tf.compat.v1.nn.static_state_saving

Calculate the sufficient statistics for the mean and variance of x.

tf.compat.v1.nn.sufficient_statistics(
    x, axes, shift=None, keep_dims=None, name=None, keepdims=None
)

These sufficient statistics are computed using the one pass algorithm on an input that's optionally shifted. See: https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Computing_shifted_data

For example:

t = [[1, 2, 3], [4, 5, 6]]
sufficient_statistics(t, [1])
(<tf.Tensor: shape=(), dtype=int32, numpy=3>, <tf.Tensor: shape=(2,),
dtype=int32, numpy=array([ 6, 15], dtype=int32)>, <tf.Tensor: shape=(2,),
dtype=int32, numpy=array([14, 77], dtype=int32)>, None)
sufficient_statistics(t, [-1])
(<tf.Tensor: shape=(), dtype=int32, numpy=3>, <tf.Tensor: shape=(2,),
dtype=int32, numpy=array([ 6, 15], dtype=int32)>, <tf.Tensor: shape=(2,),
dtype=int32, numpy=array([14, 77], dtype=int32)>, None)

Args
`x`	A `Tensor`.
`axes`	Array of ints. Axes along which to compute mean and variance. As in Python, the axes can also be negative numbers. A negative axis is interpreted as counting from the end of the rank, i.e., axis + rank(values)-th dimension.
`shift`	A `Tensor` containing the value by which to shift the data for numerical stability, or `None` if no shift is to be performed. A shift close to the true mean provides the most numerically stable results.
`keep_dims`	produce statistics with the same dimensionality as the input.
`name`	Name used to scope the operations that compute the sufficient stats.
`keepdims`	Alias for keep_dims.

Returns
Four `Tensor` objects of the same type as `x`: the count (number of elements to average over). the (possibly shifted) sum of the elements in the array. the (possibly shifted) sum of squares of the elements in the array. the shift by which the mean must be corrected or None if `shift` is None.

TensorFlow

tf.compat / v1 / v1.nn.static_state_saving_rnn

For example:

Args

Returns

TensorFlow

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tf.compat / v1 / v1.nn.static_state_saving_rnn

For example:

Args

Returns