Python variables.global_variables方法代码示例

# 需要导入模块: from tensorflow.python.ops import variables [as 别名]
# 或者: from tensorflow.python.ops.variables import global_variables [as 别名]
def _initialize_variables(session):
  """Utility to initialize uninitialized variables on the fly."""
  variables = variables_module.global_variables()
  candidate_vars = []
  for v in variables:
    if not getattr(v, '_keras_initialized', False):
      candidate_vars.append(v)
  # This step is expensive, so we only run it on variables not already
  # marked as initialized.
  is_initialized = session.run(
      [variables_module.is_variable_initialized(v) for v in candidate_vars])
  uninitialized_vars = []
  for flag, v in zip(is_initialized, candidate_vars):
    if not flag:
      uninitialized_vars.append(v)
    v._keras_initialized = True
  if uninitialized_vars:
    session.run(variables_module.variables_initializer(uninitialized_vars)) 

# 需要导入模块: from tensorflow.python.ops import variables [as 别名]
# 或者: from tensorflow.python.ops.variables import global_variables [as 别名]
def _init_saver(self, saver=USE_DEFAULT):
    """Initializes saver.

    Args:
      saver: A `Saver` object. If set to USE_DEFAULT, create one that
        saves all the variables.
    """
    if saver is Supervisor.USE_DEFAULT:
      saver = self._get_first_op_from_collection(ops.GraphKeys.SAVERS)
      if saver is None and variables.global_variables():
        saver = saver_mod.Saver()
        ops.add_to_collection(ops.GraphKeys.SAVERS, saver)
    self._saver = saver 

# 需要导入模块: from tensorflow.python.ops import variables [as 别名]
# 或者: from tensorflow.python.ops.variables import global_variables [as 别名]
def _initialize_variables():
  """Utility to initialize uninitialized variables on the fly.
  """
  variables = variables_module.global_variables()
  uninitialized_variables = []
  for v in variables:
    if not hasattr(v, '_keras_initialized') or not v._keras_initialized:
      uninitialized_variables.append(v)
      v._keras_initialized = True
  if uninitialized_variables:
    sess = get_session()
    sess.run(variables_module.variables_initializer(uninitialized_variables)) 

# 需要导入模块: from tensorflow.python.ops import variables [as 别名]
# 或者: from tensorflow.python.ops.variables import global_variables [as 别名]
def _get_saver():
  """Lazy init and return saver."""
  saver = _get_first_op_from_collection(ops.GraphKeys.SAVERS)
  if saver is not None:
    if saver:
      saver = saver[0]
    else:
      saver = None
  if saver is None and variables.global_variables():
    saver = tf_saver.Saver()
    ops.add_to_collection(ops.GraphKeys.SAVERS, saver)
  return saver 

# 需要导入模块: from tensorflow.python.ops import variables [as 别名]
# 或者: from tensorflow.python.ops.variables import global_variables [as 别名]
def _get_saver():
  """Lazy init and return saver."""
  saver = _get_first_op_from_collection(ops.GraphKeys.SAVERS)
  if saver is None and variables.global_variables():
    saver = tf_saver.Saver()
    ops.add_to_collection(ops.GraphKeys.SAVERS, saver)
  return saver 

# 需要导入模块: from tensorflow.python.ops import variables [as 别名]
# 或者: from tensorflow.python.ops.variables import global_variables [as 别名]
def testTrainWithInitFromFn(self):
    logdir1 = os.path.join(
        tempfile.mkdtemp(prefix=self.get_temp_dir()), 'tmp_logs1')
    logdir2 = os.path.join(
        tempfile.mkdtemp(prefix=self.get_temp_dir()), 'tmp_logs2')

    # First, train the model one step (make sure the error is high).
    with ops.Graph().as_default():
      random_seed.set_random_seed(0)
      train_op = self.create_train_op()
      loss = learning.train(train_op, logdir1, number_of_steps=1)
      self.assertGreater(loss, .5)

    # Next, train the model to convergence.
    with ops.Graph().as_default():
      random_seed.set_random_seed(1)
      train_op = self.create_train_op()
      loss = learning.train(
          train_op, logdir1, number_of_steps=300, log_every_n_steps=10)
      self.assertIsNotNone(loss)
      self.assertLess(loss, .015)

    # Finally, advance the model a single step and validate that the loss is
    # still low.
    with ops.Graph().as_default():
      random_seed.set_random_seed(2)
      train_op = self.create_train_op()

      model_variables = variables_lib.global_variables()
      model_path = os.path.join(logdir1, 'model.ckpt-300')
      saver = saver_lib.Saver(model_variables)

      def RestoreFn(sess):
        saver.restore(sess, model_path)

      loss = learning.train(
          train_op, logdir2, number_of_steps=1, init_fn=RestoreFn)

      self.assertIsNotNone(loss)
      self.assertLess(loss, .015) 

# 需要导入模块: from tensorflow.python.ops import variables [as 别名]
# 或者: from tensorflow.python.ops.variables import global_variables [as 别名]
def DISABLED_testShared(self):
    with self.test_session():
      with specs.ops:
        # pylint: disable=undefined-variable
        f = Shared(Fr(100))
        g = f | f | f | f
      inputs = constant_op.constant(_rand(10, 100))
      _ = g.funcall(inputs)
      self.assertEqual(len(variables.global_variables()), 2) 

# 需要导入模块: from tensorflow.python.ops import variables [as 别名]
# 或者: from tensorflow.python.ops.variables import global_variables [as 别名]
def testLocalVariableNotInAllVariables(self):
    with self.cached_session():
      with variable_scope.variable_scope('A'):
        a = variables_lib2.local_variable(0)
        self.assertNotIn(a, variables_lib.global_variables())
        self.assertIn(a, variables_lib.local_variables()) 

# 需要导入模块: from tensorflow.python.ops import variables [as 别名]
# 或者: from tensorflow.python.ops.variables import global_variables [as 别名]
def test_global_variable(self):
    with self.cached_session() as sess:
      self.assertEqual([], variables_lib.global_variables())
      value0 = 42
      variables_lib2.global_variable(value0)
      value1 = 43
      variables_lib2.global_variable(value1)
      variables = variables_lib.global_variables()
      self.assertEqual(2, len(variables))
      with self.assertRaises(errors_impl.FailedPreconditionError):
        sess.run(variables)
      variables_lib.variables_initializer(variables).run()
      self.assertAllEqual(set([value0, value1]), set(sess.run(variables))) 

# 需要导入模块: from tensorflow.python.ops import variables [as 别名]
# 或者: from tensorflow.python.ops.variables import global_variables [as 别名]
def testVariableNameAndShape(self):
    with self.cached_session():
      with variable_scope.variable_scope('A'):
        a = variables_lib2.global_variable([1, 1, 1, 1, 1], name='a')
        self.assertEqual(a.op.name, 'A/a')
        self.assertListEqual(a.get_shape().as_list(), [5])
        self.assertListEqual([a], variables_lib.global_variables()) 

# 需要导入模块: from tensorflow.python.ops import variables [as 别名]
# 或者: from tensorflow.python.ops.variables import global_variables [as 别名]
def testGlobalVariableNotInLocalVariables(self):
    with self.cached_session():
      with variable_scope.variable_scope('A'):
        a = variables_lib2.global_variable(0)
        self.assertNotIn(a, variables_lib.local_variables())
        self.assertIn(a, variables_lib.global_variables()) 

# 需要导入模块: from tensorflow.python.ops import variables [as 别名]
# 或者: from tensorflow.python.ops.variables import global_variables [as 别名]
def testNotInLocalVariables(self):
    with self.cached_session():
      with variable_scope.variable_scope('A'):
        a = variables_lib2.model_variable('a', [5])
        self.assertIn(a, variables_lib.global_variables())
        self.assertIn(a, ops.get_collection(ops.GraphKeys.MODEL_VARIABLES))
        self.assertNotIn(a, variables_lib.local_variables()) 

# 需要导入模块: from tensorflow.python.ops import variables [as 别名]
# 或者: from tensorflow.python.ops.variables import global_variables [as 别名]
def testAverages(self):
    with self.cached_session() as session:
      scale = 2.
      grad = array_ops.ones([3, 4]) * scale
      log_norm = np.log(np.sqrt(scale**2 * grad.get_shape().num_elements()))
      grads_and_vars = [(grad, grad)]
      grads_and_vars = optimizers_lib.adaptive_clipping_fn(
          decay=0.5)(grads_and_vars)

      var_dict = {}
      for var in variables.global_variables():
        if var.name.startswith("AdaptiveMaxNorm"):
          var_dict[var.name.split(":")[0]] = var
      self.assertEqual(2, len(var_dict))
      moving_mean = var_dict["AdaptiveMaxNorm/mean"]
      moving_sq_mean = var_dict["AdaptiveMaxNorm/sq_mean"]
      variables.global_variables_initializer().run()
      mean, sq_mean = session.run([moving_mean, moving_sq_mean])
      self.assertEqual([0], mean)
      self.assertEqual([0], sq_mean)
      for i in range(20):
        mean, sq_mean, _ = session.run(
            [moving_mean, moving_sq_mean, grads_and_vars[0][0]])
        if i == 0:
          self.assertLess(mean, 0.9 * log_norm)
          self.assertLess(sq_mean, 0.9 * log_norm**2)

      self.assertAlmostEqual(float(mean), log_norm, places=4)
      self.assertAlmostEqual(float(sq_mean), log_norm**2, places=4) 

# 需要导入模块: from tensorflow.python.ops import variables [as 别名]
# 或者: from tensorflow.python.ops.variables import global_variables [as 别名]
def testReuse(self):

    def f(x):
      return core_layers.dense(x, self.CHANNELS // 2)

    def g(x):
      return core_layers.dense(x, self.CHANNELS // 2)

    x = random_ops.random_uniform(
        [self.BATCH_SIZE, self.CHANNELS], dtype=dtypes.float32)
    x1, x2 = array_ops.split(x, 2, axis=-1)

    with variable_scope.variable_scope("test"):
      y1, y2 = rev_block_lib.rev_block(x1, x2, f, g, num_layers=self.NUM_LAYERS)

    num_vars_before = len(variables.global_variables())

    with variable_scope.variable_scope("test", reuse=True):
      y1, y2 = rev_block_lib.rev_block(x1, x2, f, g, num_layers=self.NUM_LAYERS)

    num_vars_after = len(variables.global_variables())
    self.assertEqual(num_vars_before, num_vars_after)

    loss = math_ops.reduce_mean(y1 + y2)
    _ = gradients_impl.gradients(loss,
                                 [x] + variables.trainable_variables())

    with variable_scope.variable_scope("test", reuse=True):
      y1, y2 = rev_block_lib.rev_block(x1, x2, f, g, num_layers=self.NUM_LAYERS)

    num_vars_after = len(variables.global_variables())
    self.assertEqual(num_vars_before, num_vars_after) 

# 需要导入模块: from tensorflow.python.ops import variables [as 别名]
# 或者: from tensorflow.python.ops.variables import global_variables [as 别名]
def testTrainWithInitFromFn(self):
    logdir1 = tempfile.mkdtemp('tmp_logs1')
    logdir2 = tempfile.mkdtemp('tmp_logs2')

    # First, train the model one step (make sure the error is high).
    with ops.Graph().as_default():
      random_seed.set_random_seed(0)
      train_op = self.create_train_op()
      loss = learning.train(train_op, logdir1, number_of_steps=1)
      self.assertGreater(loss, .5)

    # Next, train the model to convergence.
    with ops.Graph().as_default():
      random_seed.set_random_seed(1)
      train_op = self.create_train_op()
      loss = learning.train(
          train_op, logdir1, number_of_steps=300, log_every_n_steps=10)
      self.assertIsNotNone(loss)
      self.assertLess(loss, .015)

    # Finally, advance the model a single step and validate that the loss is
    # still low.
    with ops.Graph().as_default():
      random_seed.set_random_seed(2)
      train_op = self.create_train_op()

      model_variables = variables_lib.global_variables()
      model_path = os.path.join(logdir1, 'model.ckpt-300')
      saver = saver_lib.Saver(model_variables)

      def RestoreFn(sess):
        saver.restore(sess, model_path)

      loss = learning.train(
          train_op, logdir2, number_of_steps=1, init_fn=RestoreFn)

      self.assertIsNotNone(loss)
      self.assertLess(loss, .015)