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Python gen_math_ops.log方法代码示例

本文整理汇总了Python中tensorflow.python.ops.gen_math_ops.log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.log方法的具体用法?Python gen_math_ops.log怎么用?Python gen_math_ops.log使用的例子?那么, 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.log方法的3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: log_sigmoid

# 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 log [as 别名]
def log_sigmoid(x, name=None):
  """Computes log sigmoid of `x` element-wise.

  Specifically, `y = log(1 / (1 + exp(-x)))`.  For numerical stability,
  we use `y = -tf.nn.softplus(-x)`.

  Args:
    x: A Tensor with type `float32` or `float64`.
    name: A name for the operation (optional).

  Returns:
    A Tensor with the same type as `x`.
  """
  with ops.name_scope(name, "LogSigmoid", [x]) as name:
    x = ops.convert_to_tensor(x, name="x")
    return gen_math_ops._neg(gen_nn_ops.softplus(-x), name=name) 
开发者ID:ryfeus,项目名称:lambda-packs,代码行数:18,代码来源:math_ops.py

示例2: reduce_logsumexp

# 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 log [as 别名]
def reduce_logsumexp(input_tensor,
                     axis=None,
                     keep_dims=False,
                     name=None,
                     reduction_indices=None):
  """Computes log(sum(exp(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.

  This function is more numerically stable than log(sum(exp(input))). It avoids
  overflows caused by taking the exp of large inputs and underflows caused by
  taking the log of small inputs.

  For example:

  ```python
  # 'x' is [[0, 0, 0]]
  #         [0, 0, 0]]
  tf.reduce_logsumexp(x) ==> log(6)
  tf.reduce_logsumexp(x, 0) ==> [log(2), log(2), log(2)]
  tf.reduce_logsumexp(x, 1) ==> [log(3), log(3)]
  tf.reduce_logsumexp(x, 1, keep_dims=True) ==> [[log(3)], [log(3)]]
  tf.reduce_logsumexp(x, [0, 1]) ==> log(6)
  ```

  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.
  """
  with ops.name_scope(name, "ReduceLogSumExp", [input_tensor]) as name:
    my_max = array_ops.stop_gradient(
        reduce_max(
            input_tensor,
            axis=axis,
            reduction_indices=reduction_indices,
            keep_dims=True))
    result = gen_math_ops.log(
        reduce_sum(
            gen_math_ops.exp(input_tensor - my_max),
            axis,
            keep_dims=True,
            reduction_indices=reduction_indices)) + my_max
    if not keep_dims:
      if isinstance(axis, int):
        axis = [axis]
      result = array_ops.squeeze(result, axis)
    return result 
开发者ID:ryfeus,项目名称:lambda-packs,代码行数:62,代码来源:math_ops.py

示例3: reduce_logsumexp

# 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 log [as 别名]
def reduce_logsumexp(input_tensor, reduction_indices=None, keep_dims=False,
                     name=None):
  """Computes log(sum(exp(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.

  This function is more numerically stable than log(sum(exp(input))). It avoids
  overflows caused by taking the exp of large inputs and underflows caused by
  taking the log of small inputs.

  For example:

  ```python
  # 'x' is [[0, 0, 0]]
  #         [0, 0, 0]]
  tf.reduce_logsumexp(x) ==> log(6)
  tf.reduce_logsumexp(x, 0) ==> [log(2), log(2), log(2)]
  tf.reduce_logsumexp(x, 1) ==> [log(3), log(3)]
  tf.reduce_logsumexp(x, 1, keep_dims=True) ==> [[log(3)], [log(3)]]
  tf.reduce_logsumexp(x, [0, 1]) ==> log(6)
  ```

  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.
  """
  with ops.name_scope(name, "ReduceLogSumExp", [input_tensor]) as name:
    my_max = array_ops.stop_gradient(
        reduce_max(input_tensor, reduction_indices, keep_dims=True))
    result = gen_math_ops.log(reduce_sum(
        gen_math_ops.exp(input_tensor - my_max),
        reduction_indices,
        keep_dims=True)) + my_max
    if not keep_dims:
      if isinstance(reduction_indices, int):
        reduction_indices = [reduction_indices]
      result = array_ops.squeeze(result, reduction_indices)
    return result 
开发者ID:tobegit3hub,项目名称:deep_image_model,代码行数:52,代码来源:math_ops.py


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