some autoassociator code

compile.py

@@ -11,6 +11,7 @@ import core
 
 
 def to_func(inputs, outputs):
+    print gof.Env(inputs, outputs)
     p = prog(inputs, outputs)
     print p.env
     def f(*args):

core.py

@@ -51,6 +51,16 @@ def print_graph(*rs):
     print as_string(*rs)
 
 
+def input(x):
+    if isinstance(x, numpy.ndarray):
+        return NumpyR(x)
+    elif isinstance(x, (int, float)):
+        return NumpyR(numpy.array(x))
+    elif isinstance(x, gof.Result):
+        raise TypeError("%s is already a result." % x)
+    else:
+        return PythonR(x)
+
 def wrap(x):
     if isinstance(x, NumpyR):
         return x
@@ -60,12 +70,34 @@ def wrap(x):
         return x.out
     elif isinstance(x, Proxy):
         return wrap(x._obj)
+    else:
+        return input(x)
+#     elif isinstance(x, numpy.ndarray):
+#         return NumpyR(x)
+#     elif isinstance(x, (int, float)):
+#         return NumpyR(numpy.array(x))
+#     else:
+#         return PythonR(x)
+
+def literal(x):
+    try:
+        present = x in gof.literals_db
+    except TypeError: # x is unhashable
+        present = False
+
+    if present:
+        return gof.literals_db.get(x)
     elif isinstance(x, numpy.ndarray):
-        return NumpyR(x)
+        ret = NumpyR(x, constant = True)
     elif isinstance(x, (int, float)):
-        return NumpyR(numpy.array(x))
+        ret = NumpyR(numpy.array(x), constant = True)
+    elif isinstance(x, gof.Result):
+        raise TypeError("%s is already a result." % x)
     else:
-        return PythonR(x)
+        return PythonR(x, constant = True)
+    gof.literals_db[x] = ret
+    return ret
+
 
 
 inplace = gof.Destroyer

test.py

@@ -63,24 +63,28 @@ import grad
 
 ############################
 
-# core.build_mode()
-# dim = core.wrap(())
-# dim2 = core.wrap((2, 2))
-# a = core.zeros(dim, dtype='int32') #(core.NumpyR(numpy.ones((3, 3))))
-# b = core.ones(dim2, 'int32') #(core.NumpyR(numpy.ones((3, 3))))
-# c = core.zeros(dim, dtype='int32')
-# d = a + (b + b) + c + numpy.ones(())
-# e = d + (b * c)
-# core.pop_mode()
+#core.build_mode()
+dim = core.wrap(())
+dim2 = core.wrap((2, 2))
+a = core.zeros(dim, dtype='int32') #(core.NumpyR(numpy.ones((3, 3))))
+b = core.ones(dim2, 'int32') #(core.NumpyR(numpy.ones((3, 3))))
+c = core.zeros(dim, dtype='int32')
 
+d = a + (b + b) + c + numpy.ones(())
+e = d + (b * c)
 
-# #print e
-# #print gof.graph.ops([dim], [e])
-# #1/0
+#core.pop_mode()
 
-# #print gof.Env([dim], [e])
+print e
+
+#print e
+#print gof.graph.ops([dim], [e])
+#1/0
+
+#print gof.Env([dim], [e])
+
+#f = compile.to_func([dim], [e])
 
-# #f = compile.to_func([dim], [e])
 # f = compile.to_func([a, b, c], [e])
 
 # print f(1, 2, 3)
@@ -108,20 +112,46 @@ import grad
 
 ############################
 
-a = core.ones((2, 2))
-b = core.ones((2, 2))
+# a = core.ones((2, 2))
+# b = core.ones((2, 2))
+
+# def f():
+#     return (a + b) + (a + b)
+
+# r = core.build(f)
+
+# g = grad.grad(r, a)
+
+# core.print_graph(g)
+# print [id(input) for input in g.owner.inputs]
+# print gof.literals_db
+
+# core.print_graph(r)
+
 
-def f():
-    return (a + b) + (a + b)
 
-r = core.build(f)
+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))
 
-g = grad.grad(r, a)
 
-core.print_graph(g)
-core.print_graph(r)
+def autoassociator(w, x):
+    forward = sigmoid(core.dot(w.T, sigmoid(core.dot(w, x))))
+    w -= 0.01 * grad.grad(core.sqr(x - forward(x)), w)
 
+w = core.input(numpy.random.rand())
+x = core.input(numpy.random.rand())
 
+for i in xrange(10000):
+    autoassociator(w, dataset.next())
 
 
 
+# 1 = mul(mul(neg(scal(mul(sub(0.736213102665, sigmoid(*3)), 1.0), 2.0)), sigmoid(*3)), sub(1, sigmoid(*3)))
+# 2 = transpose(0.11474051836)
+# 3 = dot(*2, *5)
+# 4 = dot(0.11474051836, 0.736213102665)
+# 5 = sigmoid(*4)
+# add(transpose(dot(*1, transpose(*5))), dot(mul(mul(dot(transpose(*2), *1), sigmoid(*4)), sub(1, sigmoid(*4))), transpose(0.736213102665)))