<commit_before>
import theano.tensor as T
from math import floor
    
#### Exponential linear unit
def Elu(x, alpha = 1):
    """ 
    Exponential Linear Units. 
    
    Applies point-wise ela to the input supplied. ``alpha`` is defualt to ``0``. 
    Supplying a value to ``alpha`` would make this a leay Elu. 

    Notes: 
        Reference :<NAME>, <NAME>, and <NAME>. "Fast and accurate 
         deep network learning by exponential linear units (elus)." arXiv preprint arXiv:1511.07289 
         (2015).
    
    Args:
        x: could be a ``theano.tensor`` or a ``theano.shared`` or ``numpy`` arrays or 
            ``python lists``.             
        alpha: should be a ``float``. Default is ``1``.
        
    Returns: 
        same as input: returns a point-wise rectified output.        
    """ 
    y = T.nnet.elu(x,alpha = alpha)
    return(y)

#### rectified linear unit
def ReLU(x, alpha = 0):
    """ 
    Rectified Linear Units. 
    
    Applies point-wise rectification to the input supplied. ``alpha`` is defualt to ``0``. 
    Supplying a value to ``alpha`` would make this a leay ReLU. 

    Notes:
        Reference: Nair, Vinod, and <NAME>. "Rectified linear units improve restricted
         boltzmann machines." Proceedings of the 27th International Conference on Machine Learning 
         (ICML-10). 2010.
    
    Args:
        x: could be a ``theano.tensor`` or a ``theano.shared`` or ``numpy`` arrays or 
            ``python lists``.             
        alpha: should be a ``float``.<commit_msg>some bug for travis<commit_after>
import theano.tensor as T
from math import floor
    
#### Exponential linear unit
def Elu(x, alpha = 1):
    """ 
    Exponential Linear Units. 
    
    Applies point-wise ela to the input supplied. ``alpha`` is defualt to ``0``. 
    Supplying a value to ``alpha`` would make this a leay Elu. 

    Notes: 
        Reference :<NAME>, <NAME>, and <NAME>. "Fast and accurate 
         deep network learning by exponential linear units (elus)." arXiv preprint arXiv:1511.07289 
         (2015).
    
    Args:
        x: could be a ``theano.tensor`` or a ``theano.shared`` or ``numpy`` arrays or 
            ``python lists``.             
        alpha: should be a ``float``. Default is ``1``.
        
    Returns: 
        same as input: returns a point-wise rectified output.        
    """ 
    y = T.nnet.elu(x,alpha = alpha)
    return(y)

#### rectified linear unit
def ReLU(x, alpha = 0):
    """ 
    Rectified Linear Units. 
    
    Applies point-wise rectification to the input supplied. ``alpha`` is defualt to ``0``. 
    Supplying a value to ``alpha`` would make this a leay ReLU. 

    Notes:
        Reference: <NAME>, and <NAME>. "Rectified linear units improve restricted
         boltzmann machines." Proceedings of the 27th International Conference on Machine Learning 
         (ICML-10). 2010.
    
    Args:
        x: could be a ``theano.tensor`` or a ``theano.shared`` or ``numpy`` arrays or 
            ``python lists``.             
        alpha: should be a ``float``.
{'fraction_non_alphanumeric': 0.11299251058287203, 'fraction_numerical': 0.016932595245848257, 'mean_word_length': 2.163748712667353, 'pattern_counts': {'":': 0, '<': 12, '<?xml version=': 0, '>': 12, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '7427837', 'n_tokens_mistral': 970, 'n_tokens_neox': 868, 'n_words': 353}