final version

model v2/get_mnist.py

@@ -24,4 +24,4 @@ def get_mnist_data(n_samples=5000):
             
     return mnist_data
 
-#data=get_mnist_data()
\ No newline at end of file
+data=get_mnist_data()
\ No newline at end of file

model v2/model_mnist.py

@@ -10,6 +10,7 @@ val_after_iter= 10 # evaluate the training process after number of iterations
 #n_samples= 500     # how many data samples should be used during training
 
 learning_rate= 0.1   # learning rate
+n_epoch=20
 
 class Model:
     
@@ -80,10 +81,11 @@ class Model:
         
         @param data: matrix that contains 100 features and 1 label
         '''
+
         
-        random.shuffle(data)
-        
-        for epoch in range(0,1):
+        for epoch in range(0,n_epoch):
+            
+            random.shuffle(data)
             
             outputs=[]
             error=0
@@ -97,7 +99,7 @@ class Model:
                 
                 x=np.reshape(x, [784,1]) #bring the features into right shape
                 zeros=np.zeros(shape=[N_OUTPUT,1])
-                zeros[label]=1
+                zeros[int(label)]=1
                 y=zeros
             
                 z_=[] # storage of neurons values before activation
@@ -148,7 +150,7 @@ class Model:
                 
 if __name__ == "__main__":    
     
-    data=get_mnist_data()   
+    data=get_mnist_data(n_samples=5000)   
     model=Model()
     model.train(data)