bla

test.py

@@ -131,53 +131,69 @@ import grad
 
 ############################
 
-# def dataset_1hot(x, targ, n):
-#     """Return an looping iterator over 1-hot vectors
+def dataset_1hot(x, targ, n):
+    """Return an looping iterator over 1-hot vectors
 
-#     This function is a generator for the integers range(n) that works by
-#     side-effect on the numpy ndarray mat.
-#     On each iteration, mat is set (in-place) to the next element of an infinite
-#     sequence of 1-hot vectors.
+    This function is a generator for the integers range(n) that works by
+    side-effect on the numpy ndarray mat.
+    On each iteration, mat is set (in-place) to the next element of an infinite
+    sequence of 1-hot vectors.
 
-#     """
-#     assert targ.size == 1
+    """
+    assert targ.size == 1
 
-#     for i in xrange(n):
-#         idx = i % x.shape[1]
-#         x[:] = 0
-#         x[0,idx] = 1
-#         targ[0] = idx
-#         yield i
+    for i in xrange(n):
+        idx = i % x.shape[1]
+        x[:] = 0
+        x[0,idx] = 1
+        targ[0] = idx
+        yield i
 
 
-# class sigmoid(core.omega_op):
-#     def impl(x):
-#         return 1.0 / (1.0 + numpy.exp(-x))
-#     def grad(x, gz):
-#         return gz * sigmoid(x) * (1 - sigmoid(x))
+class sigmoid(core.omega_op):
+    def impl(x):
+        return 1.0 / (1.0 + numpy.exp(-x))
+    def grad(x, gz):
+        return gz * sigmoid(x) * (1 - sigmoid(x))
 
 
 # x = core.zeros((1, 10))
 # w = core.input(numpy.random.rand(10, 15))
 
-# #print x.data, w.data
+x = numpy.zeros((1, 10))
+w = numpy.random.rand(10, 15)
 
-# def autoassociator(w, x):
-#     forward = sigmoid(core.dot(sigmoid(core.dot(x, w)), w.T))
-#     rec_error = core.sum(core.sqr(x - forward))
-#     w -= 0.1 * grad.grad(rec_error, w)
-#     return w, rec_error
+#print x.data, w.data
+
+import inspect
+
+def omega_compile(f):
+    args, varargs, kwargs, defaults = inspect.getargspec(f)
+    assert not varargs
+    assert not kwargs
+    def ret(*args):
+        outputs = core.build(f, *args)
+        return compile.prog(args, outputs)
+    return ret
+
+@omega_compile
+def autoassociator(w, x):
+    forward = sigmoid(core.dot(sigmoid(core.dot(x, w)), w.T))
+    rec_error = core.sum(core.sqr(x - forward))
+    w -= 0.1 * grad.grad(rec_error, w)
+    return w, rec_error
 
 # w2, rec_error = core.build(autoassociator, w, x)
-# f = compile.to_func([w, x], [w2, rec_error])
+# #f = compile.to_func([w, x], [w2, rec_error])
+# f = compile.single(w2, rec_error)
 
-# for i in dataset_1hot(x.data, numpy.ndarray((1, )), 10000):
-#     w2, rec_error = f(w.data, x.data)
-#     if not(i % 1000):
-#         print rec_error
+for i in dataset_1hot(x.data, numpy.ndarray((1, )), 10000):
+    w2, rec_error = f() #w.data, x.data)
+    if not(i % 1000):
+        print rec_error
 
-# print "done!"
-# print w.data
+print "done!"
+print w.data