# HG changeset patch
# User Pascal Lamblin <lamblinp@iro.umontreal.ca>
# Date 1219440786 14400
# Node ID 060c12314734a2e43e1c12a8ca48936ab388f657
# Parent  b3315b2528246b3f26372222cf2fab6983fc215f
Hopefully last bugfix in Softmax

diff -r b3315b252824 -r 060c12314734 nnet_ops.py
--- a/nnet_ops.py	Fri Aug 22 15:53:34 2008 -0400
+++ b/nnet_ops.py	Fri Aug 22 17:33:06 2008 -0400
@@ -105,7 +105,7 @@
             row = x[i] + b
             sm[i] = numpy.exp(row - numpy.max(row))
             sm[i] *= 1.0 / numpy.sum(sm[i])
-        output_storage[0][0] = nll
+        output_storage[0][0] = sm
 
     def grad(self, (x, b), (g_sm,)):
         sm = softmax_with_bias(x, b)
@@ -257,7 +257,7 @@
         #dx[i,j] = - (\sum_k dy[i,k] sm[i,k]) sm[i,j] + dy[i,j] sm[i,j]
         for i in xrange(sm.shape[0]):
             dy_times_sm_i = dy[i] * sm[i]
-            dx[i] = dy_times_sm - sum(dy_times_sm_i) * y[i]
+            dx[i] = dy_times_sm_i - sum(dy_times_sm_i) * sm[i]
         output_storage[0][0] = dx
 
     def grad(self, *args):
@@ -318,6 +318,10 @@
         }
         ''' % dict(locals(), **sub)
 
+def softmax(x, **kwargs):
+    b = tensor.zeros_like(x[0,:])
+    return softmax_with_bias(x, b, **kwargs)
+
 
 class CrossentropySoftmax1HotWithBias(theano.Op):
     """A special compound L{Op} for the output of neural-net classifiers.
@@ -576,7 +580,7 @@
         y = tensor.as_tensor(self.val)
         if x.type.dtype != y.type.dtype:
             TypeError("the value to prepend don't have the same type as the matrix")
-        
+
         node = theano.Apply(op=self, inputs=[mat], outputs=[tensor.matrix()])
         return node
 
@@ -599,7 +603,7 @@
     def grad(self, (mat,), (goutput,)):
         return goutput[:,1:]
 
-class Prepend_scalar_to_each_row(theano.Op):        
+class Prepend_scalar_to_each_row(theano.Op):
     def make_node(self, val, mat):
         #check type of input
         if isinstance(val, float):
@@ -610,7 +614,7 @@
         y = tensor.as_tensor(val)
         if x.type.dtype != y.type.dtype:
             TypeError("the value to prepend don't have the same type as the matrix")
-        
+
         node = theano.Apply(op=self, inputs=[val,mat], outputs=[tensor.matrix()])
         return node
 
@@ -659,4 +663,4 @@
     def grad(self, (theta, A, B), (gtheta,)):
         raise NotImplementedError()
 
-    
+