Update train.py

avoid ensembling if no model weights met the threshold f1 score in the case of classification

train.py

@@ -269,35 +269,25 @@ def run(_config, n_classes, n_epochs, input_height,
         list_classes = list(classification_classes_name.values())
         testX, testY = generate_data_from_folder_evaluation(dir_eval, input_height, input_width, n_classes, list_classes)
 
-        #print(testY.shape, testY)
-
         y_tot=np.zeros((testX.shape[0],n_classes))
-        indexer=0
 
         score_best=[]
         score_best.append(0)
 
         num_rows = return_number_of_total_training_data(dir_train)
-
         weights=[]
         
         for i in range(n_epochs):
-            #history = model.fit(trainX, trainY, epochs=1, batch_size=n_batch, validation_data=(testX, testY), verbose=2)#,class_weight=weights)
-            history = model.fit( generate_data_from_folder_training(dir_train, n_batch , input_height, input_width, n_classes, list_classes), steps_per_epoch=num_rows / n_batch, verbose=0)#,class_weight=weights)
+            history = model.fit( generate_data_from_folder_training(dir_train, n_batch , input_height, input_width, n_classes, list_classes), steps_per_epoch=num_rows / n_batch, verbose=1)#,class_weight=weights)
             
             y_pr_class = []
             for jj in range(testY.shape[0]):
                 y_pr=model.predict(testX[jj,:,:,:].reshape(1,input_height,input_width,3), verbose=0)
                 y_pr_ind= np.argmax(y_pr,axis=1)
-                #print(y_pr_ind, 'y_pr_ind')
                 y_pr_class.append(y_pr_ind)
             
-            
             y_pr_class = np.array(y_pr_class)
-            #model.save('./models_save/model_'+str(i)+'.h5')
-            #y_pr_class=np.argmax(y_pr,axis=1)
             f1score=f1_score(np.argmax(testY,axis=1), y_pr_class, average='macro')
-            
             print(i,f1score)
             
             if f1score>score_best[0]:
@@ -306,30 +296,20 @@ def run(_config, n_classes, n_epochs, input_height,
                 
             if f1score > f1_threshold_classification:
                 weights.append(model.get_weights() )
-                y_tot=y_tot+y_pr
-                
-                indexer+=1
-        y_tot=y_tot/float(indexer)
-
                 
-        new_weights=list()
 
-        for weights_list_tuple in zip(*weights):
-            new_weights.append( [np.array(weights_).mean(axis=0) for weights_ in zip(*weights_list_tuple)]  )
+        if len(weights) >= 1:
+            new_weights=list()
+            for weights_list_tuple in zip(*weights):
+                new_weights.append( [np.array(weights_).mean(axis=0) for weights_ in zip(*weights_list_tuple)]  )
                 
-        new_weights = [np.array(x) for x in new_weights]
+            new_weights = [np.array(x) for x in new_weights]
+            model_weight_averaged=tf.keras.models.clone_model(model)
+            model_weight_averaged.set_weights(new_weights)
     
-        model_weight_averaged=tf.keras.models.clone_model(model)
-        
-        model_weight_averaged.set_weights(new_weights)
-
-        #y_tot_end=np.argmax(y_tot,axis=1)
-        #print(f1_score(np.argmax(testY,axis=1), y_tot_end, average='macro'))
-
-        ##best_model.save('model_taza.h5')
-        model_weight_averaged.save(os.path.join(dir_output,'model_ens_avg'))
-        with open(os.path.join( os.path.join(dir_output,'model_ens_avg'), "config.json"), "w") as fp:
-            json.dump(_config, fp)  # encode dict into JSON
+            model_weight_averaged.save(os.path.join(dir_output,'model_ens_avg'))
+            with open(os.path.join( os.path.join(dir_output,'model_ens_avg'), "config.json"), "w") as fp:
+                json.dump(_config, fp)  # encode dict into JSON
             
         with open(os.path.join( os.path.join(dir_output,'model_best'), "config.json"), "w") as fp:
             json.dump(_config, fp)  # encode dict into JSON