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Python gen_math_ops._prod方法代码示例

本文整理汇总了Python中tensorflow.python.ops.gen_math_ops._prod方法的典型用法代码示例。如果您正苦于以下问题:Python gen_math_ops._prod方法的具体用法?Python gen_math_ops._prod怎么用?Python gen_math_ops._prod使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在tensorflow.python.ops.gen_math_ops的用法示例。


在下文中一共展示了gen_math_ops._prod方法的6个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: size_internal

# 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 _prod [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) 
开发者ID:ryfeus,项目名称:lambda-packs,代码行数:27,代码来源:array_ops.py

示例2: size_internal

# 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 _prod [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) 
开发者ID:tobegit3hub,项目名称:deep_image_model,代码行数:27,代码来源:array_ops.py

示例3: reduce_prod

# 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 _prod [as 别名]
def reduce_prod(input_tensor, reduction_indices=None, keep_dims=False,
                name=None):
  """Computes the product 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._prod(input_tensor, _ReductionDims(input_tensor,
                                                         reduction_indices),
                            keep_dims, name=name) 
开发者ID:tobegit3hub,项目名称:deep_image_model,代码行数:27,代码来源:math_ops.py

示例4: size_internal

# 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 _prod [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) 
开发者ID:PacktPublishing,项目名称:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda,代码行数:27,代码来源:array_ops.py

示例5: reduce_prod

# 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 _prod [as 别名]
def reduce_prod(input_tensor,
                axis=None,
                keep_dims=False,
                name=None,
                reduction_indices=None):
  """Computes the product 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.prod
  @end_compatibility
  """
  return gen_math_ops._prod(
      input_tensor,
      _ReductionDims(input_tensor, axis, reduction_indices),
      keep_dims,
      name=name) 
开发者ID:ryfeus,项目名称:lambda-packs,代码行数:37,代码来源:math_ops.py

示例6: reduce_prod

# 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 _prod [as 别名]
def reduce_prod(input_tensor,
                axis=None,
                keep_dims=False,
                name=None,
                reduction_indices=None):
  """Computes the product 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.prod
  @end_compatibility
  """
  return gen_math_ops._prod(
      input_tensor,
      _ReductionDims(input_tensor, axis, reduction_indices),
      keep_dims,
      name=name) 
开发者ID:PacktPublishing,项目名称:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda,代码行数:38,代码来源:math_ops.py


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