tf.contrib.distributions.reduce_weighted_logsumexp

Computes log(abs(sum(weight * exp(elements across tensor dimensions)))).

tf.contrib.distributions.reduce_weighted_logsumexp(
    logx, w=None, axis=None, keep_dims=False, return_sign=False, name=None
)

If all weights w are known to be positive, it is more efficient to directly use reduce_logsumexp, i.e., tf.reduce_logsumexp(logx + tf.math.log(w)) is more efficient than du.reduce_weighted_logsumexp(logx, w).

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(w * 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:

x = tf.constant([[0., 0, 0],
                 [0, 0, 0]])

w = tf.constant([[-1., 1, 1],
                 [1, 1, 1]])

du.reduce_weighted_logsumexp(x, w)
# ==> log(-1*1 + 1*1 + 1*1 + 1*1 + 1*1 + 1*1) = log(4)

du.reduce_weighted_logsumexp(x, w, axis=0)
# ==> [log(-1+1), log(1+1), log(1+1)]

du.reduce_weighted_logsumexp(x, w, axis=1)
# ==> [log(-1+1+1), log(1+1+1)]

du.reduce_weighted_logsumexp(x, w, axis=1, keep_dims=True)
# ==> [[log(-1+1+1)], [log(1+1+1)]]

du.reduce_weighted_logsumexp(x, w, axis=[0, 1])
# ==> log(-1+5)