# file_name = "COMPLETE_CIFAR10.csv"
# data = open_file(file_name)
data = get_data_from_csv(file_name)
data = format_data_without_header(data)
dataset = "mnist"
best_topology = run_q_learning(data,dataset)
print("best_topology: ", best_topology)
# accuracy, loss = to_verify_model(best_topology)
'''
#Get random topologies then save to csv file
# random_topology_file = 'test_random_topology.csv'
# num_model = 1500
# file_name = get_random_topology(num_model, random_topology_file)
# print(file_name)
# pre_train_model_cifar10(file_name)
#Run Q-learning to find best topology
# file_name = "fixed_model_dict.csv"
# file_name = "bad_model.csv"
# file_name = "biased_dict.csv"
file_name = "COMPLETE_CIFAR10.csv"
# data = open_file(file_name)
data = get_data_from_csv(file_name)
data = format_data_without_header(data)
# print(data[0])
dataset = "cifar10"
best_topology = run_q_learning(data, dataset)
print("best_topology: ", best_topology)
# accuracy, loss = to_verify_model(best_topology)
DATASET = "mnist"
best_topology = run_q_learning(data,DATASET)
print("best_topology: ", best_topology)
# verify_model(best_topology,DATASET)
'''
'''
#Get random topologies then save to csv file
INPUT_FILE_NAME_RANDOM_TOPO = 'test_random_topology.csv'
NUM_MODEL = 1500
OUTPUT_FILE_NAME = "new_trained_cifar10.csv"
INPUT_FILE_NAME = get_random_topology(NUM_MODEL, INPUT_FILE_NAME_RANDOM_TOPO)
print(INPUT_FILE_NAME)
pre_train_model_cifar10(INPUT_FILE_NAME,OUTPUT_FILE_NAME)
'''
#Run Q-learning to find best topology
file_name = "COMPLETE_CIFAR10.csv"
data = get_data_from_csv(file_name)
data = format_data_without_header(data)
DATASET = "cifar10"
best_topology = run_q_learning(data,DATASET)
print("best_topology: ", best_topology)
verify_model(best_topology,DATASET)
'''
model = ['c_1','c_6','c_5','m_2']
# DATASET = 'cifar10'
DATASET = 'mnist'
verify_model(model, DATASET)
'''
# from TRAIN_MODEL_MNIST import train
from RANDOM_TOPOLOGY import get_random_topology
# from TRAIN_MODEL_MNIST import pre_train_model, to_verify_model
if __name__ == "__main__":
# random_topology_file = 'test_random_topology.csv'
# num_model = 1500
# file_name = get_random_topology(num_model, random_topology_file)
# print(file_name)
# pre_train_model(file_name)
file_name = "fixed_model_dict.csv"
# file_name = "bad_model.csv"
# file_name = "biased_dict.csv"
data = open_file(file_name)
best_topology = run_q_learning(data)
print("best_topology: ", best_topology)
# accuracy, loss = to_verify_model(best_topology)
#
# first_layer = best_topology['Layer 1']
# print("first_layer:",first_layer)
# second_layer = best_topology['Layer 2']
# print("second_layer:",second_layer)
#
# third_layer = best_topology['Layer 3']
# print("third_layer:",third_layer)
#
# if third_layer[0] == 's':
# forth_layer = '-'
# else: