Python tensor_shape.scalar方法代码示例

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import scalar [as 别名]
def add_evaluation_step(result_tensor, ground_truth_tensor):
  """Inserts the operations we need to evaluate the accuracy of our results.

  Args:
    result_tensor: The new final node that produces results.
    ground_truth_tensor: The node we feed ground truth data
    into.

  Returns:
    Tuple of (evaluation step, prediction).
  """
  with tf.name_scope('accuracy'):
    with tf.name_scope('correct_prediction'):
      prediction = tf.argmax(result_tensor, 1)
      correct_prediction = tf.equal(
          prediction, tf.argmax(ground_truth_tensor, 1))
    with tf.name_scope('accuracy'):
      evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  tf.summary.scalar('accuracy', evaluation_step)
  return evaluation_step, prediction 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import scalar [as 别名]
def __init__(self, table_ref, default_value, initializer):
    """Construct a table object from a table reference.

    If requires a table initializer object (subclass of `TableInitializerBase`).
    It provides the table key and value types, as well as the op to initialize
    the table. The caller is responsible to execute the initialization op.

    Args:
      table_ref: The table reference, i.e. the output of the lookup table ops.
      default_value: The value to use if a key is missing in the table.
      initializer: The table initializer to use.
    """
    super(InitializableLookupTableBase,
          self).__init__(initializer.key_dtype, initializer.value_dtype,
                         table_ref.op.name.split("/")[-1])
    self._table_ref = table_ref
    self._default_value = ops.convert_to_tensor(
        default_value, dtype=self._value_dtype)
    self._default_value.get_shape().merge_with(tensor_shape.scalar())
    self._init = initializer.initialize(self) 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import scalar [as 别名]
def __init__(self, iterator_resource, initializer, output_types,
               output_shapes):
    """Creates a new iterator from the given iterator resource.

    NOTE(mrry): Most users will not call this initializer directly, and will
    instead use `Iterator.from_dataset()` or `Dataset.make_one_shot_iterator()`.

    Args:
      iterator_resource: A `tf.resource` scalar `tf.Tensor` representing the
        iterator.
      initializer: A `tf.Operation` that should be run to initialize this
        iterator.
      output_types: A nested structure of `tf.DType` objects corresponding to
        each component of an element of this iterator.
      output_shapes: A nested structure of `tf.TensorShape` objects
        corresponding to each component of an element of this dataset.
    """
    self._iterator_resource = iterator_resource
    self._initializer = initializer
    self._output_types = output_types
    self._output_shapes = output_shapes 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import scalar [as 别名]
def enumerate(self, start=0):
    """Enumerate the elements of this dataset.  Similar to python's `enumerate`.

    For example:

    ```python
    # NOTE: The following examples use `{ ... }` to represent the
    # contents of a dataset.
    a = { 1, 2, 3 }
    b = { (7, 8), (9, 10), (11, 12) }

    # The nested structure of the `datasets` argument determines the
    # structure of elements in the resulting dataset.
    a.enumerate(start=5) == { (5, 1), (6, 2), (7, 3) }
    b.enumerate() == { (0, (7, 8)), (1, (9, 10)), (2, (11, 12)) }

    Args:
      start: A `tf.int64` scalar `tf.Tensor`, representing the start
        value for enumeration.

    Returns:
      A `Dataset`.
    """
    max_value = np.iinfo(dtypes.int64.as_numpy_dtype).max
    return Dataset.zip((Dataset.range(start, max_value), self)) 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import scalar [as 别名]
def map(self, map_func, num_threads=None, output_buffer_size=None):
    """Maps `map_func` across this datset.

    Args:
      map_func: A function mapping a nested structure of tensors (having
        shapes and types defined by `self.output_shapes` and
       `self.output_types`) to another nested structure of tensors.
      num_threads: (Optional.) A `tf.int32` scalar `tf.Tensor`, representing
        the number of threads to use for processing elements in parallel. If
        not specified, elements will be processed sequentially without
        buffering.
      output_buffer_size: (Optional.) A `tf.int64` scalar `tf.Tensor`,
        representing the maximum number of processed elements that will be
        buffered when processing in parallel.

    Returns:
      A `Dataset`.
    """
    return MapDataset(self, map_func, num_threads, output_buffer_size) 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import scalar [as 别名]
def _padding_value_to_tensor(value, output_type):
  """Converts the padding value to a tensor.

  Args:
    value: The padding value.
    output_type: Its expected dtype.

  Returns:
    A scalar `Tensor`.

  Raises:
    ValueError: if the padding value is not a scalar.
    TypeError: if the padding value's type does not match `output_type`.
  """
  value = ops.convert_to_tensor(value, name="padding_value")
  if not value.shape.is_compatible_with(tensor_shape.scalar()):
    raise ValueError(
        "Padding value should be a scalar, but is not: %s" % value)
  if value.dtype != output_type:
    raise TypeError(
        "Padding value tensor (%s) does not match output type: %s"
        % (value, output_type))
  return value 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import scalar [as 别名]
def __init__(self, table_ref, default_value, initializer):
    """Construct a table object from a table reference.

    If requires a table initializer object (subclass of `TableInitializerBase`).
    It provides the table key and value types, as well as the op to initialize
    the table. The caller is responsible to execute the initialization op.

    Args:
      table_ref: The table reference, i.e. the output of the lookup table ops.
      default_value: The value to use if a key is missing in the table.
      initializer: The table initializer to use.
    """
    super(InitializableLookupTableBase, self).__init__(
        initializer.key_dtype, initializer.value_dtype,
        table_ref.op.name.split("/")[-1])
    self._table_ref = table_ref
    self._default_value = ops.convert_to_tensor(default_value,
                                                dtype=self._value_dtype)
    self._default_value.get_shape().merge_with(tensor_shape.scalar())
    self._init = initializer.initialize(self) 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import scalar [as 别名]
def variable_summaries(var):
  """Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""
  with tf.name_scope('summaries'):
    mean = tf.reduce_mean(var)
    tf.summary.scalar('mean', mean)
    with tf.name_scope('stddev'):
      stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
    tf.summary.scalar('stddev', stddev)
    tf.summary.scalar('max', tf.reduce_max(var))
    tf.summary.scalar('min', tf.reduce_min(var))
    tf.summary.histogram('histogram', var) 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import scalar [as 别名]
def _event_shape(self):
    return tensor_shape.scalar() 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import scalar [as 别名]
def size(self, name=None):
    """Compute the number of elements in this table.

    Args:
      name: A name for the operation (optional).

    Returns:
      A scalar tensor containing the number of elements in this table.
    """
    with ops.name_scope(name, "%s_Size" % self._name,
                        [self._table_ref]) as scope:
      # pylint: disable=protected-access
      return gen_lookup_ops._lookup_table_size_v2(self._table_ref, name=scope)
      # pylint: enable=protected-access 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import scalar [as 别名]
def scalar_shape(unused_op):
  """Shape function for ops that output a scalar value."""
  return [tensor_shape.scalar()] 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import scalar [as 别名]
def make_dataset_resource(self):
    """Creates a `tf.Tensor` of  `tf.resource` tensor representing this dataset.

    Returns:
      A scalar `tf.Tensor` of `tf.resource` type, which represents this dataset.
    """
    raise NotImplementedError("Dataset.make_dataset_resource") 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import scalar [as 别名]
def repeat(self, count=None):
    """Repeats this dataset `count` times.

    Args:
      count: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the
        number of times the elements of this dataset should be repeated. The
        default behavior (if `count` is `None` or `-1`) is for the elements to
        be repeated indefinitely.

    Returns:
      A `Dataset`.
    """
    return RepeatDataset(self, count) 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import scalar [as 别名]
def take(self, count):
    """Creates a `Dataset` with at most `count` elements from this dataset.

    Args:
      count: A `tf.int64` scalar `tf.Tensor`, representing the number of
        elements of this dataset that should be taken to form the new dataset.
        If `count` is -1, or if `count` is greater than the size of this
        dataset, the new dataset will contain all elements of this dataset.

    Returns:
      A `Dataset`.
    """
    return TakeDataset(self, count) 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import scalar [as 别名]
def skip(self, count):
    """Creates a `Dataset` that skips `count` elements from this dataset.

    Args:
      count: A `tf.int64` scalar `tf.Tensor`, representing the number
        of elements of this dataset that should be skipped to form the
        new dataset.  If `count` is greater than the size of this
        dataset, the new dataset will contain no elements.  If `count`
        is -1, skips the entire dataset.

    Returns:
      A `Dataset`.
    """
    return SkipDataset(self, count)