added progress bar

model.py

@@ -6,6 +6,7 @@ from random import seed, random
 from pprint import pprint as pp
 from math import log, exp
 import numpy as np
+from fractions import gcd
 
 removed=[]
 
@@ -90,7 +91,7 @@ def predict(model, x):
     return 1-np.argmax(h)
 
 
-def fit(model, X, y, passes=1000):
+def fit(model, X, y, passes=1000, verbose=False):
     m = X.shape[0]
     w1,w2 = model['w1'],model['w2'] #58x28, 29x2
     li,lh,lo=model['li'],model['lh'],model['lo']
@@ -128,7 +129,9 @@ def fit(model, X, y, passes=1000):
         w1,w2 = ws[idx][0],ws[idx][1]
         model['w1'],model['w2'] = w1,w2
 
-        if i % (passes/10)==0:
+        print("training... %0.2f%%\r" % (i*100/passes), end='')
+
+        if verbose and i % (passes/10)==0:
             print(i,costs[idx])
 
     return model
@@ -154,9 +157,10 @@ if __name__ == "__main__":
     #     model = {'li':57,'lh':h,'lo':2,'lambda':0.1,'eta':0.1}    
     #     model['w1'] = np.random.randn(model['li']+1, model['lh'])/np.sqrt(model['li']+1)  #58x28
     #     model['w2'] = np.random.randn(model['lh']+1, model['lo'])/np.sqrt(model['lh']+1)    #29x2
-        
+         
     model = fit(model, X, y, passes=1500)
 
+    print("\npredicting...")
     m = X.shape[0]
     py,y2=[],[]
     for i,row in enumerate(tX):
@@ -173,5 +177,7 @@ if __name__ == "__main__":
         for i,ans in enumerate(py):
             writer.writerow([i,ans])
 
+    print("done...")
+
     # acc = m-np.sum(abs(np.array(py)-np.array(y2)))
     # print(acc*100/m)