Python gen_array_ops.size方法代碼示例

# 需要導入模塊: from tensorflow.python.ops import gen_array_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_array_ops import size [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.ops import gen_array_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_array_ops import size [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.ops import gen_array_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_array_ops import size [as 別名]
def rank_internal(input, name=None, optimize=True):
  # pylint: disable=redefined-builtin
  """Returns the rank of a tensor.

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    optimize: if true, encode the rank as a constant when possible.

  Returns:
    A `Tensor` of type `int32`.
  """
  with ops.name_scope(name, "Rank", [input]) as name:
    if isinstance(
        input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
      return gen_array_ops.size(input.dense_shape, name=name)
    else:
      input_tensor = ops.convert_to_tensor(input)
      input_shape = input_tensor.get_shape()
      if optimize and input_shape.ndims is not None:
        return constant(input_shape.ndims, dtypes.int32, name=name)
      return gen_array_ops.rank(input, name=name) 

# 需要導入模塊: from tensorflow.python.ops import gen_array_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_array_ops import size [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.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.ops import gen_array_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_array_ops import size [as 別名]
def rank_internal(input, name=None, optimize=True):
  # pylint: disable=redefined-builtin
  """Returns the rank of a tensor.

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    optimize: if true, encode the rank as a constant when possible.

  Returns:
    A `Tensor` of type `int32`.
  """
  with ops.name_scope(name, "Rank", [input]) as name:
    if isinstance(
        input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
      return gen_array_ops.size(input.shape, name=name)
    else:
      input_tensor = ops.convert_to_tensor(input)
      input_shape = input_tensor.get_shape()
      if optimize and input_shape.ndims is not None:
        return constant(input_shape.ndims, dtypes.int32, name=name)
      return gen_array_ops.rank(input, name=name) 

# 需要導入模塊: from tensorflow.python.ops import gen_array_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_array_ops import size [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 = tf.constant([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]])
  tf.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.ops import gen_array_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_array_ops import size [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.ops import gen_array_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_array_ops import size [as 別名]
def rank_internal(input, name=None, optimize=True):
  # pylint: disable=redefined-builtin
  """Returns the rank of a tensor.

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    optimize: if true, encode the rank as a constant when possible.

  Returns:
    A `Tensor` of type `int32`.
  """
  with ops.name_scope(name, "Rank", [input]) as name:
    if isinstance(input, (sparse_tensor.SparseTensor,
                          sparse_tensor.SparseTensorValue)):
      return gen_array_ops.size(input.dense_shape, name=name)
    else:
      input_tensor = ops.convert_to_tensor(input)
      input_shape = input_tensor.get_shape()
      if optimize and input_shape.ndims is not None:
        return constant(input_shape.ndims, dtypes.int32, name=name)
      return gen_array_ops.rank(input, name=name) 

# 需要導入模塊: from tensorflow.python.ops import gen_array_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_array_ops import size [as 別名]
def _compute_size_of_strided_dim(shrink, spec, size):
  """Computes the size of a single strided slice dimension."""

  unknown = None  # Document what None means here.
  use_full_range = None  # Document other use of None.
  # if this is a shrink axis (i.e. a non-range index)
  # it either will produce an error or return 1
  if shrink:
    return 1
  if size is unknown or size.value is unknown:
    return unknown
  size = size.value
  stride = spec.step
  if stride is not unknown:
    if stride == 0:
      return unknown
    stride = spec.step
    valid_range = [0, size] if stride > 0 else [-1, size - 1]

    # PEP-8 naming
    # pylint: disable=invalid-name
    def canonical(x, c):
      if x is use_full_range:
        return valid_range[c] if stride > 0 else valid_range[(c + 1) & 1]
      else:
        x_fwd = size + x if x < 0 else x  # make negative indices positive
        return max(valid_range[0], min(valid_range[1], x_fwd))

    begin = canonical(spec.start, 0)
    end = canonical(spec.stop, 1)
    interval_length = end - begin
    if interval_length == 0 or ((interval_length < 0) != (stride < 0)):
      return 0
    else:
      remainder = 1 if interval_length % stride != 0 else 0
      return interval_length // stride + remainder
  else:
    return unknown  # unknown because stride is unknown 

# 需要導入模塊: from tensorflow.python.ops import gen_array_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_array_ops import size [as 別名]
def squeeze(input, squeeze_dims=None, name=None):
  # pylint: disable=redefined-builtin
  """Removes dimensions of size 1 from the shape of a tensor.

  Given a tensor `input`, this operation returns a tensor of the same type with
  all dimensions of size 1 removed. If you don't want to remove all size 1
  dimensions, you can remove specific size 1 dimensions by specifying
  `squeeze_dims`.

  For example:

  ```prettyprint
  # 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
  shape(squeeze(t)) ==> [2, 3]
            ```

  Or, to remove specific size 1 dimensions:

  ```prettyprint
  # 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
  shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1]
  ```

  Args:
    input: A `Tensor`. The `input` to squeeze.
    squeeze_dims: An optional list of `ints`. Defaults to `[]`.
      If specified, only squeezes the dimensions listed. The dimension
      index starts at 0. It is an error to squeeze a dimension that is not 1.
    name: A name for the operation (optional).

  Returns:
    A `Tensor`. Has the same type as `input`.
    Contains the same data as `input`, but has one or more dimensions of
    size 1 removed.
  """
  if np.isscalar(squeeze_dims):
    squeeze_dims = [squeeze_dims]
  return gen_array_ops._squeeze(input, squeeze_dims, name) 

# 需要導入模塊: from tensorflow.python.ops import gen_array_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_array_ops import size [as 別名]
def expand_dims(input, axis=None, name=None, dim=None):
  """Inserts a dimension of 1 into a tensor's shape.

  Given a tensor `input`, this operation inserts a dimension of 1 at the
  dimension index `axis` of `input`'s shape. The dimension index `axis` starts
  at zero; if you specify a negative number for `axis` it is counted backward
  from the end.

  This operation is useful if you want to add a batch dimension to a single
  element. For example, if you have a single image of shape `[height, width,
  channels]`, you can make it a batch of 1 image with `expand_dims(image, 0)`,
  which will make the shape `[1, height, width, channels]`.

  Other examples:

  ```python
  # 't' is a tensor of shape [2]
  shape(expand_dims(t, 0)) ==> [1, 2]
  shape(expand_dims(t, 1)) ==> [2, 1]
  shape(expand_dims(t, -1)) ==> [2, 1]

  # 't2' is a tensor of shape [2, 3, 5]
  shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5]
  shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5]
  shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1]
  ```

  This operation requires that:

  `-1-input.dims() <= dim <= input.dims()`

  This operation is related to `squeeze()`, which removes dimensions of
  size 1.

  Args:
    input: A `Tensor`.
    axis: 0-D (scalar). Specifies the dimension index at which to
      expand the shape of `input`.
    name: The name of the output `Tensor`.
    dim: 0-D (scalar). Equivalent to `axis`, to be deprecated.

  Returns:
    A `Tensor` with the same data as `input`, but its shape has an additional
    dimension of size 1 added.

  Raises:
    ValueError: if both `dim` and `axis` are specified.
  """
  # TODO(aselle): Remove argument dim
  if dim is not None:
    if axis is not None:
      raise ValueError("can't specify both 'dim' and 'axis'")
    axis = dim
  return gen_array_ops._expand_dims(input, axis, name)
# pylint: enable=redefined-builtin,protected-access


# Aliases for some automatically-generated names.
# pylint: disable=protected-access 

# 需要導入模塊: from tensorflow.python.ops import gen_array_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_array_ops import size [as 別名]
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 

# 需要導入模塊: from tensorflow.python.ops import gen_array_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_array_ops import size [as 別名]
def sequence_mask(lengths, maxlen=None, dtype=dtypes.bool, name=None):
  """Return a mask tensor representing the first N positions of each row.

  Example:

  ```python
  tf.sequence_mask([1, 3, 2], 5) =
    [[True, False, False, False, False],
     [True, True, True, False, False],
     [True, True, False, False, False]]
  ```

  Args:
    lengths: 1D integer tensor, all its values < maxlen.
    maxlen: scalar integer tensor, maximum length of each row. Default: use
            maximum over lengths.
    dtype: output type of the resulting tensor.
    name: name of the op.
  Returns:
    A 2D mask tensor, as shown in the example above, cast to specified dtype.

  Raises:
    ValueError: if the arguments have invalid rank.
  """
  with ops.name_scope(name, "SequenceMask", [lengths, maxlen]):
    lengths = ops.convert_to_tensor(lengths)
    if lengths.get_shape().ndims != 1:
      raise ValueError("lengths must be 1D for sequence_mask")

    if maxlen is None:
      maxlen = gen_math_ops._max(lengths, [0])
    else:
      maxlen = ops.convert_to_tensor(maxlen)
    if maxlen.get_shape().ndims != 0:
      raise ValueError("maxlen must be scalar for sequence_mask")

    # The basic idea is to compare a range row vector of size maxlen:
    # [0, 1, 2, 3, 4]
    # to length as a matrix with 1 column: [[1], [3], [2]].
    # Because of broadcasting on both arguments this comparison results
    # in a matrix of size (len(lengths), maxlen)
    row_vector = gen_math_ops._range(constant(0, maxlen.dtype),
                                     maxlen,
                                     constant(1, maxlen.dtype))
    # Since maxlen >= max(lengths), it is safe to use maxlen as a cast
    # authoritative type. Whenever maxlen fits into tf.int32, so do the lengths.
    matrix = gen_math_ops.cast(expand_dims(lengths, 1), maxlen.dtype)
    result = row_vector < matrix

    if dtype is None or result.dtype.base_dtype == dtype.base_dtype:
      return result
    else:
      return gen_math_ops.cast(result, dtype) 

# 需要導入模塊: from tensorflow.python.ops import gen_array_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_array_ops import size [as 別名]
def squeeze(input, axis=None, name=None, squeeze_dims=None):
  # pylint: disable=redefined-builtin
  """Removes dimensions of size 1 from the shape of a tensor.

  Given a tensor `input`, this operation returns a tensor of the same type with
  all dimensions of size 1 removed. If you don't want to remove all size 1
  dimensions, you can remove specific size 1 dimensions by specifying
  `axis`.

  For example:

  ```prettyprint
  # 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
  shape(squeeze(t)) ==> [2, 3]
  ```

  Or, to remove specific size 1 dimensions:

  ```prettyprint
  # 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
  shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1]
  ```

  Args:
    input: A `Tensor`. The `input` to squeeze.
    axis: An optional list of `ints`. Defaults to `[]`.
      If specified, only squeezes the dimensions listed. The dimension
      index starts at 0. It is an error to squeeze a dimension that is not 1.
    name: A name for the operation (optional).
    squeeze_dims: Deprecated keyword argument that is now axis.

  Returns:
    A `Tensor`. Has the same type as `input`.
    Contains the same data as `input`, but has one or more dimensions of
    size 1 removed.

  Raises:
    ValueError: When both `squeeze_dims` and `axis` are specified.
  """
  if squeeze_dims is not None:
    if axis is not None:
      raise ValueError("Cannot specify both 'squeeze_dims' and 'axis'")
    axis = squeeze_dims
  if np.isscalar(axis):
    axis = [axis]
  return gen_array_ops._squeeze(input, axis, name) 

# 需要導入模塊: from tensorflow.python.ops import gen_array_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_array_ops import size [as 別名]
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.")