--- a/sandbox/rbm/globals.py	Tue Jul 08 20:39:36 2008 -0400
+++ b/sandbox/rbm/globals.py	Tue Jul 08 20:48:56 2008 -0400
@@ -2,11 +2,10 @@
 Global variables.
 """
 
-#INPUT_DIMENSION = 1000
+INPUT_DIMENSION = 1000
 #INPUT_DIMENSION = 100
-INPUT_DIMENSION = 4
-#HIDDEN_DIMENSION = 10
-HIDDEN_DIMENSION = 6
+HIDDEN_DIMENSION = 20
+#HIDDEN_DIMENSION = 6
 LEARNING_RATE = 0.1
 LR = LEARNING_RATE
 SEED = 666

--- a/sandbox/rbm/model.py	Tue Jul 08 20:39:36 2008 -0400
+++ b/sandbox/rbm/model.py	Tue Jul 08 20:48:56 2008 -0400
@@ -30,6 +30,15 @@
             else: x[j][i] = 0
     return x
 
+def crossentropy(output, target):
+    """
+    Compute the crossentropy of binary output wrt binary target.
+    @note: We do not sum, crossentropy is computed by component.
+    @todo: Rewrite as a scalar, and then broadcast to tensor.
+    """
+    return -(target * numpy.log(output) + (1 - target) * numpy.log(1 - output))
+
+
 class Model:
     def __init__(self):
         self.parameters = parameters.Parameters(randomly_initialize=True)
@@ -55,11 +64,11 @@
         print "Q(h[0][i] = 1 | v[0]):", q0
         print "h[0]:", h0
         print "P(v[1][j] = 1 | h[0]):", p0
+        print "XENT(P(v[1][j] = 1 | h[0]) | v0):", numpy.sum(crossentropy(p0, v0))
         print "v[1]:", v1
         print "Q(h[1][i] = 1 | v[1]):", q1
 
-        print self.parameters.w.shape
         self.parameters.w += LR * (dot(v0.T, h0) - dot(v1.T, q1))
         self.parameters.b += LR * (h0 - q1)
         self.parameters.c += LR * (v0 - v1)
-        print self.parameters
+#        print self.parameters