parameters: (self, y)
docstring: Return a float representing the number of errors in the minibatch
          over the total number of examples of the minibatch ; zero one
          loss over the size of the minibatch
  
          :type y: theano.tensor.TensorType
          :param y: corresponds to a vector that gives for each example the
                    correct label
docstring_summary: Return a float representing the number of errors in the minibatch
          over the total number of examples of the minibatch ; zero one
          loss over the size of the minibatch
docstring_tokens: Return a float representing the number of errors in the minibatch over the total number of examples of the minibatch ; zero one loss over the size of the minibatch
function: def errors(self, y):
          """Return a float representing the number of errors in the minibatch
          over the total number of examples of the minibatch ; zero one
          loss over the size of the minibatch
  
          :type y: theano.tensor.TensorType
          :param y: corresponds to a vector that gives for each example the
                    correct label
          """
  
          # check if y has same dimension of y_pred
          if y.ndim != self.y_pred.ndim:
              raise TypeError(
                  'y should have the same shape as self.y_pred',
                  ('y', y.type, 'y_pred', self.y_pred.type)
              )
          # check if y is of the correct datatype
          if y.dtype.startswith('int'):
              # the T.neq operator returns a vector of 0s and 1s, where 1
              # represents a mistake in prediction
              return T.mean(T.neq(self.y_pred, y))
          else:
              raise NotImplementedError()
function_tokens: def errors ( self , y ) : # check if y has same dimension of y_pred if y . ndim != self . y_pred . ndim : raise TypeError ( 'y should have the same shape as self.y_pred' , ( 'y' , y . type , 'y_pred' , self . y_pred . type ) ) # check if y is of the correct datatype if y . dtype . startswith ( 'int' ) : # the T.neq operator returns a vector of 0s and 1s, where 1 # represents a mistake in prediction return T . mean ( T . neq ( self . y_pred , y ) ) else : raise NotImplementedError ( )