code: def elu(x):
      """Exponential Linear Unit :math:`\\varphi(x) = (x > 0) ? x : e^x - 1`
  
      The Exponential Linear Unit (ELU) was introduced in [1]_. Compared to the
      linear rectifier :func:`rectify`, it has a mean activation closer to zero
      and nonzero gradient for negative input, which can help convergence.
      Compared to the leaky rectifier :class:`LeakyRectify`, it saturates for
      highly negative inputs.
  
      Parameters
      ----------
      x : float32
          The activation (the summed, weighed input of a neuron).
  
      Returns
      -------
      float32
          The output of the exponential linear unit for the activation.
  
      Notes
      -----
      In [1]_, an additional parameter :math:`\\alpha` controls the (negative)
      saturation value for negative inputs, but is set to 1 for all experiments.
      It is omitted here.
  
      References
      ----------
      .. [1] Djork-Arné Clevert, Thomas Unterthiner, Sepp Hochreiter (2015):
         Fast and Accurate Deep Network Learning by Exponential Linear Units
         (ELUs), http://arxiv.org/abs/1511.07289
      """
      return theano.tensor.switch(x > 0, x, theano.tensor.expm1(x))
  
file_path: Lasagne/lasagne/nonlinearities.py
imports: import theano.tensor
label_desc: Use image node_id 5 for calling a global function with example usage: elu(x) and returns: theano