>>> tf.nn.elu(0.0)
<tf.Tensor: shape=(), dtype=float32, numpy=0.0>
>>> tf.nn.elu(-1000.0)
<tf.Tensor: shape=(), dtype=float32, numpy=-1.0>

See [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
](http://arxiv.org/abs/1511.07289)
  }];

  let arguments = (ins
    TF_FloatTensor:$features
  );

  let results = (outs
    TF_FloatTensor:$activations
  );

OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.

================================================================

github.com/docker/go-units
https://github.com/docker/go-units
----------------------------------------------------------------

                                 Apache License
                           Version 2.0, January 2004

<sign>            ::= \"+\" | \"-\" <signedNumber>    ::= <number> | <sign><number> <suffix>          ::= <binarySI> | <decimalExponent> | <decimalSI> <binarySI>        ::= Ki | Mi | Gi | Ti | Pi | Ei\n\n\t(International System of units; See: http://physics.nist.gov/cuu/Units/binary.html)\n\n<decimalSI>       ::= m | \"\" | k | M | G | T | P | E\n\n\t(Note that 1024 = 1Ki but 1000 = 1k; I didn't choose the capitalization.)\n\n<decimalExponent> ::= \"e\" <signedNumber> | \"E\" <signedNumber> ```\n\nNo...