fix imports

backend_react/react_app.py

@@ -24,10 +24,9 @@ import sys
 sys.path.insert(0, os.path.abspath('..'))
 
 # # import agents and its functions
-from MetaAugment.autoaugment_learners import ucb_learner as UCB1_JC
-from MetaAugment.autoaugment_learners import evo_learner
-import MetaAugment.controller_networks as cn
 import MetaAugment.autoaugment_learners as aal
+import MetaAugment.controller_networks as cont_n
+import MetaAugment.child_networks as cn
 print('@@@ import successful')
 
 # import agents and its functions
@@ -194,8 +193,8 @@ def training():
 
 
     if auto_aug_learner == 'UCB':
-        policies = UCB1_JC.generate_policies(num_policies, num_sub_policies)
-        q_values, best_q_values = UCB1_JC.run_UCB1(
+        policies = aal.ucb_learner.generate_policies(num_policies, num_sub_policies)
+        q_values, best_q_values = aal.ucb_learner.run_UCB1(
                                                 policies,
                                                 batch_size, 
                                                 learning_rate, 
@@ -210,21 +209,19 @@ def training():
         best_q_values = np.array(best_q_values)
 
     elif auto_aug_learner == 'Evolutionary Learner':
-
-        network = cn.evo_controller.evo_controller(fun_num=num_funcs, p_bins=1, m_bins=1, sub_num_pol=1)
-        child_network = aal.evo.LeNet()
-        learner = aal.evo.evo_learner(
-                                    network=network, 
-                                    fun_num=num_funcs, 
-                                    p_bins=1, 
-                                    mag_bins=1, 
-                                    sub_num_pol=1, 
-                                    ds = ds, 
-                                    ds_name=ds_name, 
-                                    exclude_method=exclude_method, 
-                                    child_network=child_network
-                                    )
-
+        network = cont_n.evo_controller(fun_num=num_funcs, p_bins=1, m_bins=1, sub_num_pol=1)
+        child_network = cn.LeNet()
+        learner = aal.evo_learner(
+                                network=network, 
+                                fun_num=num_funcs, 
+                                p_bins=1, 
+                                mag_bins=1, 
+                                sub_num_pol=1, 
+                                ds = ds, 
+                                ds_name=ds_name, 
+                                exclude_method=exclude_method, 
+                                child_network=child_network
+                                )
         learner.run_instance()
     elif auto_aug_learner == 'Random Searcher':
         pass 

flask_mvp/app.py

@@ -3,7 +3,8 @@
 #     app.run(host='0.0.0.0',port=port)
 
 from numpy import broadcast
-from auto_augmentation import home, progress,result, training
+from auto_augmentation import home, progress,result
+from flask_mvp.auto_augmentation import training
 from flask_socketio import SocketIO,  send
 
 from flask import Flask, flash, request, redirect, url_for

flask_mvp/auto_augmentation/__init__.py

@@ -2,7 +2,8 @@ import os
 
 from flask import Flask, render_template, request, flash
 
-from auto_augmentation import home, progress,result, training
+from auto_augmentation import home, progress,result
+from flask_mvp.auto_augmentation import training
 
 def create_app(test_config=None):
     # create and configure the app

flask_mvp/auto_augmentation/progress.py

@@ -94,9 +94,28 @@ def response():
 
         if auto_aug_learner == 'UCB':
             policies = ucb_learner.generate_policies(num_policies, num_sub_policies)
-            q_values, best_q_values = ucb_learner.run_UCB1(policies, batch_size, learning_rate, ds, toy_size, max_epochs, early_stop_num, iterations, IsLeNet, ds_name)
+            q_values, best_q_values = ucb_learner.run_UCB1(
+                                                        policies, 
+                                                        batch_size, 
+                                                        learning_rate, 
+                                                        ds, 
+                                                        toy_size, 
+                                                        max_epochs, 
+                                                        early_stop_num, 
+                                                        iterations, 
+                                                        IsLeNet, 
+                                                        ds_name
+                                                        )    
+            best_q_values = np.array(best_q_values)
         elif auto_aug_learner == 'Evolutionary Learner':
-            learner = Evo.Evolutionary_learner(fun_num=num_funcs, p_bins=1, mag_bins=1, sub_num_pol=1, ds_name=ds_name, exclude_method=exclude_method)
+            learner = Evo.Evolutionary_learner(
+                                            fun_num=num_funcs, 
+                                            p_bins=1, 
+                                            mag_bins=1, 
+                                            sub_num_pol=1, 
+                                            ds_name=ds_name, 
+                                            exclude_method=exclude_method
+                                            )
             learner.run_instance()
         elif auto_aug_learner == 'Random Searcher':
             # As opposed to when ucb==True, `ds` and `IsLenet` are processed outside of the agent