added sigmoid-based logistic regression example

examples/logistic_regression.py

@@ -8,7 +8,7 @@ from theano.sandbox import module
 import numpy as N
 
 
-class LogisticRegression(module.FancyModule):
+class LogisticRegressionN(module.FancyModule):
     class __instance_type__(module.FancyModuleInstance):
         def initialize(self, n_in, n_out):
             #self.component is the LogisticRegressionTemplate instance that built this guy.
@@ -18,7 +18,7 @@ class LogisticRegression(module.FancyModule):
             self.lr = 0.01
 
     def __init__(self, x = None, targ = None):
-        super(LogisticRegression, self).__init__() #boilerplate
+        super(LogisticRegressionN, self).__init__() #boilerplate
 
         self.x = x if x is not None else T.matrix()
         self.targ = targ if targ is not None else T.lvector()
@@ -39,19 +39,50 @@ class LogisticRegression(module.FancyModule):
                 updates = dict((p, p - self.lr * g) for p, g in zip(self.params, gparams)))
         self.apply = module.Method([self.x], T.argmax(T.dot(self.x, self.w) + self.b, axis=1))
 
-lr = LogisticRegression().make(10, 5, mode='FAST_COMPILE')
+class LogisticRegression2(module.FancyModule):
+    class __instance_type__(module.FancyModuleInstance):
+        def initialize(self, n_in):
+            #self.component is the LogisticRegressionTemplate instance that built this guy.
+
+            self.w = N.random.randn(n_in,1)
+            self.b = N.random.randn(1)
+            self.lr = 0.01
+
+    def __init__(self, x = None, targ = None):
+        super(LogisticRegression2, self).__init__() #boilerplate
+
+        self.x = x if x is not None else T.matrix()
+        self.targ = targ if targ is not None else T.lvector()
+
+        self.w = module.Member(T.dmatrix())   #automatically names
+        self.b = module.Member(T.dvector())   #automatically names
+        self.lr = module.Member(T.dscalar()) #provides an external interface to change it
+        #and makes it an implicit input to any Method you build.
+
+        self.params = [self.w, self.b]
+
+        y = nnet_ops.sigmoid(T.dot(self.x, self.w))
+        xent_elem = self.targ * T.log(y) - (1.0 - self.targ) *T.log(1.0 - y)
+        xent = T.sum(xent_elem)
+
+        gparams = T.grad(xent, self.params)
+
+        self.update = module.Method([self.x, self.targ], [xent, self.w, gparams[0]],
+                updates = dict((p, p - self.lr * g) for p, g in zip(self.params, gparams)))
+        self.apply = module.Method([self.x], T.argmax(T.dot(self.x, self.w) + self.b, axis=1))
+
+if __name__ == '__main__':
+    lr = LogisticRegression2().make(10, mode='FAST_COMPILE')
 
-data_x = N.random.randn(10,10)
-data_y = (N.random.randn(10) > 0)
+    data_x = N.random.randn(10, 10)
+    data_y = (N.random.randn(10) > 0)
 
 
-print lr.params
-print lr.w
-print lr.b
-print lr
-for i in xrange(100):
-    xe = lr.update(data_x, data_y)
-    print N.sum(xe)
-    
+    print lr.params
+    print lr.w.shape
+    for i in xrange(10):
+        xe = lr.update(data_x, data_y)
+        print N.sum(xe), lr.w.shape
+