Added CrossEntropy

networks/layers/layer.py

@@ -203,7 +203,10 @@ def loss_reg(self):
 
     def backprop(self,dOut=None):
         return self.dx
-
+
+    def accuracy(self,scores,y):
+        return 1.0*np.sum(np.argmax(scores,axis=1)==y)/y.shape[0]
+
 class SVM():
 
     def __init__(self):
@@ -222,6 +225,9 @@ def loss_reg(self):
 
     def backprop(self,dOut=None):
         return self.dx
+
+    def accuracy(self,scores,y):
+        return 1.0*np.sum(np.argmax(scores,axis=1)==y)/y.shape[0]
 
 def MSE():
 
@@ -241,6 +247,9 @@ def loss_reg(self):
 
     def backprop(self,dOut=None):
         return self.dx
+
+    def accuracy(self,scores,y):
+        return rel_error(scores,y)
 
 class CrossEntropy():
 
@@ -260,7 +269,10 @@ def loss_reg(self):
 
     def backprop(self,dOut=None):
         return self.dx
-
+
+    def accuracy(self,scores,y):
+        return 1.0*np.sum(round(scores,0)==y)/y.shape[0]
+
 class BatchNormalization(object):
 
     def __init__(self,gamma,beta,params,update_params):

networks/network.py

@@ -211,10 +211,7 @@ def batch_test(self,X,y):
             loss += layer.loss_reg()
 
         scores,inp = self.layers[-1].forward(inp,y)
-        return self.accuracy(scores,y),inp+loss
-
-    def accuracy(self,scores,y):
-        return 1.0*np.sum(np.argmax(scores,axis=1)==y)/y.shape[0]
+        return self.layers[-1].accuracy(scores,y),inp+loss
 
     def predict(self,X):
         inp = X

setup.py

@@ -3,12 +3,12 @@
 setup(
     name = 'networks',
     packages = ['networks','networks/layers','networks/layers/util/','networks/layers/descent/'],
-    version = '0.3.5',  # Ideally should be same as your GitHub release tag varsion
+    version = '0.3.6',  # Ideally should be same as your GitHub release tag varsion
     description = 'Allows to create NN models',
     author = 'Shharrnam Chhatpar',
     author_email = '[email protected]',
     url = 'https://github.com/sharnam19/Networks',
-    download_url = 'https://github.com/sharnam19/Networks/archive/v0.3.5.tar.gz',
+    download_url = 'https://github.com/sharnam19/Networks/archive/v0.3.6.tar.gz',
     keywords = ['machine-learning', 'deep-learning','neural-networks','linear regression',
                 'logistic regression'],
     classifiers = [],