simplify code a bit

network.py

@@ -55,7 +55,7 @@ def d_sigmoid(vec):
 
 
 def L(x, y):
-    return (x - y) * (x - y)
+    return 0.5 * (x - y) * (x - y)
 
 
 class Model(object):
@@ -86,19 +86,14 @@ class Model(object):
         return self.h(self.z1(x))
 
     def f(self, x):
-        return self.w2.dot(self.a(x)) + self.b2
+        return np.dot(self.w2, self.a(x)) + self.b2
 
     def dLdf(self, x, y):
-        return -2.0 * (y - self.f(x))
-
-    def dfdb2(self):
-        return np.array([1.0])
+        return self.f(x) - y
 
     def dLdb2(self, x, y):
-        return self.dLdf(x, y) * self.dfdb2()
+        return self.dLdf(x, y) 
 
-    def dfdw2(self, x):  # how each entry of f changes wrt each entry of w2
-        return self.a(x)
 
     def dfda(self):  # how f changes with ith element of a
         return self.w2
@@ -111,22 +106,16 @@ class Model(object):
         """Compute dL/dz1 for an input x and expected output y"""
         return self.dLdf(x, y) * np.dot(self.dfda(), self.dadz1(x))
 
-    def dz1dw1(self, x):
-        return x * self.w1
-
     def dLdw1(self, x, y):
         """Compute dL/dw1 for an input x and expected output y"""
-        return self.dLdf(x, y) * np.sum(self.dfda() * self.dadz1(x) * self.dz1dw1(x))
+        return self.dLdf(x, y) * np.dot(self.dfda(), self.dadz1(x) * x)
 
     def dLdw2(self, x, y):
         """Compute dL/dw2 for an input x and expected output y"""
-        return self.dLdf(x, y) * self.dfdw2(x)
-
-    def dz1db1(self):
-        return np.ones(self.b1.shape)
+        return self.dLdf(x, y) * self.a(x) #df/dw2
 
     def dLdb1(self, x, y):
-        return self.dLdf(x, y) * np.sum(self.dfda() * self.dadz1(x) * self.dz1db1())
+        return self.dLdf(x, y) * np.dot(self.dfda(), self.dadz1(x))
 
     def backward(self, training_samples, ETA):
         for sample in training_samples:
@@ -195,12 +184,12 @@ def evaluate(model, samples):
 TRAIN_DATA, TEST_DATA = dataset_get_sin()
 # TRAIN_DATA, TEST_DATA = dataset_get_linear()
 
-MODEL = Model(10, sigmoid, d_sigmoid, DATA_TYPE)
+MODEL = Model(60, sigmoid, d_sigmoid, DATA_TYPE)
 # MODEL = Model(10, relu, d_relu, DATA_TYPE)
 
 # Train the model with some training data
 TRAINING_ITERS = 5000
-LEARNING_RATE = 0.0005
+LEARNING_RATE = 0.005
 TRAINING_SUBSET_SIZE = len(TRAIN_DATA)
 
 print TRAINING_SUBSET_SIZE