Python gen_array_ops.where方法代码示例

# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import where [as 别名]
def slice(input_, begin, size, name=None):
  # pylint: disable=redefined-builtin
  """Extracts a slice from a tensor.

  This operation extracts a slice of size `size` from a tensor `input` starting
  at the location specified by `begin`. The slice `size` is represented as a
  tensor shape, where `size[i]` is the number of elements of the 'i'th dimension
  of `input` that you want to slice. The starting location (`begin`) for the
  slice is represented as an offset in each dimension of `input`. In other
  words, `begin[i]` is the offset into the 'i'th dimension of `input` that you
  want to slice from.

  `begin` is zero-based; `size` is one-based. If `size[i]` is -1,
  all remaining elements in dimension i are included in the
  slice. In other words, this is equivalent to setting:

  `size[i] = input.dim_size(i) - begin[i]`

  This operation requires that:

  `0 <= begin[i] <= begin[i] + size[i] <= Di  for i in [0, n]`

  For example:

  ```python
  # 'input' is [[[1, 1, 1], [2, 2, 2]],
  #             [[3, 3, 3], [4, 4, 4]],
  #             [[5, 5, 5], [6, 6, 6]]]
  tf.slice(input, [1, 0, 0], [1, 1, 3]) ==> [[[3, 3, 3]]]
  tf.slice(input, [1, 0, 0], [1, 2, 3]) ==> [[[3, 3, 3],
                                              [4, 4, 4]]]
  tf.slice(input, [1, 0, 0], [2, 1, 3]) ==> [[[3, 3, 3]],
                                             [[5, 5, 5]]]
  ```

  Args:
    input_: A `Tensor`.
    begin: An `int32` or `int64` `Tensor`.
    size: An `int32` or `int64` `Tensor`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` the same type as `input`.
  """
  return gen_array_ops._slice(input_, begin, size, name=name)


# pylint: disable=invalid-name 

# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import where [as 别名]
def _autopacking_helper(list_or_tuple, dtype, name):
  """Converts the given list or tuple to a tensor by packing.

  Args:
    list_or_tuple: A (possibly nested) list or tuple containing a tensor.
    dtype: The element type of the returned tensor.
    name: A name for the returned tensor.

  Returns:
    A `tf.Tensor` with value equivalent to `list_or_tuple`.
  """
  must_pack = False
  converted_elems = []
  with ops.name_scope(name) as scope:
    for i, elem in enumerate(list_or_tuple):
      if ops.is_dense_tensor_like(elem):
        if dtype is not None and elem.dtype.base_dtype != dtype:
          raise TypeError(
              "Cannot convert a list containing a tensor of dtype "
              "%s to %s (Tensor is: %r)" % (elem.dtype, dtype, elem))
        converted_elems.append(elem)
        must_pack = True
      elif isinstance(elem, (list, tuple)):
        converted_elem = _autopacking_helper(elem, dtype, str(i))
        if ops.is_dense_tensor_like(converted_elem):
          must_pack = True
        converted_elems.append(converted_elem)
      else:
        converted_elems.append(elem)
    if must_pack:
      elems_as_tensors = []
      for i, elem in enumerate(converted_elems):
        if ops.is_dense_tensor_like(elem):
          elems_as_tensors.append(elem)
        else:
          # NOTE(mrry): This is inefficient, but it enables us to
          # handle the case where the list arguments are other
          # convertible-to-tensor types, such as numpy arrays.
          elems_as_tensors.append(
              constant_op.constant(elem, dtype=dtype, name=str(i)))
      return gen_array_ops._pack(elems_as_tensors, name=scope)
    else:
      return converted_elems 

# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import where [as 别名]
def transpose(a, perm=None, name="transpose"):
  """Transposes `a`. Permutes the dimensions according to `perm`.

  The returned tensor's dimension i will correspond to the input dimension
  `perm[i]`. If `perm` is not given, it is set to (n-1...0), where n is
  the rank of the input tensor. Hence by default, this operation performs a
  regular matrix transpose on 2-D input Tensors.

  For example:

  ```python
  # 'x' is [[1 2 3]
  #         [4 5 6]]
  tf.transpose(x) ==> [[1 4]
                       [2 5]
                       [3 6]]

  # Equivalently
  tf.transpose(x, perm=[1, 0]) ==> [[1 4]
                                    [2 5]
                                    [3 6]]

  # 'perm' is more useful for n-dimensional tensors, for n > 2
  # 'x' is   [[[1  2  3]
  #            [4  5  6]]
  #           [[7  8  9]
  #            [10 11 12]]]
  # Take the transpose of the matrices in dimension-0
  tf.transpose(x, perm=[0, 2, 1]) ==> [[[1  4]
                                        [2  5]
                                        [3  6]]

                                       [[7 10]
                                        [8 11]
                                        [9 12]]]
  ```

  Args:
    a: A `Tensor`.
    perm: A permutation of the dimensions of `a`.
    name: A name for the operation (optional).

  Returns:
    A transposed `Tensor`.
  """
  with ops.name_scope(name, "transpose", [a]) as name:
    if perm is None:
      rank = gen_array_ops.rank(a)
      perm = (rank - 1) - gen_math_ops._range(0, rank, 1)
      ret = gen_array_ops.transpose(a, perm, name=name)
      # NOTE(mrry): Setting the shape explicitly because
      #   reverse is not handled by the shape function.
      input_shape = ret.op.inputs[0].get_shape().dims
      if input_shape is not None:
        ret.set_shape(input_shape[::-1])
    else:
      ret = gen_array_ops.transpose(a, perm, name=name)
    return ret


# pylint: disable=invalid-name 

# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import where [as 别名]
def where(condition, x=None, y=None, name=None):
  """Return the elements, either from `x` or `y`, depending on the `condition`.

  If both `x` and `y` are None, then this operation returns the coordinates of
  true elements of `condition`.  The coordinates are returned in a 2-D tensor
  where the first dimension (rows) represents the number of true elements, and
  the second dimension (columns) represents the coordinates of the true
  elements. Keep in mind, the shape of the output tensor can vary depending on
  how many true values there are in input. Indices are output in row-major
  order.

  If both non-None, `x` and `y` must have the same shape.
  The `condition` tensor must be a scalar if `x` and `y` are scalar.
  If `x` and `y` are vectors of higher rank, then `condition` must be either a
  vector with size matching the first dimension of `x`, or must have the same
  shape as `x`.

  The `condition` tensor acts as a mask that chooses, based on the value at each
  element, whether the corresponding element / row in the output should be taken
  from `x` (if true) or `y` (if false).

  If `condition` is a vector and `x` and `y` are higher rank matrices, then it
  chooses which row (outer dimension) to copy from `x` and `y`. If `condition`
  has the same shape as `x` and `y`, then it chooses which element to copy from
  `x` and `y`.

  Args:
    condition: A `Tensor` of type `bool`
    x: A Tensor which may have the same shape as `condition`. If `condition` is
      rank 1, `x` may have higher rank, but its first dimension must match the
      size of `condition`.
    y: A `tensor` with the same shape and type as `x`.
    name: A name of the operation (optional)

  Returns:
    A `Tensor` with the same type and shape as `x`, `y` if they are non-None.
    A `Tensor` with shape `(num_true, dim_size(condition))`.

  Raises:
    ValueError: When exactly one of `x` or `y` is non-None.
  """
  if x is None and y is None:
    return gen_array_ops.where(input=condition, name=name)
  elif x is not None and y is not None:
    return gen_math_ops._select(condition=condition, t=x, e=y, name=name)
  else:
    raise ValueError("x and y must both be non-None or both be None.") 

# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import where [as 别名]
def where(condition, x=None, y=None, name=None):
  """Return the elements, either from `x` or `y`, depending on the `condition`.

  If both `x` and `y` are None, then this operation returns the coordinates of
  true elements of `condition`.  The coordinates are returned in a 2-D tensor
  where the first dimension (rows) represents the number of true elements, and
  the second dimension (columns) represents the coordinates of the true
  elements. Keep in mind, the shape of the output tensor can vary depending on
  how many true values there are in input. Indices are output in row-major
  order.

  If both non-None, `x` and `y` must have the same shape.
  The `condition` tensor must be a scalar if `x` and `y` are scalar.
  If `x` and `y` are vectors or higher rank, then `condition` must be either a
  vector with size matching the first dimension of `x`, or must have the same
  shape as `x`.

  The `condition` tensor acts as a mask that chooses, based on the value at each
  element, whether the corresponding element / row in the output should be taken
  from `x` (if true) or `y` (if false).

  If `condition` is a vector and `x` and `y` are higher rank matrices, then it
  chooses which row (outer dimension) to copy from `x` and `y`. If `condition`
  has the same shape as `x` and `y`, then it chooses which element to copy from
  `x` and `y`.

  Args:
    condition: A `Tensor` of type `bool`
    x: A Tensor which may have the same shape as `condition`. If `condition` is
      rank 1, `x` may have higher rank, but its first dimension must match the
      size of `condition`.
    y: A `tensor` with the same shape and type as `x`.
    name: A name of the operation (optional)

  Returns:
    A `Tensor` with the same type and shape as `x`, `y` if they are non-None.
    A `Tensor` with shape `(num_true, dim_size(condition))`.

  Raises:
    ValueError: When exactly one of `x` or `y` is non-None.
  """
  if x is None and y is None:
    return gen_array_ops.where(input=condition, name=name)
  elif x is not None and y is not None:
    return gen_math_ops._select(condition=condition, t=x, e=y, name=name)
  else:
    raise ValueError("x and y must both be non-None or both be None.") 

# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import where [as 别名]
def where(condition, x=None, y=None, name=None):
  """Return the elements, either from `x` or `y`, depending on the `condition`.

  If both `x` and `y` are None, then this operation returns the coordinates of
  true elements of `condition`.  The coordinates are returned in a 2-D tensor
  where the first dimension (rows) represents the number of true elements, and
  the second dimension (columns) represents the coordinates of the true
  elements. Keep in mind, the shape of the output tensor can vary depending on
  how many true values there are in input. Indices are output in row-major
  order.

  If both non-None, `x` and `y` must have the same shape.
  The `condition` tensor must be a scalar if `x` and `y` are scalar.
  If `x` and `y` are vectors or higher rank, then `condition` must be either a
  vector with size matching the first dimension of `x`, or must have the same
  shape as `x`.

  The `condition` tensor acts as a mask that chooses, based on the value at each
  element, whether the corresponding element / row in the output should be taken
  from `x` (if true) or `y` (if false).

  If `condition` is a vector and `x` and `y` are higher rank matrices, then it
  chooses which row (outer dimension) to copy from `x` and `y`. If `condition`
  has the same shape as `x` and `y`, then it chooses which element to copy from
  `x` and `y`.

  Args:
    condition: A `Tensor` of type `bool`
    x: A Tensor which may have the same shape as `condition`. If `condition` is
      rank 1, `x` may have higher rank, but its first dimension must match the
      size of `condition`.
    y: A `tensor` with the same shape and type as `x`.
    name: A name of the operation (optional)

  Returns:
    A `Tensor` with the same type and shape as `x`, `y` if they are non-None.
    A `Tensor` with shape `(num_true, dim_size(condition))`.

  Raises:
    ValueError: When exactly one of `x` or `y` is non-None.
  """
  if x is None and y is None:
    return gen_array_ops.where(input=condition, name=name)
  elif x is not None and y is not None:
    return gen_math_ops.select(condition=condition, t=x, e=y, name=name)
  else:
    raise ValueError("x and y must both be non-None or both be None.") 

# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import where [as 别名]
def slice(input_, begin, size, name=None):
  # pylint: disable=redefined-builtin
  """Extracts a slice from a tensor.

  This operation extracts a slice of size `size` from a tensor `input` starting
  at the location specified by `begin`. The slice `size` is represented as a
  tensor shape, where `size[i]` is the number of elements of the 'i'th dimension
  of `input` that you want to slice. The starting location (`begin`) for the
  slice is represented as an offset in each dimension of `input`. In other
  words, `begin[i]` is the offset into the 'i'th dimension of `input` that you
  want to slice from.

  Note that @{tf.Tensor.__getitem__} is typically a more pythonic way to
  perform slices, as it allows you to write `foo[3:7, :-2]` instead of
  `tf.slice([3, 0], [4, foo.get_shape()[1]-2])`.

  `begin` is zero-based; `size` is one-based. If `size[i]` is -1,
  all remaining elements in dimension i are included in the
  slice. In other words, this is equivalent to setting:

  `size[i] = input.dim_size(i) - begin[i]`

  This operation requires that:

  `0 <= begin[i] <= begin[i] + size[i] <= Di  for i in [0, n]`

  For example:

  ```python
  t = tf.constant([[[1, 1, 1], [2, 2, 2]],
                   [[3, 3, 3], [4, 4, 4]],
                   [[5, 5, 5], [6, 6, 6]]])
  tf.slice(t, [1, 0, 0], [1, 1, 3])  # [[[3, 3, 3]]]
  tf.slice(t, [1, 0, 0], [1, 2, 3])  # [[[3, 3, 3],
                                     #   [4, 4, 4]]]
  tf.slice(t, [1, 0, 0], [2, 1, 3])  # [[[3, 3, 3]],
                                     #  [[5, 5, 5]]]
  ```

  Args:
    input_: A `Tensor`.
    begin: An `int32` or `int64` `Tensor`.
    size: An `int32` or `int64` `Tensor`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` the same type as `input`.
  """
  return gen_array_ops._slice(input_, begin, size, name=name)


# pylint: disable=invalid-name 

# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import where [as 别名]
def _autopacking_helper(list_or_tuple, dtype, name):
  """Converts the given list or tuple to a tensor by packing.

  Args:
    list_or_tuple: A (possibly nested) list or tuple containing a tensor.
    dtype: The element type of the returned tensor.
    name: A name for the returned tensor.

  Returns:
    A `tf.Tensor` with value equivalent to `list_or_tuple`.
  """
  must_pack = False
  converted_elems = []
  with ops.name_scope(name) as scope:
    for i, elem in enumerate(list_or_tuple):
      if ops.is_dense_tensor_like(elem):
        if dtype is not None and elem.dtype.base_dtype != dtype:
          raise TypeError("Cannot convert a list containing a tensor of dtype "
                          "%s to %s (Tensor is: %r)" % (elem.dtype, dtype,
                                                        elem))
        converted_elems.append(elem)
        must_pack = True
      elif isinstance(elem, (list, tuple)):
        converted_elem = _autopacking_helper(elem, dtype, str(i))
        if ops.is_dense_tensor_like(converted_elem):
          must_pack = True
        converted_elems.append(converted_elem)
      else:
        converted_elems.append(elem)
    if must_pack:
      elems_as_tensors = []
      for i, elem in enumerate(converted_elems):
        if ops.is_dense_tensor_like(elem):
          elems_as_tensors.append(elem)
        else:
          # NOTE(mrry): This is inefficient, but it enables us to
          # handle the case where the list arguments are other
          # convertible-to-tensor types, such as numpy arrays.
          elems_as_tensors.append(
              constant_op.constant(elem, dtype=dtype, name=str(i)))
      return gen_array_ops._pack(elems_as_tensors, name=scope)
    else:
      return converted_elems 

# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import where [as 别名]
def transpose(a, perm=None, name="transpose"):
  """Transposes `a`. Permutes the dimensions according to `perm`.

  The returned tensor's dimension i will correspond to the input dimension
  `perm[i]`. If `perm` is not given, it is set to (n-1...0), where n is
  the rank of the input tensor. Hence by default, this operation performs a
  regular matrix transpose on 2-D input Tensors.

  For example:

  ```python
  x = tf.constant([[1, 2, 3], [4, 5, 6]])
  tf.transpose(x)  # [[1, 4]
                   #  [2, 5]
                   #  [3, 6]]

  # Equivalently
  tf.transpose(x, perm=[1, 0])  # [[1, 4]
                                #  [2, 5]
                                #  [3, 6]]

  # 'perm' is more useful for n-dimensional tensors, for n > 2
  x = tf.constant([[[ 1,  2,  3],
                    [ 4,  5,  6]],
                   [[ 7,  8,  9],
                    [10, 11, 12]]])

  # Take the transpose of the matrices in dimension-0
  tf.transpose(x, perm=[0, 2, 1])  # [[[1,  4],
                                   #   [2,  5],
                                   #   [3,  6]],
                                   #  [[7, 10],
                                   #   [8, 11],
                                   #   [9, 12]]]
  ```

  Args:
    a: A `Tensor`.
    perm: A permutation of the dimensions of `a`.
    name: A name for the operation (optional).

  Returns:
    A transposed `Tensor`.
  """
  with ops.name_scope(name, "transpose", [a]) as name:
    if perm is None:
      rank = gen_array_ops.rank(a)
      perm = (rank - 1) - gen_math_ops._range(0, rank, 1)
      ret = gen_array_ops.transpose(a, perm, name=name)
      # NOTE(mrry): Setting the shape explicitly because
      #   reverse is not handled by the shape function.
      if context.in_graph_mode():
        input_shape = ret.op.inputs[0].get_shape().dims
        if input_shape is not None:
          ret.set_shape(input_shape[::-1])
    else:
      ret = gen_array_ops.transpose(a, perm, name=name)
    return ret


# pylint: disable=invalid-name