Py version

main.py

@@ -0,0 +1,61 @@
+
+import numpy as np
+
+from nn import Tnn
+
+Num     = 30
+x0      = np.linspace(0,1,Num)
+y0      = np.linspace(0,1,Num)
+xx,yy   = np.meshgrid(x0,y0)
+#zz1      = np.sqrt(4**2.0 - (xx**2.0 + yy**2.0)) / 2.0
+#zz2      = np.sqrt(2**2.0 - (xx**2.0 + yy**2.0)) / np.sqrt(2)
+
+zz1     = xx*yy
+zz2     = xx/2.0 + yy / 2.0
+
+input = np.c_[xx.reshape((Num*Num,1)).copy(),yy.reshape((Num*Num,1)).copy()]
+output= np.c_[zz1.reshape((Num*Num,1)).copy(), zz2.reshape((Num*Num,1)).copy()]
+
+test_input = np.c_[np.random.random(10),np.random.random(10)]
+
+
+if __name__ == '__main__':
+    nt0 = Tnn(2,10,2)
+    n_iteration = 100
+
+    lr_cal = 1.0
+    anneal = 0.999
+
+    # start loop
+    for i0 in range(n_iteration):
+
+        err_cal = 0;
+        for j0 in range(output.shape[0]):
+            err_cal += nt0.train(input[j0,:], output[j0,:], lr_cal)
+
+        #print 'Error: ', err_cal / output.shape[0], ' lr: ', lr_cal
+        lr_cal *= anneal
+
+        # validation
+        test_prd = np.array([])
+
+        for k0 in range(test_input.shape[0]):
+            if(k0==0):
+                test_prd = np.append(test_prd, nt0.predict(test_input[k0,:]))
+            else:
+                test_prd = np.vstack([test_prd, nt0.predict(test_input[k0,:])])
+
+        # ground true
+        test_gr  = np.c_[test_input[:,0].copy()*test_input[:,1].copy(), test_input[:,1].copy()/2.0 + test_input[:,0].copy()/2.0]
+
+        err_val = 0;
+        for k0 in range(test_prd.shape[0]):
+            for m0 in range(output.shape[1]):
+                err_val += 0.5*(test_prd[k0,m0] -test_gr[k0,m0])**2.0
+
+        print 'Error: ', err_cal / output.shape[0], ' lr: ', lr_cal, 'Valid_Error: ', err_val / test_input.shape[0]
+
+
+
+
+