Clarify docstring for categorical cross-entropy

lasagne/objectives.py

@@ -149,6 +149,9 @@ def categorical_crossentropy(predictions, targets):
 
  .. math:: L_i = - \\sum_j{t_{i,j} \\log(p_{i,j})}
 
+ :math:`p` are the predictions, :math:`t` are the targets, :math:`i`
+ denotes the data point and :math:`j` denotes the class.
+
  Parameters
  ----------
  predictions : Theano 2D tensor
@@ -157,6 +160,8 @@ def categorical_crossentropy(predictions, targets):
  targets : Theano 2D tensor or 1D tensor
  Either targets in [0, 1] matching the layout of `predictions`, or
  a vector of int giving the correct class index per data point.
+ In the case of an integer vector argument, each element
+ represents the position of the '1' in a one-hot encoding.
 
  Returns
  -------