Python math_ops.logical_not方法代码示例

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import logical_not [as 别名]
def _MaximumMinimumGrad(op, grad, selector_op):
  """Factor out the code for the gradient of Maximum or Minimum."""
  x = op.inputs[0]
  y = op.inputs[1]
  gdtype = grad.dtype
  sx = array_ops.shape(x)
  sy = array_ops.shape(y)
  gradshape = array_ops.shape(grad)
  zeros = array_ops.zeros(gradshape, gdtype)
  xmask = selector_op(x, y)
  rx, ry = gen_array_ops._broadcast_gradient_args(sx, sy)
  xgrad = array_ops.where(xmask, grad, zeros)
  ygrad = array_ops.where(math_ops.logical_not(xmask), grad, zeros)
  gx = array_ops.reshape(math_ops.reduce_sum(xgrad, rx), sx)
  gy = array_ops.reshape(math_ops.reduce_sum(ygrad, ry), sy)
  return (gx, gy) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import logical_not [as 别名]
def _make_auc_histograms(boolean_labels, scores, score_range, nbins):
  """Create histogram tensors from one batch of labels/scores."""

  with variable_scope.variable_scope(
      None, 'make_auc_histograms', [boolean_labels, scores, nbins]):
    # Histogram of scores for records in this batch with True label.
    hist_true = histogram_ops.histogram_fixed_width(
        array_ops.boolean_mask(scores, boolean_labels),
        score_range,
        nbins=nbins,
        dtype=dtypes.int64,
        name='hist_true')
    # Histogram of scores for records in this batch with False label.
    hist_false = histogram_ops.histogram_fixed_width(
        array_ops.boolean_mask(scores, math_ops.logical_not(boolean_labels)),
        score_range,
        nbins=nbins,
        dtype=dtypes.int64,
        name='hist_false')
    return hist_true, hist_false 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import logical_not [as 别名]
def _wrap_computation_in_while_loop_with_stopping_signals(device, op_fn):
  """Wraps the ops generated by `op_fn` in tf.while_loop."""

  def cond(scalar_stopping_signal):
    return math_ops.logical_not(
        _StopSignals.should_stop(scalar_stopping_signal))

  def computation(unused_scalar_stopping_signal):
    return_value = op_fn()
    execute_ops = return_value['ops']
    signals = return_value['signals']
    with ops.control_dependencies(execute_ops):
      return _StopSignals.as_scalar_stopping_signal(signals)

  # By setting parallel_iterations=1, the parallel execution in while_loop is
  # basically turned off.
  with ops.device(device):
    return control_flow_ops.while_loop(
        cond,
        computation, [_StopSignals.NON_STOPPING_SIGNAL],
        parallel_iterations=1) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import logical_not [as 别名]
def report_uninitialized_resources(resource_list=None,
                                   name="report_uninitialized_resources"):
  """Returns the names of all uninitialized resources in resource_list.

  If the returned tensor is empty then all resources have been initialized.

  Args:
   resource_list: resources to check. If None, will use shared_resources() +
    local_resources().
   name: name for the resource-checking op.

  Returns:
   Tensor containing names of the handles of all resources which have not
   yet been initialized.

  """
  if resource_list is None:
    resource_list = shared_resources() + local_resources()
  with ops.name_scope(name):
    if not resource_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)
    # Get a 1-D boolean tensor listing whether each resource is initialized.
    variables_mask = math_ops.logical_not(
        array_ops.stack([r.is_initialized for r in resource_list]))
    # Get a 1-D string tensor containing all the resource names.
    variable_names_tensor = array_ops.constant(
        [s.handle.name for s in resource_list])
    # Return a 1-D tensor containing all the names of uninitialized resources.
    return array_ops.boolean_mask(variable_names_tensor, variables_mask) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import logical_not [as 别名]
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 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import logical_not [as 别名]
def _logical_not(x):
  """Convenience function which attempts to statically apply `logical_not`."""
  x_ = _static_value(x)
  if x_ is None:
    return math_ops.logical_not(x)
  return constant_op.constant(np.logical_not(x_)) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import logical_not [as 别名]
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)) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import logical_not [as 别名]
def __invert__(self):
    return logical_not(self) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import logical_not [as 别名]
def setUp(self):
    super(LogicalNotTest, self).setUp()
    self.ops = [('logical_not', operator.invert, math_ops.logical_not,
                 core.logical_not),]
    self.test_lt = self.original_lt < 10 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import logical_not [as 别名]
def _predict_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn):
  """Executes `model_fn_wrapper` multiple times on all TPU shards."""
  (single_tpu_predict_step, host_calls, captured_scaffold_fn,
   captured_predict_hooks
  ) = model_fn_wrapper.convert_to_single_tpu_predict_step(dequeue_fn)

  def multi_tpu_predict_steps_on_single_shard():

    def cond(scalar_stopping_signal):
      return math_ops.logical_not(
          _StopSignals.should_stop(scalar_stopping_signal))

    inputs = [_StopSignals.NON_STOPPING_SIGNAL]
    outputs = training_loop.while_loop(
        cond, single_tpu_predict_step, inputs=inputs, name=b'loop')
    return outputs

  (compile_op, dummy_predict_op,) = tpu.split_compile_and_shard(
      multi_tpu_predict_steps_on_single_shard,
      inputs=[],
      num_shards=ctx.num_replicas,
      outputs_from_all_shards=False,
      device_assignment=ctx.device_assignment)

  dummy_predict_op = dummy_predict_op[0]
  scaffold = _get_scaffold(captured_scaffold_fn)
  return (compile_op, dummy_predict_op, host_calls, scaffold,
          captured_predict_hooks.get()) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import logical_not [as 别名]
def report_uninitialized_resources(resource_list=None,
                                   name="report_uninitialized_resources"):
  """Returns the names of all uninitialized resources in resource_list.

  If the returned tensor is empty then all resources have been initialized.

  Args:
   resource_list: resources to check. If None, will use shared_resources() +
    local_resources().
   name: name for the resource-checking op.

  Returns:
   Tensor containing names of the handles of all resources which have not
   yet been initialized.

  """
  if resource_list is None:
    resource_list = shared_resources() + local_resources()
  with ops.name_scope(name):
    if not resource_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)
    # Get a 1-D boolean tensor listing whether each resource is initialized.
    variables_mask = math_ops.logical_not(array_ops.pack(
        [r.is_initialized for r in resource_list]))
    # Get a 1-D string tensor containing all the resource names.
    variable_names_tensor = array_ops.constant(
        [s.handle.name for s in resource_list])
    # Return a 1-D tensor containing all the names of uninitialized resources.
    return array_ops.boolean_mask(variable_names_tensor, variables_mask) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import logical_not [as 别名]
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 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import logical_not [as 别名]
def _predict_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn):
  """Executes `model_fn_wrapper` multiple times on all TPU shards."""
  (single_tpu_predict_step, host_calls, captured_scaffold_fn,
   captured_predict_hooks
  ) = model_fn_wrapper.convert_to_single_tpu_predict_step(dequeue_fn)

  def multi_tpu_predict_steps_on_single_shard():

    def cond(scalar_stopping_signal):
      return math_ops.logical_not(
          _StopSignals.should_stop(scalar_stopping_signal))

    inputs = [_StopSignals.NON_STOPPING_SIGNAL]
    outputs = training_loop.while_loop(
        cond, single_tpu_predict_step, inputs=inputs, name=b'loop')
    return outputs

  (dummy_predict_op,) = tpu.shard(
      multi_tpu_predict_steps_on_single_shard,
      inputs=[],
      num_shards=ctx.num_replicas,
      outputs_from_all_shards=False,
      device_assignment=ctx.device_assignment)

  scaffold = _get_scaffold(captured_scaffold_fn)
  return dummy_predict_op, host_calls, scaffold, captured_predict_hooks.get() 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import logical_not [as 别名]
def apply_score_masking(self, score, mask):  #pylint: disable=no-self-use
        """ ignore sequence paddings """
        padding_mask = tf.expand_dims(math_ops.logical_not(mask), 2)
        # Bias so padding positions do not contribute to attention distribution.
        score -= 1.e9 * math_ops.cast(padding_mask, dtype=tf.float32)
        return score