init logistic_regression.py

examples/logistic_regression.py

+import sys
+sys.path.insert(0, '..')
+import theano
+from theano import tensor as T
+from theano.sandbox import nnet_ops
+from theano.sandbox import module
+
+import numpy as N
+
+
+class LogisticRegression(module.FancyModule):
+    class __instance_type__(module.FancyModuleInstance):
+        def initialize(self, n_in, n_out):
+            #self.component is the LogisticRegressionTemplate instance that built this guy.
+
+            self.w = N.random.randn(n_in, n_out)
+            self.b = N.random.randn( n_out)
+            self.lr = 0.01
+
+    def __init__(self, x = None, targ = None):
+        super(LogisticRegression, 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.matrix())   #automatically names
+        self.b = module.Member(T.vector())   #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]
+
+        xent, y = nnet_ops.crossentropy_softmax_1hot(
+                T.dot(self.x, self.w) + self.b, self.targ)
+
+        gparams = T.grad(xent, self.params)
+
+        self.update = module.Method([self.x, self.targ], xent,
+                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')
+
+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)
+    
+
+