Python dtypes.bool方法代碼示例

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import bool [as 別名]
def _parse_kwargs_as_attrs(func_name, **kwargs):
  """Parses **kwargs into a node's attributes."""
  attrs = {}

  noinline = kwargs.pop("noinline", None)
  if noinline is not None:
    attrs["_noinline"] = attr_value_pb2.AttrValue(b=bool(noinline))

  compiled = kwargs.pop("compiled", None)
  separate_compiled_gradients = kwargs.pop("separate_compiled_gradients", None)
  if compiled is not None:
    attrs["_XlaCompile"] = attr_value_pb2.AttrValue(b=bool(compiled))
    attrs["_XlaSeparateCompiledGradients"] = attr_value_pb2.AttrValue(
        b=bool(separate_compiled_gradients))
    attrs["_XlaScope"] = attr_value_pb2.AttrValue(
        s=("function_%s" % func_name).encode())

  if kwargs:
    raise ValueError("Unknown keyword arguments: %s" % kwargs.keys())
  return attrs 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import bool [as 別名]
def var(x, axis=None, keepdims=False):
  """Variance of a tensor, alongside the specified axis.

  Arguments:
      x: A tensor or variable.
      axis: An integer, the axis to compute the variance.
      keepdims: A boolean, whether to keep the dimensions or not.
          If `keepdims` is `False`, the rank of the tensor is reduced
          by 1. If `keepdims` is `True`,
          the reduced dimension is retained with length 1.

  Returns:
      A tensor with the variance of elements of `x`.
  """
  axis = _normalize_axis(axis, ndim(x))
  if x.dtype.base_dtype == dtypes_module.bool:
    x = math_ops.cast(x, floatx())
  m = math_ops.reduce_mean(x, reduction_indices=axis, keep_dims=True)
  devs_squared = math_ops.square(x - m)
  return math_ops.reduce_mean(
      devs_squared, reduction_indices=axis, keep_dims=keepdims) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import bool [as 別名]
def mean(x, axis=None, keepdims=False):
  """Mean of a tensor, alongside the specified axis.

  Arguments:
      x: A tensor or variable.
      axis: A list of integer. Axes to compute the mean.
      keepdims: A boolean, whether to keep the dimensions or not.
          If `keepdims` is `False`, the rank of the tensor is reduced
          by 1 for each entry in `axis`. If `keep_dims` is `True`,
          the reduced dimensions are retained with length 1.

  Returns:
      A tensor with the mean of elements of `x`.
  """
  axis = _normalize_axis(axis, ndim(x))
  if x.dtype.base_dtype == dtypes_module.bool:
    x = math_ops.cast(x, floatx())
  return math_ops.reduce_mean(x, reduction_indices=axis, keep_dims=keepdims) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import bool [as 別名]
def _streaming_auc(predictions, labels, weights=None, class_id=None,
                   curve="ROC"):
  # pylint: disable=missing-docstring
  predictions = math_ops.to_float(predictions)
  if labels.dtype.base_dtype != dtypes.bool:
    logging.warning("Casting %s labels to bool.", labels.dtype)
    labels = math_ops.cast(labels, dtypes.bool)
  weights = _float_weights_or_none(weights)
  if weights is not None:
    weights = weights_broadcast_ops.broadcast_weights(weights, predictions)
  if class_id is not None:
    if weights is not None:
      weights = weights[:, class_id]
    predictions = predictions[:, class_id]
    labels = labels[:, class_id]
  return metrics_lib.streaming_auc(
      predictions, labels, weights=weights, curve=curve) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import bool [as 別名]
def __init__(self, inputs, sequence_length, time_major=False, name=None):
        """Initializer.

        Args:
          inputs: A (structure of) input tensors.
          sequence_length: An int32 vector tensor.
          time_major: Python bool.  Whether the tensors in `inputs` are time major.
            If `False` (default), they are assumed to be batch major.
          name: Name scope for any created operations.

        Raises:
          ValueError: if `sequence_length` is not a 1D tensor.
        """
        with ops.name_scope(name, "TrainingHelper", [inputs, sequence_length]):
            inputs = ops.convert_to_tensor(inputs, name="inputs")
            self._inputs = inputs
            if not time_major:
                inputs = nest.map_structure(_transpose_batch_time, inputs)

            self._input_tas = nest.map_structure(_unstack_ta, inputs)
            self._sequence_length = ops.convert_to_tensor(
                sequence_length, name="sequence_length")
            if self._sequence_length.get_shape().ndims != 1:
                raise ValueError(
                    "Expected sequence_length to be a vector, but received shape: %s" %
                    self._sequence_length.get_shape())

            self._zero_inputs = nest.map_structure(
                lambda inp: array_ops.zeros_like(inp[0, :]), inputs)

            self._batch_size = shape_list(sequence_length)[0] 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import bool [as 別名]
def sample(self, time, outputs, state, name=None):
        """Gets a sample for one step."""
        with ops.name_scope(name, "ScheduledEmbeddingTrainingHelperSample",
                            [time, outputs, state]):
            # Return -1s where we did not sample, and sample_ids elsewhere
            select_sampler = bernoulli.Bernoulli(
                probs=self._sampling_probability, dtype=dtypes.bool)
            select_sample = select_sampler.sample(
                sample_shape=self.batch_size, seed=self._scheduling_seed)
            sample_id_sampler = categorical.Categorical(logits=outputs)
            return array_ops.where(
                select_sample,
                sample_id_sampler.sample(seed=self._seed),
                gen_array_ops.fill([self.batch_size], -1)) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import bool [as 別名]
def summarize_tensor(tensor, tag=None):
  """Summarize a tensor using a suitable summary type.

  This function adds a summary op for `tensor`. The type of summary depends on
  the shape of `tensor`. For scalars, a `scalar_summary` is created, for all
  other tensors, `histogram_summary` is used.

  Args:
    tensor: The tensor to summarize
    tag: The tag to use, if None then use tensor's op's name.

  Returns:
    The summary op created or None for string tensors.
  """
  # Skips string tensors and boolean tensors (not handled by the summaries).
  if (tensor.dtype.is_compatible_with(dtypes.string) or
      tensor.dtype.base_dtype == dtypes.bool):
    return None

  if tensor.get_shape().ndims == 0:
    # For scalars, use a scalar summary.
    return _add_scalar_summary(tensor, tag)
  else:
    # We may land in here if the rank is still unknown. The histogram won't
    # hurt if this ends up being a scalar.
    return _add_histogram_summary(tensor, tag) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import bool [as 別名]
def false_negatives(labels, predictions, weights=None,
                    metrics_collections=None,
                    updates_collections=None,
                    name=None):
  """Computes the total number of false negatives.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    labels: The ground truth values, a `Tensor` whose dimensions must match
      `predictions`. Will be cast to `bool`.
    predictions: The predicted values, a `Tensor` of arbitrary dimensions. Will
      be cast to `bool`.
    weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `labels`, and must be broadcastable to `labels` (i.e., all dimensions must
      be either `1`, or the same as the corresponding `labels` dimension).
    metrics_collections: An optional list of collections that the metric
      value variable should be added to.
    updates_collections: An optional list of collections that the metric update
      ops should be added to.
    name: An optional variable_scope name.

  Returns:
    value_tensor: A `Tensor` representing the current value of the metric.
    update_op: An operation that accumulates the error from a batch of data.

  Raises:
    ValueError: If `weights` is not `None` and its shape doesn't match `values`,
      or if either `metrics_collections` or `updates_collections` are not a list
      or tuple.
  """
  with variable_scope.variable_scope(
      name, 'false_negatives', (predictions, labels, weights)):

    labels = math_ops.cast(labels, dtype=dtypes.bool)
    predictions = math_ops.cast(predictions, dtype=dtypes.bool)
    predictions.get_shape().assert_is_compatible_with(labels.get_shape())
    is_false_negative = math_ops.logical_and(math_ops.equal(labels, True),
                                             math_ops.equal(predictions, False))
    return _count_condition(is_false_negative, weights, metrics_collections,
                            updates_collections) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import bool [as 別名]
def zeros(shape, dtype=dtypes.float32, name=None):
  """Creates a tensor with all elements set to zero.

  This operation returns a tensor of type `dtype` with shape `shape` and
  all elements set to zero.

  For example:

  ```python
  tf.zeros([3, 4], tf.int32) ==> [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
  ```

  Args:
    shape: Either a list of integers, or a 1-D `Tensor` of type `int32`.
    dtype: The type of an element in the resulting `Tensor`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` with all elements set to zero.
  """
  dtype = dtypes.as_dtype(dtype).base_dtype
  with ops.name_scope(name, "zeros", [shape]) as name:
    if dtype == dtypes.bool:
      zero = False
    elif dtype == dtypes.string:
      zero = ""
    else:
      zero = 0
    try:
      shape = tensor_shape.as_shape(shape)
      output = constant(zero, shape=shape, dtype=dtype, name=name)
    except (TypeError, ValueError):
      shape = ops.convert_to_tensor(shape, dtype=dtypes.int32, name="shape")
      output = fill(shape, constant(zero, dtype=dtype), name=name)
  assert output.dtype.base_dtype == dtype
  return output 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import bool [as 別名]
def ones(shape, dtype=dtypes.float32, name=None):
  """Creates a tensor with all elements set to 1.

  This operation returns a tensor of type `dtype` with shape `shape` and all
  elements set to 1.

  For example:

  ```python
  tf.ones([2, 3], tf.int32) ==> [[1, 1, 1], [1, 1, 1]]
  ```

  Args:
    shape: Either a list of integers, or a 1-D `Tensor` of type `int32`.
    dtype: The type of an element in the resulting `Tensor`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` with all elements set to 1.
  """
  dtype = dtypes.as_dtype(dtype).base_dtype
  with ops.name_scope(name, "ones", [shape]) as name:
    one = True if dtype == dtypes.bool else 1
    try:
      shape = tensor_shape.as_shape(shape)
      output = constant(one, shape=shape, dtype=dtype, name=name)
    except (TypeError, ValueError):
      shape = ops.convert_to_tensor(shape, dtype=dtypes.int32, name="shape")
      output = fill(shape, constant(one, dtype=dtype), name=name)
  assert output.dtype.base_dtype == dtype
  return output 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import bool [as 別名]
def assert_type(tensor, tf_type, message=None, name=None):
  """Statically asserts that the given `Tensor` is of the specified type.

  Args:
    tensor: A tensorflow `Tensor`.
    tf_type: A tensorflow type (`dtypes.float32`, `tf.int64`, `dtypes.bool`,
      etc).
    message: A string to prefix to the default message.
    name:  A name to give this `Op`.  Defaults to "assert_type"

  Raises:
    TypeError: If the tensors data type doesn't match `tf_type`.

  Returns:
    A `no_op` that does nothing.  Type can be determined statically.
  """
  message = message or ''
  with ops.name_scope(name, 'assert_type', [tensor]):
    tensor = ops.convert_to_tensor(tensor, name='tensor')
    if tensor.dtype != tf_type:
      raise TypeError(
          '%s  %s must be of type %s' % (message, tensor.op.name, tf_type))

    return control_flow_ops.no_op('statically_determined_correct_type')


# pylint: disable=line-too-long 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import bool [as 別名]
def same_dynamic_shape(a, b):
  """Returns whether a and b have the same dynamic shape.

  Args:
    a: `Tensor`
    b: `Tensor`

  Returns:
    `bool` `Tensor` representing if both tensors have the same shape.
  """
  a = ops.convert_to_tensor(a, name="a")
  b = ops.convert_to_tensor(b, name="b")

  # Here we can't just do math_ops.equal(a.shape, b.shape), since
  # static shape inference may break the equality comparison between
  # shape(a) and shape(b) in math_ops.equal.
  def all_shapes_equal():
    return math_ops.reduce_all(math_ops.equal(
        array_ops.concat([array_ops.shape(a), array_ops.shape(b)], 0),
        array_ops.concat([array_ops.shape(b), array_ops.shape(a)], 0)))

  # One of the shapes isn't fully defined, so we need to use the dynamic
  # shape.
  return control_flow_ops.cond(
      math_ops.equal(array_ops.rank(a), array_ops.rank(b)),
      all_shapes_equal,
      lambda: constant_op.constant(False)) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import bool [as 別名]
def _FilterBool(v):
  if isinstance(v, (list, tuple)):
    return _FirstNotNone([_FilterBool(x) for x in v])
  return None if isinstance(v, bool) else _NotNone(v) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import bool [as 別名]
def clear_session():
  """Destroys the current TF graph and creates a new one.

  Useful to avoid clutter from old models / layers.
  """
  global _SESSION
  global _GRAPH_LEARNING_PHASES  # pylint: disable=global-variable-not-assigned
  ops.reset_default_graph()
  reset_uids()
  _SESSION = None
  phase = array_ops.placeholder(dtype='bool', name='keras_learning_phase')
  _GRAPH_LEARNING_PHASES = {}
  _GRAPH_LEARNING_PHASES[ops.get_default_graph()] = phase 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import bool [as 別名]
def any(x, axis=None, keepdims=False):
  """Bitwise reduction (logical OR).

  Arguments:
      x: Tensor or variable.
      axis: axis along which to perform the reduction.
      keepdims: whether the drop or broadcast the reduction axes.

  Returns:
      A uint8 tensor (0s and 1s).
  """
  axis = _normalize_axis(axis, ndim(x))
  x = math_ops.cast(x, dtypes_module.bool)
  return math_ops.reduce_any(x, reduction_indices=axis, keep_dims=keepdims)