Python gen_math_ops.minimum方法代碼示例

# 需要導入模塊: from tensorflow.python.ops import gen_math_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_math_ops import minimum [as 別名]
def reduce_min(input_tensor, reduction_indices=None, keep_dims=False,
               name=None):
  """Computes the minimum of elements across dimensions of a tensor.

  Reduces `input_tensor` along the dimensions given in `reduction_indices`.
  Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
  entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
  are retained with length 1.

  If `reduction_indices` has no entries, all dimensions are reduced, and a
  tensor with a single element is returned.

  Args:
    input_tensor: The tensor to reduce. Should have numeric type.
    reduction_indices: The dimensions to reduce. If `None` (the default),
      reduces all dimensions.
    keep_dims: If true, retains reduced dimensions with length 1.
    name: A name for the operation (optional).

  Returns:
    The reduced tensor.
  """
  return gen_math_ops._min(input_tensor, _ReductionDims(input_tensor,
                                                        reduction_indices),
                           keep_dims, name=name) 

# 需要導入模塊: from tensorflow.python.ops import gen_math_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_math_ops import minimum [as 別名]
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) 

# 需要導入模塊: from tensorflow.python.ops import gen_math_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_math_ops import minimum [as 別名]
def reduce_min(input_tensor,
               axis=None,
               keep_dims=False,
               name=None,
               reduction_indices=None):
  """Computes the minimum of elements across dimensions of a tensor.

  Reduces `input_tensor` along the dimensions given in `axis`.
  Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
  entry in `axis`. If `keep_dims` is true, the reduced dimensions
  are retained with length 1.

  If `axis` has no entries, all dimensions are reduced, and a
  tensor with a single element is returned.

  Args:
    input_tensor: The tensor to reduce. Should have numeric type.
    axis: The dimensions to reduce. If `None` (the default),
      reduces all dimensions.
    keep_dims: If true, retains reduced dimensions with length 1.
    name: A name for the operation (optional).
    reduction_indices: The old (deprecated) name for axis.

  Returns:
    The reduced tensor.

  @compatibility(numpy)
  Equivalent to np.min
  @end_compatibility
  """
  return gen_math_ops._min(
      input_tensor,
      _ReductionDims(input_tensor, axis, reduction_indices),
      keep_dims,
      name=name) 

# 需要導入模塊: from tensorflow.python.ops import gen_math_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_math_ops import minimum [as 別名]
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) 

# 需要導入模塊: from tensorflow.python.ops import gen_math_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_math_ops import minimum [as 別名]
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) 

# 需要導入模塊: from tensorflow.python.ops import gen_math_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_math_ops import minimum [as 別名]
def __sample_w_rej(self, n, seed):
        c = math_ops.sqrt((4 * (self.scale ** 2)) + (self.__mf - 1) ** 2)
        b_true = (-2 * self.scale + c) / (self.__mf - 1)
        
        # using Taylor approximation with a smooth swift from 10 < scale < 11
        # to avoid numerical errors for large scale
        b_app = (self.__mf - 1) / (4 * self.scale)
        s = gen_math_ops.minimum(gen_math_ops.maximum(0., self.scale - 10), 1.)
        b = b_app * s + b_true * (1 - s)
        
        a = (self.__mf - 1 + 2 * self.scale + c) / 4
        d = (4 * a * b) / (1 + b) - (self.__mf - 1) * math_ops.log(self.__mf - 1)

        self.__b, (self.__e, self.__w) = b, self.__while_loop(b, a, d, n, seed)
        return self.__w 

# 需要導入模塊: from tensorflow.python.ops import gen_math_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_math_ops import minimum [as 別名]
def reduce_min(input_tensor,
               axis=None,
               keep_dims=False,
               name=None,
               reduction_indices=None):
  """Computes the minimum of elements across dimensions of a tensor.

  Reduces `input_tensor` along the dimensions given in `axis`.
  Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
  entry in `axis`. If `keep_dims` is true, the reduced dimensions
  are retained with length 1.

  If `axis` has no entries, all dimensions are reduced, and a
  tensor with a single element is returned.

  Args:
    input_tensor: The tensor to reduce. Should have numeric type.
    axis: The dimensions to reduce. If `None` (the default),
      reduces all dimensions. Must be in the range
      `[-rank(input_tensor), rank(input_tensor))`.
    keep_dims: If true, retains reduced dimensions with length 1.
    name: A name for the operation (optional).
    reduction_indices: The old (deprecated) name for axis.

  Returns:
    The reduced tensor.

  @compatibility(numpy)
  Equivalent to np.min
  @end_compatibility
  """
  return gen_math_ops._min(
      input_tensor,
      _ReductionDims(input_tensor, axis, reduction_indices),
      keep_dims,
      name=name) 

# 需要導入模塊: from tensorflow.python.ops import gen_math_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_math_ops import minimum [as 別名]
def bincount(arr,
             weights=None,
             minlength=None,
             maxlength=None,
             dtype=dtypes.int32):
  """Counts the number of occurrences of each value in an integer array.

  If `minlength` and `maxlength` are not given, returns a vector with length
  `tf.reduce_max(arr) + 1` if `arr` is non-empty, and length 0 otherwise.
  If `weights` are non-None, then index `i` of the output stores the sum of the
  value in `weights` at each index where the corresponding value in `arr` is
  `i`.

  Args:
    arr: An int32 tensor of non-negative values.
    weights: If non-None, must be the same shape as arr. For each value in
        `arr`, the bin will be incremented by the corresponding weight instead
        of 1.
    minlength: If given, ensures the output has length at least `minlength`,
        padding with zeros at the end if necessary.
    maxlength: If given, skips values in `arr` that are equal or greater than
        `maxlength`, ensuring that the output has length at most `maxlength`.
    dtype: If `weights` is None, determines the type of the output bins.

  Returns:
    A vector with the same dtype as `weights` or the given `dtype`. The bin
    values.
  """
  arr = ops.convert_to_tensor(arr, name="arr", dtype=dtypes.int32)
  array_is_nonempty = reduce_prod(array_ops.shape(arr)) > 0
  output_size = cast(array_is_nonempty, dtypes.int32) * (reduce_max(arr) + 1)
  if minlength is not None:
    minlength = ops.convert_to_tensor(
        minlength, name="minlength", dtype=dtypes.int32)
    output_size = gen_math_ops.maximum(minlength, output_size)
  if maxlength is not None:
    maxlength = ops.convert_to_tensor(
        maxlength, name="maxlength", dtype=dtypes.int32)
    output_size = gen_math_ops.minimum(maxlength, output_size)
  weights = (ops.convert_to_tensor(weights, name="weights")
             if weights is not None else constant_op.constant([], dtype))
  return gen_math_ops.bincount(arr, output_size, weights)