Python gen_math_ops._max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 _max [as 别名]
def reduce_max(input_tensor, reduction_indices=None, keep_dims=False,
               name=None):
  """Computes the maximum 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._max(input_tensor, _ReductionDims(input_tensor,
                                                        reduction_indices),
                           keep_dims, 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 _max [as 别名]
def reduce_max(input_tensor,
               axis=None,
               keep_dims=False,
               name=None,
               reduction_indices=None):
  """Computes the maximum 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.max
  @end_compatibility
  """
  return gen_math_ops._max(
      input_tensor,
      _ReductionDims(input_tensor, axis, reduction_indices),
      keep_dims,
      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 _max [as 别名]
def reduce_max(input_tensor,
               axis=None,
               keep_dims=False,
               name=None,
               reduction_indices=None):
  """Computes the maximum 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.max
  @end_compatibility
  """
  return gen_math_ops._max(
      input_tensor,
      _ReductionDims(input_tensor, axis, reduction_indices),
      keep_dims,
      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 _max [as 别名]
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 _max [as 别名]
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)
    result = gen_math_ops._range(0, maxlen, 1) < expand_dims(lengths, 1)
    if dtype is None or result.dtype.base_dtype == dtype.base_dtype:
      return result
    else:
      return gen_math_ops.cast(result, dtype) 

# 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 _max [as 别名]
def sequence_mask(lengths, maxlen=None, dtype=dtypes.bool, name=None):
  """Returns a mask tensor representing the first N positions of each cell.

  If `lengths` has shape `[d_1, d_2, ..., d_n]` the resulting tensor `mask` has
  dtype `dtype` and shape `[d_1, d_2, ..., d_n, maxlen]`, with

  ```
  mask[i_1, i_2, ..., i_n, j] = (j < lengths[i_1, i_2, ..., i_n])
  ```

  Examples:

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

  tf.sequence_mask([[1, 3],[2,0]])  # [[[True, False, False],
                                    #   [True, True, True]],
                                    #  [[True, True, False],
                                    #   [False, False, False]]]
  ```

  Args:
    lengths: integer tensor, all its values <= maxlen.
    maxlen: scalar integer tensor, size of last dimension of returned tensor.
      Default is the maximum value in `lengths`.
    dtype: output type of the resulting tensor.
    name: name of the op.
  Returns:
    A mask tensor of shape `lengths.shape + (maxlen,)`, cast to specified dtype.
  Raises:
    ValueError: if `maxlen` is not a scalar.
  """
  with ops.name_scope(name, "SequenceMask", [lengths, maxlen]):
    lengths = ops.convert_to_tensor(lengths)

    if maxlen is None:
      maxlen = gen_math_ops._max(lengths, _all_dimensions(lengths))
    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)