Upload New File

classification_modules/nn_utils.py

+from keras.callbacks import Callback, EarlyStopping, ModelCheckpoint
+
+
+class TrainingHistory(Callback):
+    
+    def __init__(self, x_test, y_test, CLASSES_LIST):
+        super(Callback, self).__init__()
+        self.x_test = x_test
+        self.y_test = y_test
+        self.CLASSES_LIST = CLASSES_LIST
+        
+    def on_train_begin(self, logs={}):
+        self.losses = []
+        self.epoch_losses = []
+        self.epoch_val_losses = []
+        self.val_losses = []
+        self.predictions = []
+        self.epochs = []
+        self.f1 = []
+        self.i = 0
+        self.save_every = 50
+
+    def on_epoch_end(self, epoch, logs={}):
+        
+        y_predicted = self.model.predict(self.x_test).argmax(1)
+        print(y_predicted.shape)
+        
+        print("Test Accuracy:", accuracy_score(self.y_test, y_predicted))
+    
+        p, r, f1, s = precision_recall_fscore_support(self.y_test, y_predicted, 
+                                                      average='micro',
+                                                      labels=[x for x in 
+                                                              self.CLASSES_LIST])
+        
+        print('p r f1 %.1f %.1f %.1f' % (np.average(p, weights=s)*100.0, 
+                                         np.average(r, weights=s)*100.0, 
+                                         np.average(f1, weights=s)*100.0))
+        
+        try:
+            print(classification_report(self.y_test, y_predicted, labels=[x for x in 
+                                                               self.CLASSES_LIST]))
+        except:
+            print('ZERO')
+            
+
+