analyzers.py 文件源码

python
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项目:transform 作者: tensorflow 项目源码 文件源码
def var(x, reduce_instance_dims=True, name=None):
  """Computes the variance of the values of a `Tensor` over the whole dataset.

  Uses the biased variance (0 delta degrees of freedom), as given by
  (x - mean(x))**2 / length(x).

  Args:
    x: A `Tensor`.
    reduce_instance_dims: By default collapses the batch and instance dimensions
        to arrive at a single scalar output. If False, only collapses the batch
        dimension and outputs a vector of the same shape as the input.
    name: (Optional) A name for this operation.

  Returns:
    A `Tensor` containing the variance. If `x` is floating point, the variance
    will have the same type as `x`. If `x` is integral, the output is cast to
    float32 for int8 and int16 and float64 for int32 and int64 (similar to the
    behavior of tf.truediv).
  """
  with tf.name_scope(name, 'var'):
    # Note: Calling `mean`, `sum`, and `size` as defined in this module, not the
    # builtins.
    x_mean = mean(x, reduce_instance_dims)
    # x_mean will be float32 or float64, depending on type of x.
    squared_deviations = tf.square(tf.cast(x, x_mean.dtype) - x_mean)
    return mean(squared_deviations, reduce_instance_dims)
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