minor unsupervised fix

Author: lazyprogrammer

unsupervised_class2/unsupervised.py

@@ -57,15 +57,15 @@ def fit_to_input(self, k, learning_rate=0.00001, mu=0.99, reg=10e-10, epochs=200
         # # choose Y[0] b/c it's shape 1xD, we want just a D-size vector, not 1xD matrix
         # cost = -(t*T.log(Y[0]) + (1 - t)*(T.log(1 - Y[0]))).sum() + reg*(X * X).sum()
 
-        cost = -T.log(Y[k]) + reg*(X * X).sum()
+        cost = -T.log(Y[0,k]) + reg*(X * X).sum()
 
         updates = [
             (X, X + mu*dX - learning_rate*T.grad(cost, X)),
             (dX, mu*dX - learning_rate*T.grad(cost, X)),
         ]
         train = theano.function(
             inputs=[],
-            outputs=cost,
+            outputs=[cost, Y],
             updates=updates,
         )
 
@@ -74,7 +74,9 @@ def fit_to_input(self, k, learning_rate=0.00001, mu=0.99, reg=10e-10, epochs=200
         for i in xrange(epochs):
             if i % 1000 == 0:
                 print "epoch:", i
-            the_cost = train()
+            the_cost, out = train()
+            if i == 0:
+                print "out.shape:", out.shape
             costs.append(the_cost)
             # if the_cost < 10:
             #     break

unsupervised_class2/visualize_features.py

@@ -47,10 +47,10 @@ def main(loadfile=None, savefile=None):
 
 if __name__ == '__main__':
     # to load a saved file
-    # main(loadfile='rbm15.npz')
+    main(loadfile='rbm15.npz')
 
     # to neither load nor save
     # main()
 
     # to save a trained unsupervised deep network
-    main(savefile='rbm15.npz')
+    # main(savefile='rbm15.npz')