我们从Python开源项目中，提取了以下10个代码示例，用于说明如何使用tensorflow.is_variable_initialized()。
def initialize_uninitialized_global_variables(sess): """ Only initializes the variables of a TensorFlow session that were not already initialized. :param sess: the TensorFlow session :return: """ # List all global variables global_vars = tf.global_variables() # Find initialized status for all variables is_var_init = [tf.is_variable_initialized(var) for var in global_vars] is_initialized = sess.run(is_var_init) # List all variables that were not initialized previously not_initialized_vars = [var for (var, init) in zip(global_vars, is_initialized) if not init] # Initialize all uninitialized variables found, if any if len(not_initialized_vars): sess.run(tf.variables_initializer(not_initialized_vars))
def initialize_uninitialized_variables(sess): """ Only initialize the weights that have not yet been initialized by other means, such as importing a metagraph and a checkpoint. It's useful when extending an existing model. """ uninit_vars = [] uninit_tensors = [] for var in tf.global_variables(): uninit_vars.append(var) uninit_tensors.append(tf.is_variable_initialized(var)) uninit_bools = sess.run(uninit_tensors) uninit = zip(uninit_bools, uninit_vars) uninit = [var for init, var in uninit if not init] sess.run(tf.variables_initializer(uninit)) #-------------------------------------------------------------------------------
def initialize_uninitialized(sess): global_vars = tf.global_variables() is_not_initialized = sess.run([tf.is_variable_initialized(var) for var in global_vars]) not_initialized_vars = [v for (v, f) in zip(global_vars, is_not_initialized) if not f] print([str(i.name) for i in not_initialized_vars]) # only for testing if len(not_initialized_vars): sess.run(tf.variables_initializer(not_initialized_vars))
def _init_uninitialized(sess): """Initializes all uninitialized variables and returns them as a list.""" variables = tf.global_variables() if not variables: return [] # sess.run() barfs on empty list is_initialized = sess.run([tf.is_variable_initialized(v) for v in variables]) needs_init = [v for v, i in zip(variables, is_initialized) if not i] if not needs_init: return [] sess.run(tf.variables_initializer(needs_init)) return needs_init
def testIsVariableInitialized(self): for use_gpu in [True, False]: with self.test_session(use_gpu=use_gpu): v0 = state_ops.variable_op([1, 2], tf.complex64) self.assertEqual(False, tf.is_variable_initialized(v0).eval()) tf.assign(v0, [[2.0+3.0j, 3.0+2.0j]]).eval() self.assertEqual(True, tf.is_variable_initialized(v0).eval())
def _build(self): tensor = self._build_parameter() self._dataholder_tensor = tensor self._is_initialized_tensor = tf.is_variable_initialized(tensor)
def _build(self): unconstrained = self._build_parameter() constrained = self._build_constrained(unconstrained) prior = self._build_prior(unconstrained, constrained) self._is_initialized_tensor = tf.is_variable_initialized(unconstrained) self._unconstrained_tensor = unconstrained self._constrained_tensor = constrained self._prior_tensor = prior
def get_uninitialized_variables(variables=None): """Return a list of uninitialized tf variables. Parameters ---------- variables: tf.Variable, list(tf.Variable), optional Filter variable list to only those that are uninitialized. If no variables are specified the list of all variables in the graph will be used. Returns ------- list(tf.Variable) List of uninitialized tf variables. """ sess = tf.get_default_session() if variables is None: variables = tf.global_variables() else: variables = list(variables) if len(variables) == 0: return [] if semver.match(tf.__version__, '<1.0.0'): init_flag = sess.run( tf.pack([tf.is_variable_initialized(v) for v in variables])) else: init_flag = sess.run( tf.stack([tf.is_variable_initialized(v) for v in variables])) return [v for v, f in zip(variables, init_flag) if not f]
def get_uninitialized_variables(variables=None): """Return a list of uninitialized tf variables. Parameters ---------- variables: tf.Variable, list(tf.Variable), optional Filter variable list to only those that are uninitialized. If no variables are specified the list of all variables in the graph will be used. Returns ------- list(tf.Variable) List of uninitialized tf variables. """ sess = tf.get_default_session() if variables is None: variables = tf.global_variables() else: variables = list(variables) if len(variables) == 0: return [] if semver.match(tf.__version__, '<1.0.0'): init_flag = sess.run( tf.pack([tf.is_variable_initialized(v) for v in variables])) else: init_flag = sess.run( tf.stack([tf.is_variable_initialized(v) for v in variables])) return [v for v, f in zip(variables, init_flag) if not f] # Tears of the debugging...
