Python gen_math_ops.cast方法代码示例

# 需要导入模块: from tensorflow.python.ops import gen_math_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_math_ops import cast [as 别名]
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) 

# 需要导入模块: from tensorflow.python.ops import gen_math_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_math_ops import cast [as 别名]
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) 

# 需要导入模块: from tensorflow.python.ops import gen_math_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_math_ops import cast [as 别名]
def _sparse_dense_truediv(sp_indices, sp_values, sp_shape, y, name=None):
  """Internal helper function for 'sp_t / dense_t'."""
  with ops.name_scope(name, "truediv", [sp_indices, sp_values, sp_shape,
                                        y]) as name:
    sp_values = ops.convert_to_tensor(sp_values, name="sp_values")
    y = ops.convert_to_tensor(y, name="y")
    x_dtype = sp_values.dtype.base_dtype
    y_dtype = y.dtype.base_dtype
    if x_dtype != y_dtype:
      raise TypeError("x and y must have the same dtype, got %r != %r" %
                      (x_dtype, y_dtype))
    try:
      dtype = _TRUEDIV_TABLE[x_dtype]
    except KeyError:
      raise TypeError("Invalid dtype %r in __truediv__" % x_dtype)
    if dtype is not None:
      sp_values = cast(sp_values, dtype)
      y = cast(y, dtype)
    return gen_sparse_ops.sparse_dense_cwise_div(
        sp_indices, sp_values, sp_shape, y, name=name) 

# 需要导入模块: from tensorflow.python.ops import gen_math_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_math_ops import cast [as 别名]
def _sparse_dense_truediv(sp_indices, sp_values, sp_shape, y, name=None):
  """Internal helper function for 'sp_t / dense_t'."""
  with ops.name_scope(name, "truediv",
                      [sp_indices, sp_values, sp_shape, y]) as name:
    sp_values = ops.convert_to_tensor(sp_values, name="sp_values")
    y = ops.convert_to_tensor(y, name="y")
    x_dtype = sp_values.dtype.base_dtype
    y_dtype = y.dtype.base_dtype
    if x_dtype != y_dtype:
      raise TypeError("x and y must have the same dtype, got %r != %r" %
                      (x_dtype, y_dtype))
    try:
      dtype = _TRUEDIV_TABLE[x_dtype]
    except KeyError:
      raise TypeError("Invalid dtype %r in __truediv__" % x_dtype)
    if dtype is not None:
      sp_values = cast(sp_values, dtype)
      y = cast(y, dtype)
    return gen_sparse_ops.sparse_dense_cwise_div(
        sp_indices, sp_values, sp_shape, y, name=name) 

# 需要导入模块: from tensorflow.python.ops import gen_math_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_math_ops import cast [as 别名]
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.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) 

# 需要导入模块: from tensorflow.python.ops import gen_math_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_math_ops import cast [as 别名]
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) 

# 需要导入模块: from tensorflow.python.ops import gen_math_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_math_ops import cast [as 别名]
def _sparse_dense_truediv(sp_indices, sp_values, sp_shape, y, name=None):
  """Internal helper function for 'sp_t / dense_t'."""
  with ops.name_scope(name, "truediv",
                      [sp_indices, sp_values, sp_shape, y]) as name:
    sp_values = ops.convert_to_tensor(sp_values, name="sp_values")
    y = ops.convert_to_tensor(y, name="y")
    x_dtype = sp_values.dtype.base_dtype
    y_dtype = y.dtype.base_dtype
    if x_dtype != y_dtype:
      raise TypeError("x and y must have the same dtype, got %r != %r" %
                      (x_dtype, y_dtype))
    try:
      dtype = _TRUEDIV_TABLE[x_dtype]
    except KeyError:
      raise TypeError("Invalid dtype %r in __truediv__" % x_dtype)
    if dtype is not None:
      sp_values = cast(sp_values, dtype)
      y = cast(y, dtype)
    return gen_sparse_ops.sparse_dense_cwise_div(sp_indices, sp_values,
                                                 sp_shape, y, name=name) 

# 需要导入模块: from tensorflow.python.ops import gen_math_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_math_ops import cast [as 别名]
def reduced_shape(input_shape, axes):
  """Helper function for reduction ops.

  Args:
    input_shape: 1-D Tensor, the shape of the Tensor being reduced.
    axes: 1-D Tensor, the reduction axes.
  Returns:
    A 1-D Tensor, the output shape as if keep_dims were set to True.
  """
  # Example:
  # cast needed for SparseTensor reductions
  input_shape = to_int32(input_shape)       # [2, 3, 5, 7]
  axes = to_int32(axes)                     # [1, 2]

  input_rank = array_ops.size(input_shape)  # 4
  axes = (axes + input_rank) % input_rank
  axes_shape = array_ops.shape(axes)        # [2]
  return gen_data_flow_ops.dynamic_stitch(  # [2, 1, 1, 7]
      [range(input_rank),                   # [0, 1, 2, 3]
       axes],                               # [1, 2]
      [input_shape,                         # [2, 3, 5, 7]
       array_ops.fill(axes_shape, 1)])      # [1, 1] 

# 需要导入模块: from tensorflow.python.ops import gen_math_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_math_ops import cast [as 别名]
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) 

# 需要导入模块: from tensorflow.python.ops import gen_math_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_math_ops import cast [as 别名]
def cast(x, dtype, name=None):
  """Casts a tensor to a new type.

  The operation casts `x` (in case of `Tensor`) or `x.values`
  (in case of `SparseTensor`) to `dtype`.

  For example:

  ```python
  # tensor `a` is [1.8, 2.2], dtype=tf.float
  tf.cast(a, tf.int32) ==> [1, 2]  # dtype=tf.int32
  ```

  Args:
    x: A `Tensor` or `SparseTensor`.
    dtype: The destination type.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` or `SparseTensor` with same shape as `x`.

  Raises:
    TypeError: If `x` cannot be cast to the `dtype`.
  """
  base_type = dtypes.as_dtype(dtype).base_dtype
  with ops.name_scope(name, "Cast", [x]) as name:
    if isinstance(x, sparse_tensor.SparseTensor):
      values_cast = cast(x.values, base_type, name=name)
      return sparse_tensor.SparseTensor(x.indices, values_cast, x.dense_shape)
    else:
      # TODO(touts): Handle what Josh said.
      #
      # Could return ops.convert_to_tensor(x, dtype=dtype, ...)  here, but that
      # allows some conversions that cast() can't do, e.g.  casting numbers to
      # strings.
      x = ops.convert_to_tensor(x, name="x")
      if x.dtype.base_dtype == base_type:
        return x
      return gen_math_ops.cast(x, base_type, name=name) 

# 需要导入模块: from tensorflow.python.ops import gen_math_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_math_ops import cast [as 别名]
def saturate_cast(value, dtype, name=None):
  """Performs a safe saturating cast of `value` to `dtype`.

  This function casts the input to `dtype` without applying any scaling.  If
  there is a danger that values would over or underflow in the cast, this op
  applies the appropriate clamping before the cast.

  Args:
    value: A `Tensor`.
    dtype: The desired output `DType`.
    name: A name for the operation (optional).

  Returns:
    `value` safely cast to `dtype`.
  """
  # When casting to a type with smaller representable range, clamp.
  # Note that this covers casting to unsigned types as well.
  with ops.name_scope(name, "saturate_cast", [value]) as name:
    value = ops.convert_to_tensor(value, name="value")
    dtype = dtypes.as_dtype(dtype).base_dtype
    if value.dtype.min < dtype.min:
      value = gen_math_ops.maximum(value,
                                   ops.convert_to_tensor(
                                       dtype.min, dtype=value.dtype,
                                       name="min"))
    if value.dtype.max > dtype.max:
      value = gen_math_ops.minimum(value,
                                   ops.convert_to_tensor(
                                       dtype.max, dtype=value.dtype,
                                       name="max"))
    return cast(value, dtype, name=name) 

# 需要导入模块: from tensorflow.python.ops import gen_math_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_math_ops import cast [as 别名]
def to_double(x, name="ToDouble"):
  """Casts a tensor to type `float64`.

  Args:
    x: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` or `SparseTensor` with same shape as `x` with type `float64`.

  Raises:
    TypeError: If `x` cannot be cast to the `float64`.
  """
  return cast(x, dtypes.float64, name=name) 

# 需要导入模块: from tensorflow.python.ops import gen_math_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_math_ops import cast [as 别名]
def to_int32(x, name="ToInt32"):
  """Casts a tensor to type `int32`.

  Args:
    x: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` or `SparseTensor` with same shape as `x` with type `int32`.

  Raises:
    TypeError: If `x` cannot be cast to the `int32`.
  """
  return cast(x, dtypes.int32, name=name) 

# 需要导入模块: from tensorflow.python.ops import gen_math_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_math_ops import cast [as 别名]
def to_int64(x, name="ToInt64"):
  """Casts a tensor to type `int64`.

  Args:
    x: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` or `SparseTensor` with same shape as `x` with type `int64`.

  Raises:
    TypeError: If `x` cannot be cast to the `int64`.
  """
  return cast(x, dtypes.int64, name=name) 

# 需要导入模块: from tensorflow.python.ops import gen_math_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_math_ops import cast [as 别名]
def to_bfloat16(x, name="ToBFloat16"):
  """Casts a tensor to type `bfloat16`.

  Args:
    x: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` or `SparseTensor` with same shape as `x` with type `bfloat16`.

  Raises:
    TypeError: If `x` cannot be cast to the `bfloat16`.
  """
  return cast(x, dtypes.bfloat16, name=name)