an empty
1-D tensor if there are no variables or no uninitialized variables.
"""
if var_list is None:
var_list = global_variables() + local_variables()
# Backwards compatibility for old-style variables. TODO(touts): remove.
if not var_list:
var_list = []
for op in ops.get_default_graph().get_operations():
if op.type in ["Variable", "AutoReloadVariable"]:
var_list.append(op.outputs[0])
with ops.name_scope(name):
if not var_list:
# Return an empty tensor so we only need to check for returned tensor
# size being 0 as an indication of model ready.
return array_ops.constant([], dtype=dtypes.string)
else:
# Get a 1-D boolean tensor listing whether each variable is initialized.
variables_mask = math_ops.logical_not(array_ops.pack(
[state_ops.is_variable_initialized(v) for v in var_list]))
# Get a 1-D string tensor containing all the variable names.
variable_names_tensor = array_ops.constant([s.op.name for s in var_list])
# Return a 1-D tensor containing all the names of uninitialized variables.
return array_ops.boolean_mask(variable_names_tensor, variables_mask)
# pylint: disable=protected-access
示例10
def is_variable_initialized(variable):
"""Tests if a variable has been initialized.
Args:
variable: A `Variable`.
Returns:
Returns a scalar boolean Tensor, `True` if the variable has been
initialized, `False` otherwise.
"""
return state_ops.is_variable_initialized(variable)
示例11
def is_variable_initialized(variable):
"""Tests if a variable has been initialized.
Args:
variable: A `Variable`.
Returns:
Returns a scalar boolean Tensor, `True` if the variable has been
initialized, `False` otherwise.
"""
return state_ops.is_variable_initialized(variable)
示例12
def report_uninitialized_variables(var_list=None,
name="report_uninitialized_variables"):
"""Adds ops to list the names of uninitialized variables.
When run, it returns a 1-D tensor containing the names of uninitialized
variables if there are any, or an empty array if there are none.
Args:
var_list: List of `Variable` objects to check. Defaults to the
value of `global_variables() + local_variables()`
name: Optional name of the `Operation`.
Returns:
A 1-D tensor containing names of the uninitialized variables, or an empty
1-D tensor if there are no variables or no uninitialized variables.
"""
if var_list is None:
var_list = global_variables() + local_variables()
# Backwards compatibility for old-style variables. TODO(touts): remove.
if not var_list:
var_list = []
for op in ops.get_default_graph().get_operations():
if op.type in ["Variable", "VariableV2", "AutoReloadVariable"]:
var_list.append(op.outputs[0])
with ops.name_scope(name):
if not var_list:
# Return an empty tensor so we only need to check for returned tensor
# size being 0 as an indication of model ready.
return array_ops.constant([], dtype=dtypes.string)
else:
# Get a 1-D boolean tensor listing whether each variable is initialized.
variables_mask = math_ops.logical_not(
array_ops.stack(
[state_ops.is_variable_initialized(v) for v in var_list]))
# Get a 1-D string tensor containing all the variable names.
variable_names_tensor = array_ops.constant([s.op.name for s in var_list])
# Return a 1-D tensor containing all the names of uninitialized variables.
return array_ops.boolean_mask(variable_names_tensor, variables_mask)
# pylint: disable=protected-access
示例13
def report_uninitialized_variables(var_list=None,
name="report_uninitialized_variables"):
"""Adds ops to list the names of uninitialized variables.
When run, it returns a 1-D tensor containing the names of uninitialized
variables if there are any, or an empty array if there are none.
Args:
var_list: List of `Variable` objects to check. Defaults to the
value of `global_variables() + local_variables()`
name: Optional name of the `Operation`.
Returns:
A 1-D tensor containing names of the uninitialized variables, or an empty
1-D tensor if there are no variables or no uninitialized variables.
"""
if var_list is None:
var_list = global_variables() + local_variables()
# Backwards compatibility for old-style variables. TODO(touts): remove.
if not var_list:
var_list = []
for op in ops.get_default_graph().get_operations():
if op.type in ["Variable", "VariableV2", "AutoReloadVariable"]:
var_list.append(op.outputs[0])
with ops.name_scope(name):
# Run all operations on CPU
with ops.device("/cpu:0"):
if not var_list:
# Return an empty tensor so we only need to check for returned tensor
# size being 0 as an indication of model ready.
return array_ops.constant([], dtype=dtypes.string)
else:
# Get a 1-D boolean tensor listing whether each variable is initialized.
variables_mask = math_ops.logical_not(
array_ops.stack(
[state_ops.is_variable_initialized(v) for v in var_list]))
# Get a 1-D string tensor containing all the variable names.
variable_names_tensor = array_ops.constant(
[s.op.name for s in var_list])
# Return a 1-D tensor containing all the names of
# uninitialized variables.
return array_ops.boolean_mask(variable_names_tensor, variables_mask)
# pylint: disable=protected-access
