changes to Module, removed Member from user API

theano/compile/function_module.py

 """Driver of graph construction, optimization, and linking.
 
 """
+__docformat__ = "restructuredtext en"
 
 import copy_reg
 import cPickle

theano/compile/tests/test_inplace_opt_for_value.py

 #!/usr/bin/env python
 import numpy as N
-from theano import Op, Apply, tensor as T, Module, Member, Method, Mode, compile
+from theano import Op, Apply, tensor as T, Module, Method, Mode, compile
 from theano.gof import OpSub, TopoOptimizer
 
-from pylearn.algorithms.minimizer import make_minimizer # minimizer
 from theano.printing import Print
-#import sgd #until Olivier's module-import thing works better
 
 ####################
 # Library-type stuff
@@ -14,8 +12,6 @@ from theano.printing import Print
 from theano.compile import module
 from theano import tensor as T
 
-from pylearn.algorithms.minimizer import minimizer_factory
-
 class StochasticGradientDescent(module.FancyModule):
     """Fixed stepsize gradient descent"""
     def __init__(self, args, cost, params, gradients=None, stepsize=None, WEIRD_STUFF=True):
@@ -28,18 +24,18 @@ class StochasticGradientDescent(module.FancyModule):
         self.stepsize_init = None
 
         if stepsize is None:
-            self.stepsize = module.Member(T.dscalar())
+            self.stepsize = (T.dscalar())
         elif isinstance(stepsize, T.TensorResult):
             self.stepsize = stepsize
         else:
             if self.WEIRD_STUFF:
                 #TODO: why is this necessary? why does the else clause not work?
 #                self.stepsize = module.Member(T.dscalar(), init = stepsize)
-                self.stepsize = module.Member(T.dscalar())
+                self.stepsize = (T.dscalar())
                 self.stepsize_init = stepsize
             else:
 #                self.stepsize = module.Member(T.value(stepsize))
-                self.stepsize = module.Member(T.constant(stepsize))#work!
+                self.stepsize = (T.constant(stepsize))#work!
 
         if self.stepsize.ndim != 0:
             raise ValueError('stepsize must be a scalar', stepsize)
@@ -62,7 +58,6 @@ class StochasticGradientDescent(module.FancyModule):
             pass
 
 
-@minimizer_factory('sgd')
 def sgd_minimizer(stepsize=None, **args):
     def m(i,c,p,g=None):
         return StochasticGradientDescent(i, c, p, stepsize=stepsize, **args)
@@ -100,6 +95,9 @@ class TanhRnn(Op):
         return Apply(self, [x, z0, A], [z])
 
     def perform(self, node, (x,z0,A), out):
+        assert x is not None 
+        assert z0 is not None 
+        assert A is not None
         T,M = x.shape
         z = N.zeros((T+1, M))
         z[0] = z0
@@ -160,10 +158,10 @@ class ExampleRNN(Module):
         self.n_vis = n_vis
 
         #recurrent weight matrix in latent space
-        self.z0 = Member(T.dvector())
-        self.w = Member(T.dmatrix())
+        self.z0 = (T.dvector())
+        self.w = (T.dmatrix())
 
-        self.params = [self.w]
+        self.params = [self.z0, self.w]
 
         #input and target
         x, y = T.dmatrix(), T.dmatrix()
@@ -175,6 +173,7 @@ class ExampleRNN(Module):
         self.minimizer = minimizer([x, y], self.cost, self.params)
 
     def _instance_initialize(self, obj):
+        print 'INITIALIZE EXAMPLE RNN'
         n_vis = self.n_vis
 
         rng = N.random.RandomState(2342)
@@ -184,14 +183,14 @@ class ExampleRNN(Module):
         obj.minimizer.initialize()
 
 def test_example_rnn():
-    minimizer_fn = make_minimizer('sgd', stepsize = 0.001)
+    minimizer_fn = sgd_minimizer(stepsize = 0.001)
 
     n_vis = 5
     n_out = 3
     n_hid = 4
     rnn_module = ExampleRNN(n_vis, minimizer_fn)
 
-    rnn = rnn_module.make(mode='FAST_RUN')
+    rnn = rnn_module.make()
 
     rng = N.random.RandomState(7722342)
     x = rng.randn(10,n_vis)
@@ -211,6 +210,7 @@ def test_example_rnn():
             print i, rnn.minimizer.step_cost(x, y), rnn.minimizer.stepsize
         else:
             rnn.minimizer.step_cost(x, y)
+    assert rnn.minimizer.step_cost(x,y) < -20 #it starts around -.28
 
 def test_WEIRD_STUFF():
     n_vis = 3
@@ -223,8 +223,8 @@ def test_WEIRD_STUFF():
     LAG = 4
     y[LAG:] = x[:-LAG, 0:n_vis]
 
-    minimizer_fn1 = make_minimizer('sgd', stepsize = 0.001, WEIRD_STUFF = False)
-    minimizer_fn2 = make_minimizer('sgd', stepsize = 0.001, WEIRD_STUFF = True)
+    minimizer_fn1 = sgd_minimizer(stepsize = 0.001, WEIRD_STUFF = False)
+    minimizer_fn2 = sgd_minimizer(stepsize = 0.001, WEIRD_STUFF = True)
     rnn_module1 = ExampleRNN(n_vis, minimizer_fn1)
     rnn_module2 = ExampleRNN(n_vis, minimizer_fn2)
     rnn1 = rnn_module1.make(mode='FAST_RUN')

theano/compile/tests/test_module.py

 #!/usr/bin/env python
+"""Test compile.module"""
+
+__docformat__ = "restructuredtext en"
+
 import cPickle, numpy, unittest
 from theano.compile.module import *
 import theano.tensor as T
 import sys
 import theano
+
 #TODO: add test for module.make(member=init_value)
-class T_test_module(unittest.TestCase):
+class T_module(unittest.TestCase):
 
     def test_whats_up_with_submembers(self):
-        class Blah(FancyModule):
+        class Blah(Module):
             def __init__(self, stepsize):
                 super(Blah, self).__init__()
-                self.stepsize = Member(T.value(stepsize))
+                self.stepsize = T.value(stepsize)
                 x = T.dscalar()
             
                 self.step = Method([x], x - self.stepsize)
 
         B = Blah(0.0)
         b = B.make(mode='FAST_RUN')
+        assert b.stepsize == 0.0
         b.step(1.0)
         assert b.stepsize == 0.0
 
@@ -57,8 +63,23 @@ class T_test_module(unittest.TestCase):
 
             assert isinstance(m1.x,(gof.Result))
             assert isinstance(m1.y,(gof.Result))
-            for i in [m1.lx[0], m1.ly[0], m1.llx[0][0], m1.lly[0][0], m1.ltx[0][0], m1.lty[0][0], m1.ldx[0]['x'], m1.ldy[0]['y'], m1.tx[0], m1.ty[0], m1.tlx[0][0], m1.tly[0][0], m1.ttx[0][0], m1.tty[0][0], m1.tdx[0]['x'], m1.tdy[0]['y'], m1.dx['x'], m1.dy['y'], m1.dlx['x'][0], m1.dly['y'][0], m1.dtx['x'][0], m1.dty['y'][0], m1.ddx['x']['x'], m1.ddy['y']['y']]:
-                assert isinstance(i,(gof.Result))
+            for i, obj in enumerate([
+                    m1.lx[0], #0
+                    m1.llx[0][0],
+                    m1.ltx[0][0],
+                    m1.ldx[0]['x'],
+                    m1.lty[0][0],#5
+                    m1.ldy[0]['y'],
+                    m1.ly[0],
+                    m1.lly[0][0],
+                    m1.tx[0], #8
+                    m1.ty[0], m1.tlx[0][0], 
+                    m1.tly[0][0], m1.ttx[0][0], m1.tty[0][0], m1.tdx[0]['x'],
+                    m1.tdy[0]['y'], m1.dx['x'], 
+                    m1.dy['y'], m1.dlx['x'][0], m1.dly['y'][0],
+                    m1.dtx['x'][0], m1.dty['y'][0], m1.ddx['x']['x'],
+                    m1.ddy['y']['y']]):
+                assert isinstance(obj,(gof.Result))
                 
 
             inst=m1.make()
@@ -98,23 +119,72 @@ class T_test_module(unittest.TestCase):
             for i,j in zip(get_l2(),range(len(get_l2()))):
                 assert i[0]==j
 
-        local_test(lambda:T.dscalar(),lambda:Member(T.dscalar()))
-        local_test(lambda:T.value(1),lambda:Member(T.value(2)))
-        local_test(lambda:T.constant(1),lambda:Member(T.constant(2)))
+        local_test(lambda:T.dscalar(),lambda:T.dscalar())
+        local_test(lambda:T.value(1),lambda:T.value(2))
+        local_test(lambda:T.constant(1),lambda:T.constant(2))
 
-    def test_compound_structure_assignment(self):
+    def test_list_assign(self):
         """Test that list members can be assigned list-wise"""
         def local_test(x,y):
             m1=Module()
-            m1.l=[x(), y()]#cast Result]
+
+            #create a list with some results in it
+            m1.l=[x(), y()]
+
+            # create a Method that makes the second list element a shared Member
             m1.f=Method([], m1.l[1])
+            m1.g=Method([], m1.l[0])
             m = m1.make()
+
+            #assign 4 and 5 to the two results' containers in m
             m.l = [4, 5]
+            print 'm.f', m.f()
+            assert numpy.all(5 == m.f())
+            assert numpy.all(4 == m.g())
+
+        local_test(lambda:T.dscalar(),lambda:T.dscalar())
+        local_test(lambda:T.value(1),lambda:T.value(2))
+
+    def test_tuple_assign(self):
+        """Test that list members can be assigned tuple-wise"""
+        def local_test(x,y):
+            m1=Module()
+            m1.l=(x(), y())
+
+            # create a Method that makes the second list element a shared Member
+            m1.g=Method([], m1.l[0])
+            m1.f=Method([], m1.l[1])
+            m = m1.make()
+
+            #assign 4 and 5 to the two results' containers in m
+            m.l = (4, 5)
             assert 5 == m.f()
+            assert 4 == m.g()
 
-        local_test(lambda:T.dscalar(),lambda:Member(T.dscalar()))
-        local_test(lambda:T.value(1),lambda:Member(T.value(2)))
-        local_test(lambda:T.constant(1),lambda:Member(T.constant(2)))
+        local_test(lambda:T.dscalar(),lambda:T.dscalar())
+        local_test(lambda:T.value(1),lambda:T.value(2))
+
+    def test_dict_assign(self):
+        """Test that list members can be assigned dict-wise"""
+        def local_test(x,y):
+            m1=Module()
+            ##DICT
+            m1.l={'x':x(), 'y':y()}
+
+            # create a Method that makes the second list element a shared Member
+            m1.f=Method([], m1.l['y'])
+            m1.g=Method([], m1.l['x'])
+            m = m1.make()
+
+            #assign 4 and 5 to the two results' containers in m
+            m.l = dict(x=4, y=5)
+            assert 5 == m.f()
+            assert 4 == m.g()
+
+        print 'dscalar test'
+        local_test(lambda:T.dscalar(),lambda:T.dscalar())
+        print 'value test'
+        local_test(lambda:T.value(1),lambda:T.value(2))
 
         
     def test_method_in_list_or_dict(self):
@@ -201,7 +271,7 @@ class T_test_module(unittest.TestCase):
         m2=Module()
         x=T.dscalar()
         populate_module(m1,x)
-        populate_module(m2,Member(x))
+        populate_module(m2,x)
         #m1.x and m2.x should not be shared as their is no hierarchi link between them.
         inst1=m1.make()
         inst2=m2.make()
@@ -248,8 +318,8 @@ class T_test_module(unittest.TestCase):
         m4=Module()
         x=T.dscalar()
         populate_module(m1,x)
-        populate_module(m2,Member(x))
-        populate_module(m4,Member(x))
+        populate_module(m2,(x))
+        populate_module(m4,(x))
         #m1.x and m2.x should not be shared as their is no hierarchi link between them.
         inst1=m1.make()
         inst2=m2.make()
@@ -325,33 +395,59 @@ class T_test_module(unittest.TestCase):
 
         print >> sys.stderr, "MODULE TEST IMPLEMENTED BUT WE DON'T KNOW WHAT WE WANT AS A RESULT"
 
-    def test_shared_method_N(self):
-        """Test that Methods can be shared an arbitrary number of times between many submodules and
-        internal data structures."""
-        
-    #put them in subModules, sub-sub-Modules, shared between a list and a dict, shared between
-    #a list and a submodule with a dictionary, etc...
-        print >> sys.stderr, "WARNING MODULE TEST NOT IMPLEMENTED"
-
     def test_member_method_inputs(self):
         """Test that module Members can be named as Method inputs, in which case the function will
         *not* use the storage allocated for the Module's version of that Member.
         
-        si le module a un membre x et qu''une fct un parametre appele x qui n''est pas le membre cela doit etre bien traiter.
-        les poids ne change pas
+        """
+        
+        # test that explicit Method inputs don't use shared storage
+        M = Module()
+        M.x = T.dscalar()
+        M.y = T.dscalar()
+        M.f = Method([M.x], M.x + M.y)
+        M.g = Method([M.y], M.x - M.y)
+        m = M.make()
+        m.y = 77
+        assert m.f(23) == 100
+        assert m.x == None
+        m.x = 1000
+        assert m.g(23) == 977
+        assert m.y == 77
+        assert m.x == 1000
 
-"""
-        print >> sys.stderr, "WARNING MODULE TEST NOT IMPLEMENTED"
 
     def test_member_input_flags(self):
         """Test that we can manipulate the mutable, strict, etc. flags (see SymbolicInput) of
         Method inputs"""
-        print >> sys.stderr, "WARNING MODULE TEST NOT IMPLEMENTED"
+
+        M = Module()
+        M.x = T.dvector()
+        M.y = T.dvector()
+        xval= numpy.asarray([0, 0.5])
+        M.f = Method([io.In(M.x,
+            mutable=True, 
+            update=(M.x - M.y),
+            value=xval)], M.x + M.y)
+        m = M.make()
+        m.y = numpy.asarray([1, 2])
+
+        assert numpy.all(m.f(xval) == [1, 2.5])
+        assert numpy.all(xval == [-1, -1.5])
 
     def test_member_output_flags(self):
         """Test that we can manipulate the output flags (just 'borrow' I think, see SymbolicOutput)
         of Method outputs"""
-        print >> sys.stderr, "WARNING MODULE TEST NOT IMPLEMENTED"
+        M = Module()
+        M.x = T.dvector()
+        M.f = Method([M.x], io.Out(M.x*4, borrow=True))
+        m = M.make()
+
+        v0 = m.f([5, 8])
+        v0_copy = v0 * 1
+        m.f([3, 2])
+        assert numpy.all(v0 != v0_copy)
+
 
     def test_sanity_check_mode(self):
         """Test that Module.make(self) can take the same list of Modes that function can, so we can
@@ -396,8 +492,8 @@ class T_test_module(unittest.TestCase):
 def test_pickle():
     """Test that a module can be pickled"""
     M = Module()
-    M.x = Member(T.dmatrix())
-    M.y = Member(T.dmatrix())
+    M.x = (T.dmatrix())
+    M.y = (T.dmatrix())
     a = T.dmatrix()
     M.f = Method([a], a + M.x + M.y)
     M.g = Method([a], a * M.x * M.y)
@@ -418,13 +514,11 @@ def test_pickle():
     assert m_dup.x is m_dup.g.input_storage[1].data
     assert m_dup.y is m_dup.g.input_storage[2].data
 
-from numpy.testing import *
-
-@dec.knownfailureif(True, "These branch cuts are known to fail")
 def test_pickle_aliased_memory():
+    try:
         M = Module()
-    M.x = Member(T.dmatrix())
-    M.y = Member(T.dmatrix())
+        M.x = (T.dmatrix())
+        M.y = (T.dmatrix())
         a = T.dmatrix()
         M.f = Method([a], a + M.x + M.y)
         M.g = Method([a], a * M.x * M.y)
@@ -450,6 +544,9 @@ def test_pickle_aliased_memory():
         assert m.y[0,0] == 3.142
         m_dup.x[1,0] = 3.142
         assert m_dup.y[0,0] == 3.142
+    except Exception, e:
+        raise Exception('Known Failure: These branch cuts are known to fail', str(e))
+
 
 
 if __name__ == '__main__':

theano/tensor/tests/test_naacl09.py

@@ -70,27 +70,36 @@ class QuadraticDenoisingAA(module.Module):
         # ACQUIRE/MAKE INPUT
         if not input:
             input = T.matrix('input')
-        self.input = theano.External(input)
+        #self.input = theano.External(input)
+        self.input = (input)
 
         # HYPER-PARAMETERS
-        self.lr = theano.Member(T.scalar())
+        #self.lr = theano.Member(T.scalar())
+        self.lr = (T.scalar())
 
         # PARAMETERS
         if _qfilters is None:
-            self.qfilters = [theano.Member(T.dmatrix('q%i'%i)) for i in xrange(n_quadratic_filters)]
+            #self.qfilters = [theano.Member(T.dmatrix('q%i'%i)) for i in xrange(n_quadratic_filters)]
+            self.qfilters = [(T.dmatrix('q%i'%i)) for i in xrange(n_quadratic_filters)]
         else:
-            self.qfilters = [theano.Member(q) for q in _qfilters]
+            #self.qfilters = [theano.Member(q) for q in _qfilters]
+            self.qfilters = [(q) for q in _qfilters]
 
-        self.w1 = theano.Member(T.matrix('w1')) if _w1 is None else theano.Member(_w1)
+        #self.w1 = theano.Member(T.matrix('w1')) if _w1 is None else theano.Member(_w1)
+        self.w1 = (T.matrix('w1')) if _w1 is None else (_w1)
         if _w2 is None:
             if not tie_weights:
-                self.w2 = theano.Member(T.matrix())
+                #self.w2 = theano.Member(T.matrix())
+                self.w2 = (T.matrix())
             else:
                 self.w2 = self.w1.T
         else:
-            self.w2 = theano.Member(_w2)
-        self.b1 = theano.Member(T.vector('b1')) if _b1 is None else theano.Member(_b1)
-        self.b2 = theano.Member(T.vector('b2')) if _b2 is None else theano.Member(_b2)
+            #self.w2 = theano.Member(_w2)
+            self.w2 = (_w2)
+        #self.b1 = theano.Member(T.vector('b1')) if _b1 is None else theano.Member(_b1)
+        self.b1 = (T.vector('b1')) if _b1 is None else (_b1)
+        #self.b2 = theano.Member(T.vector('b2')) if _b2 is None else theano.Member(_b2)
+        self.b2 = (T.vector('b2')) if _b2 is None else (_b2)
 
 #        # REGULARIZATION COST
 #        self.regularization = self.build_regularization()
@@ -212,7 +221,8 @@ class SigmoidXEQuadraticDenoisingAA(QuadraticDenoisingAA):
     """
 
     def build_corrupted_input(self):
-        self.noise_level = theano.Member(T.scalar())
+        #self.noise_level = theano.Member(T.scalar())
+        self.noise_level = (T.scalar())
         return self.random.binomial(T.shape(self.input), 1, 1 - self.noise_level) * self.input
 
     def hid_activation_function(self, activation):
@@ -262,12 +272,17 @@ class Module_Nclass(module.FancyModule):
     def __init__(self, x=None, targ=None, w=None, b=None, lr=None, regularize=False):
         super(Module_Nclass, self).__init__() #boilerplate
 
-        self.x = module.Member(x) if x is not None else T.matrix('input')
-        self.targ = module.Member(targ) if targ is not None else T.lvector()
+        #self.x = module.Member(x) if x is not None else T.matrix('input')
+        self.x = (x) if x is not None else T.matrix('input')
+        #self.targ = module.Member(targ) if targ is not None else T.lvector()
+        self.targ = (targ) if targ is not None else T.lvector()
 
-        self.w = module.Member(w) if w is not None else module.Member(T.dmatrix())
-        self.b = module.Member(b) if b is not None else module.Member(T.dvector())
-        self.lr = module.Member(lr) if lr is not None else module.Member(T.dscalar())
+        #self.w = module.Member(w) if w is not None else module.Member(T.dmatrix())
+        self.w = (w) if w is not None else (T.dmatrix())
+        #self.b = module.Member(b) if b is not None else module.Member(T.dvector())
+        self.b = (b) if b is not None else (T.dvector())
+        #self.lr = module.Member(lr) if lr is not None else module.Member(T.dscalar())
+        self.lr = (lr) if lr is not None else (T.dscalar())
 
         self.params = [p for p in [self.w, self.b] if p.owner is None]
 
@@ -355,7 +370,8 @@ class ConvolutionalMLP(module.FancyModule):
             ):
         super(ConvolutionalMLP, self).__init__()
 
-        self.lr = module.Member(T.scalar())
+        #self.lr = module.Member(T.scalar())
+        self.lr = (T.scalar())
 
         self.inputs = [T.dmatrix() for i in range(window_size)]
         self.targ = T.lvector()