tf.math.reduce_logsumexp

TensorFlow 1 version View source on GitHub

Computes log(sum(exp(elements across dimensions of a tensor))).

Used in the notebooks

Used in the tutorials

Reduces input_tensor along the dimensions given in axis. Unless keepdims is true, the rank of the tensor is reduced by 1 for each entry in axis. If keepdims 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:

x = tf.constant([[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, keepdims=True)  # [[log(3)], [log(3)]]
tf.reduce_logsumexp(x, [0, 1])  # log(6)

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)).
keepdims If true, retains reduced dimensions with length 1.
name A name for the operation (optional).

The reduced tensor.