diff --git a/auto_augmentation/training.py b/auto_augmentation/training.py
index 7eaac2f322cdbb5c3444bbc552b1b0340b23ab78..b8b3521a52c8806aca3830da0d838c8266a8f801 100644
--- a/auto_augmentation/training.py
+++ b/auto_augmentation/training.py
@@ -29,25 +29,30 @@ bp = Blueprint("training", __name__)
@bp.route("/start_training", methods=["GET", "POST"])
def response():
- # hyperparameters to change
- # auto_aug_learner = session
+ # aa learner
auto_aug_learner = current_app.config.get('AAL')
+ # auto_aug_learner = session
+ # search space & problem setting
+ ds = current_app.config.get('ds')
+ ds_name = current_app.config.get('DSN')
+ exclude_method = current_app.config.get('exc_meth')
+ num_funcs = current_app.config.get('NUMFUN')
num_policies = current_app.config.get('NP')
num_sub_policies = current_app.config.get('NSP')
- batch_size = current_app.config.get('BS')
- learning_rate = current_app.config.get('LR')
toy_size = current_app.config.get('TS')
- max_epochs = current_app.config.get('ME')
+
+ # child network
+ IsLeNet = current_app.config.get('ISLENET')
+
+ # child network training hyperparameters
+ batch_size = current_app.config.get('BS')
early_stop_num = current_app.config.get('ESN')
iterations = current_app.config.get('IT')
- IsLeNet = current_app.config.get('ISLENET')
- ds_name = current_app.config.get('DSN')
- num_funcs = current_app.config.get('NUMFUN')
- ds = current_app.config.get('ds')
- exclude_method = current_app.config.get('exc_meth')
+ learning_rate = current_app.config.get('LR')
+ max_epochs = current_app.config.get('ME')
if auto_aug_learner == 'UCB':
diff --git a/backend_react/react_app.py b/backend_react/react_app.py
index 121760dd5dd807f5fa3190d6dc254dd7f5649937..21f5e8a2a9ae99d4b058931f2e912aa116d7ee0b 100644
--- a/backend_react/react_app.py
+++ b/backend_react/react_app.py
@@ -138,20 +138,28 @@ def get_form_data():
@app.route('/confirm', methods=['POST', 'GET'])
def confirm():
print('inside confirm')
+
+ # aa learner
auto_aug_learner = current_app.config.get('AAL')
+
+ # search space & problem setting
+ ds = current_app.config.get('ds')
+ ds_name = current_app.config.get('DSN')
+ exclude_method = current_app.config.get('exc_meth')
num_policies = current_app.config.get('NP')
num_sub_policies = current_app.config.get('NSP')
- batch_size = current_app.config.get('BS')
- learning_rate = current_app.config.get('LR')
+ num_funcs = current_app.config.get('NUMFUN')
toy_size = current_app.config.get('TS')
- max_epochs = current_app.config.get('ME')
+
+ # child network
+ IsLeNet = current_app.config.get('ISLENET')
+
+ # child network training hyperparameters
+ batch_size = current_app.config.get('BS')
early_stop_num = current_app.config.get('ESN')
iterations = current_app.config.get('IT')
- IsLeNet = current_app.config.get('ISLENET')
- ds_name = current_app.config.get('DSN')
- num_funcs = current_app.config.get('NUMFUN')
- ds = current_app.config.get('ds')
- exclude_method = current_app.config.get('exc_meth')
+ learning_rate = current_app.config.get('LR')
+ max_epochs = current_app.config.get('ME')
data = {'ds': ds, 'ds_name': ds_name, 'IsLeNet': IsLeNet, 'ds_folder.filename': ds_name,
'auto_aug_learner':auto_aug_learner, 'batch_size': batch_size, 'learning_rate': learning_rate,
@@ -161,20 +169,28 @@ def confirm():
# ========================================================================
@app.route('/training', methods=['POST', 'GET'])
def training():
+
+ # aa learner
auto_aug_learner = current_app.config.get('AAL')
+
+ # search space & problem setting
+ ds = current_app.config.get('ds')
+ ds_name = current_app.config.get('DSN')
+ exclude_method = current_app.config.get('exc_meth')
+ num_funcs = current_app.config.get('NUMFUN')
num_policies = current_app.config.get('NP')
num_sub_policies = current_app.config.get('NSP')
- batch_size = current_app.config.get('BS')
- learning_rate = current_app.config.get('LR')
toy_size = current_app.config.get('TS')
- max_epochs = current_app.config.get('ME')
+
+ # child network
+ IsLeNet = current_app.config.get('ISLENET')
+
+ # child network training hyperparameters
+ batch_size = current_app.config.get('BS')
early_stop_num = current_app.config.get('ESN')
iterations = current_app.config.get('IT')
- IsLeNet = current_app.config.get('ISLENET')
- ds_name = current_app.config.get('DSN')
- num_funcs = current_app.config.get('NUMFUN')
- ds = current_app.config.get('ds')
- exclude_method = current_app.config.get('exc_meth')
+ learning_rate = current_app.config.get('LR')
+ max_epochs = current_app.config.get('ME')
if auto_aug_learner == 'UCB':
diff --git a/flask_mvp/auto_augmentation/progress.py b/flask_mvp/auto_augmentation/progress.py
index abe15fe35fb226fe94c30d513e5634b22a72c30a..ac51b8d807cb2ad9c7c37020a7073a408020dac5 100644
--- a/flask_mvp/auto_augmentation/progress.py
+++ b/flask_mvp/auto_augmentation/progress.py
@@ -18,7 +18,7 @@ from tqdm import trange
torch.manual_seed(0)
# import agents and its functions
-from MetaAugment import UCB1_JC_py as UCB1_JC
+from MetaAugment.autoaugment_learners import ucb_learner
from MetaAugment import Evo_learner as Evo
@@ -38,21 +38,28 @@ def response():
if request.method == 'POST':
+ # generate random policies at start
+ auto_aug_learner = request.form.get("auto_aug_selection")
+
+ # search space & problem setting
+ ds = request.form.get("dataset_selection") # pick dataset (MNIST, KMNIST, FashionMNIST, CIFAR10, CIFAR100)
+ ds_up = request.files['dataset_upload']
exclude_method = request.form.getlist("action_space")
num_funcs = 14 - len(exclude_method)
+ num_policies = 5 # fix number of policies
+ num_sub_policies = 5 # fix number of sub-policies in a policy
+ toy_size = 1 # total propeortion of training and test set we use
- batch_size = 1 # size of batch the inner NN is trained with
- learning_rate = 1e-1 # fix learning rate
- ds = request.form.get("dataset_selection") # pick dataset (MNIST, KMNIST, FashionMNIST, CIFAR10, CIFAR100)
- ds_up = request.files['dataset_upload']
+ # child network
+ IsLeNet = request.form.get("network_selection") # using LeNet or EasyNet or SimpleNet ->> default
nw_up = childnetwork = request.files['network_upload']
- toy_size = 1 # total propeortion of training and test set we use
- max_epochs = 10 # max number of epochs that is run if early stopping is not hit
+
+ # child network training hyperparameters
+ batch_size = 1 # size of batch the inner NN is trained with
early_stop_num = 10 # max number of worse validation scores before early stopping is triggered
- num_policies = 5 # fix number of policies
- num_sub_policies = 5 # fix number of sub-policies in a policy
iterations = 5 # total iterations, should be more than the number of policies
- IsLeNet = request.form.get("network_selection") # using LeNet or EasyNet or SimpleNet ->> default
+ learning_rate = 1e-1 # fix learning rate
+ max_epochs = 10 # max number of epochs that is run if early stopping is not hit
# if user upload datasets and networks, save them in the database
@@ -83,16 +90,15 @@ def response():
childnetwork = request.files['network_upload']
childnetwork.save('./MetaAugment/child_networks/'+childnetwork.filename)
- # generate random policies at start
- auto_aug_leanrer = request.form.get("auto_aug_selection")
- if auto_aug_leanrer == 'UCB':
- policies = UCB1_JC.generate_policies(num_policies, num_sub_policies)
- q_values, best_q_values = UCB1_JC.run_UCB1(policies, batch_size, learning_rate, ds, toy_size, max_epochs, early_stop_num, iterations, IsLeNet, ds_name)
- elif auto_aug_leanrer == 'Evolutionary Learner':
+
+ 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)
+ 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.run_instance()
- elif auto_aug_leanrer == 'Random Searcher':
+ elif auto_aug_learner == 'Random Searcher':
# As opposed to when ucb==True, `ds` and `IsLenet` are processed outside of the agent
# This system makes more sense for the user who is not using the webapp and is instead
# using the library within their code
@@ -157,7 +163,7 @@ def response():
test_dataset,
child_network_architecture=model,
iterations=iterations)
- elif auto_aug_leanrer == 'Genetic Learner':
+ elif auto_aug_learner == 'Genetic Learner':
pass
plt.figure()
@@ -165,8 +171,8 @@ def response():
# 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, batch_size, learning_rate, ds, toy_size, max_epochs, early_stop_num, iterations, IsLeNet, ds_name)
+ # 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)
# # plt.figure()
# # plt.plot(q_values)
# best_q_values = np.array(best_q_values)