Python dtypes.int64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 int64 [as 別名]
def dense_to_sparse(tensor, eos_token=0, outputs_collections=None, scope=None):
  """Converts a dense tensor into a sparse tensor.

  An example use would be to convert dense labels to sparse ones
  so that they can be fed to the ctc_loss.

  Args:
     tensor: An `int` `Tensor` to be converted to a `Sparse`.
     eos_token: An integer. It is part of the target label that signifies the
       end of a sentence.
     outputs_collections: Collection to add the outputs.
     scope: Optional scope for name_scope.
  """
  with variable_scope.variable_scope(scope, 'dense_to_sparse', [tensor]) as sc:
    tensor = ops.convert_to_tensor(tensor)
    indices = array_ops.where(
        math_ops.not_equal(tensor, constant_op.constant(eos_token,
                                                        tensor.dtype)))
    values = array_ops.gather_nd(tensor, indices)
    shape = array_ops.shape(tensor, out_type=dtypes.int64)
    outputs = sparse_tensor.SparseTensor(indices, values, shape)
    return utils.collect_named_outputs(outputs_collections, sc.name, outputs) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 別名]
def _maybe_select_class_id(labels, predictions_idx, selected_id=None):
  """If class ID is specified, filter all other classes.

  Args:
    labels: `int64` `Tensor` or `SparseTensor` with shape
      [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
      target classes for the associated prediction. Commonly, N=1 and `labels`
      has shape [batch_size, num_labels]. [D1, ... DN] must match
      `predictions_idx`.
    predictions_idx: `int64` `Tensor` of class IDs, with shape [D1, ... DN, k]
      where N >= 1. Commonly, N=1 and `predictions_idx` has shape
      [batch size, k].
    selected_id: Int id to select.

  Returns:
    Tuple of `labels` and `predictions_idx`, possibly with classes removed.
  """
  if selected_id is None:
    return labels, predictions_idx
  return (_select_class_id(labels, selected_id),
          _select_class_id(predictions_idx, selected_id)) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 別名]
def shape_internal(input, name=None, optimize=True, out_type=dtypes.int32):
  # pylint: disable=redefined-builtin
  """Returns the shape of a tensor.

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    optimize: if true, encode the shape as a constant when possible.
    out_type: (Optional) The specified output type of the operation
      (`int32` or `int64`). Defaults to tf.int32.

  Returns:
    A `Tensor` of type `out_type`.

  """
  with ops.name_scope(name, "Shape", [input]) as name:
    if isinstance(
        input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
      return gen_math_ops.cast(input.dense_shape, out_type)
    else:
      input_tensor = ops.convert_to_tensor(input)
      input_shape = input_tensor.get_shape()
      if optimize and input_shape.is_fully_defined():
        return constant(input_shape.as_list(), out_type, name=name)
      return gen_array_ops.shape(input, name=name, out_type=out_type) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 別名]
def size(input, name=None, out_type=dtypes.int32):
  # pylint: disable=redefined-builtin
  """Returns the size of a tensor.

  This operation returns an integer representing the number of elements in
  `input`.

  For example:

  ```python
  # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]]
  size(t) ==> 12
  ```

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    out_type: (Optional) The specified output type of the operation
      (`int32` or `int64`). Defaults to tf.int32.

  Returns:
    A `Tensor` of type `out_type`. Defaults to tf.int32.
  """
  return size_internal(input, name, optimize=True, out_type=out_type) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 別名]
def size_internal(input, name=None, optimize=True, out_type=dtypes.int32):
  # pylint: disable=redefined-builtin,protected-access
  """Returns the size of a tensor.

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    optimize: if true, encode the size as a constant when possible.
    out_type: (Optional) The specified output type of the operation
      (`int32` or `int64`). Defaults to tf.int32.

  Returns:
    A `Tensor` of type `out_type`.
  """
  with ops.name_scope(name, "Size", [input]) as name:
    if isinstance(
        input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
      return gen_math_ops._prod(
          gen_math_ops.cast(input.dense_shape, out_type), 0, name=name)
    else:
      input_tensor = ops.convert_to_tensor(input)
      input_shape = input_tensor.get_shape()
      if optimize and input_shape.is_fully_defined():
        return constant(input_shape.num_elements(), out_type, name=name)
      return gen_array_ops.size(input, name=name, out_type=out_type) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 別名]
def _neg(x, name=None):
  """Computes numerical negative value element-wise.

  I.e., \\(y = -x\\).

  Args:
    x: A `Tensor` or `SparseTensor`. Must be one of the following types: `half`,
      `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` or `SparseTensor`, respectively. Has the same type as `x`.
  """
  return negative(x, name)


# pylint: enable=g-docstring-has-escape 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 別名]
def square(x, name=None):
  r"""Computes square of x element-wise.

  I.e., \\(y = x * x = x^2\\).

  Args:
    x: A `Tensor` or `SparseTensor`. Must be one of the following types: `half`,
      `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` or `SparseTensor`. Has the same type as `x`.
  """
  with ops.name_scope(name, "Square", [x]) as name:
    if isinstance(x, sparse_tensor.SparseTensor):
      x_square = gen_math_ops.square(x.values, name=name)
      return sparse_tensor.SparseTensor(
          indices=x.indices, values=x_square, dense_shape=x.dense_shape)
    else:
      return gen_math_ops.square(x, name=name) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 別名]
def sigmoid(x, name=None):
  """Computes sigmoid of `x` element-wise.

  Specifically, `y = 1 / (1 + exp(-x))`.

  Args:
    x: A Tensor with type `float32`, `float64`, `int32`, `complex64`, `int64`,
      or `qint32`.
    name: A name for the operation (optional).

  Returns:
    A Tensor with the same type as `x` if `x.dtype != qint32`
      otherwise the return type is `quint8`.

  @compatibility(numpy)
  Equivalent to np.scipy.special.expit
  @end_compatibility
  """
  with ops.name_scope(name, "Sigmoid", [x]) as name:
    x = ops.convert_to_tensor(x, name="x")
    return gen_math_ops._sigmoid(x, name=name) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 別名]
def tanh(x, name=None):
  """Computes hyperbolic tangent of `x` element-wise.

  Args:
    x: A Tensor or SparseTensor with type `float`, `double`, `int32`,
      `complex64`, `int64`, or `qint32`.
    name: A name for the operation (optional).

  Returns:
    A Tensor or SparseTensor respectively with the same type as `x` if
    `x.dtype != qint32` otherwise the return type is `quint8`.
  """
  with ops.name_scope(name, "Tanh", [x]) as name:
    if isinstance(x, sparse_tensor.SparseTensor):
      x_tanh = gen_math_ops._tanh(x.values, name=name)
      return sparse_tensor.SparseTensor(
          indices=x.indices, values=x_tanh, dense_shape=x.dense_shape)
    else:
      return gen_math_ops._tanh(x, name=name) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 別名]
def _transform_feature(self, inputs):
    input_tensor = _to_sparse_input(inputs.get(self.key))

    if self.dtype.is_integer != input_tensor.dtype.is_integer:
      raise ValueError(
          'Column dtype and SparseTensors dtype must be compatible. '
          'key: {}, column dtype: {}, tensor dtype: {}'.format(
              self.key, self.dtype, input_tensor.dtype))

    _assert_string_or_int(
        input_tensor.dtype,
        prefix='column_name: {} input_tensor'.format(self.key))

    key_dtype = self.dtype
    if input_tensor.dtype.is_integer:
      # `index_table_from_tensor` requires 64-bit integer keys.
      key_dtype = dtypes.int64
      input_tensor = math_ops.to_int64(input_tensor)

    return lookup_ops.index_table_from_tensor(
        vocabulary_list=tuple(self.vocabulary_list),
        default_value=self.default_value,
        dtype=key_dtype,
        name='{}_lookup'.format(self.key)).lookup(input_tensor) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 別名]
def _get_or_create_eval_step():
  """Gets or creates the eval step `Tensor`.

  Returns:
    A `Tensor` representing a counter for the evaluation step.

  Raises:
    ValueError: If multiple `Tensors` have been added to the
      `tf.GraphKeys.EVAL_STEP` collection.
  """
  graph = ops.get_default_graph()
  eval_steps = graph.get_collection(ops.GraphKeys.EVAL_STEP)
  if len(eval_steps) == 1:
    return eval_steps[0]
  elif len(eval_steps) > 1:
    raise ValueError('Multiple tensors added to tf.GraphKeys.EVAL_STEP')
  else:
    counter = variable_scope.get_variable(
        'eval_step',
        shape=[],
        dtype=dtypes.int64,
        initializer=init_ops.zeros_initializer(),
        trainable=False,
        collections=[ops.GraphKeys.LOCAL_VARIABLES, ops.GraphKeys.EVAL_STEP])
    return counter 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 別名]
def create_global_step(graph=None):
  """Create global step tensor in graph.

  Args:
    graph: The graph in which to create the global step tensor. If missing,
      use default graph.

  Returns:
    Global step tensor.

  Raises:
    ValueError: if global step tensor is already defined.
  """
  graph = graph or ops.get_default_graph()
  if get_global_step(graph) is not None:
    raise ValueError('"global_step" already exists.')
  # Create in proper graph and base name_scope.
  with graph.as_default() as g, g.name_scope(None):
    return variable_scope.get_variable(
        ops.GraphKeys.GLOBAL_STEP,
        shape=[],
        dtype=dtypes.int64,
        initializer=init_ops.zeros_initializer(),
        trainable=False,
        collections=[ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.GLOBAL_STEP]) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 別名]
def _tensor_shape_tensor_conversion_function(s, dtype=None, name=None,
                                             as_ref=False):
  _ = as_ref
  if not s.is_fully_defined():
    raise ValueError(
        "Cannot convert a partially known TensorShape to a Tensor: %s" % s)
  s_list = s.as_list()
  int64_value = 0
  for dim in s_list:
    if dim >= 2**31:
      int64_value = dim
      break

  if dtype is not None:
    if dtype not in (dtypes.int32, dtypes.int64):
      raise TypeError("Cannot convert a TensorShape to dtype: %s" % dtype)
    if dtype == dtypes.int32 and int64_value:
      raise ValueError("Cannot convert a TensorShape to dtype int32; "
                       "a dimension is too large (%s)" % int64_value)
  else:
    dtype = dtypes.int64 if int64_value else dtypes.int32
  if name is None:
    name = "shape_as_tensor"
  return constant(s_list, dtype=dtype, name=name) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 別名]
def _init_clusters_random(self):
    """Does random initialization of clusters.

    Returns:
      Tensor of randomly initialized clusters.
    """
    num_data = math_ops.add_n([array_ops.shape(inp)[0] for inp in self._inputs])
    # Note that for mini-batch k-means, we should ensure that the batch size of
    # data used during initialization is sufficiently large to avoid duplicated
    # clusters.
    with ops.control_dependencies(
        [check_ops.assert_less_equal(self._num_clusters, num_data)]):
      indices = random_ops.random_uniform(
          array_ops.reshape(self._num_clusters, [-1]),
          minval=0,
          maxval=math_ops.cast(num_data, dtypes.int64),
          seed=self._random_seed,
          dtype=dtypes.int64)
      clusters_init = embedding_lookup(
          self._inputs, indices, partition_strategy='div')
      return clusters_init 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 別名]
def _init_clusters_random(data, num_clusters, random_seed):
  """Does random initialization of clusters.

  Args:
    data: a list of Tensors with a matrix of data, each row is an example.
    num_clusters: an integer with the number of clusters.
    random_seed: Seed for PRNG used to initialize seeds.

  Returns:
    A Tensor with num_clusters random rows of data.
  """
  assert isinstance(data, list)
  num_data = math_ops.add_n([array_ops.shape(inp)[0] for inp in data])
  with ops.control_dependencies(
      [check_ops.assert_less_equal(num_clusters, num_data)]):
    indices = random_ops.random_uniform(
        [num_clusters],
        minval=0,
        maxval=math_ops.cast(num_data, dtypes.int64),
        seed=random_seed,
        dtype=dtypes.int64)
  indices %= math_ops.cast(num_data, dtypes.int64)
  clusters_init = embedding_lookup(data, indices, partition_strategy='div')
  return clusters_init