def __init__(self,
dqn_path,
banchmark_q,
cloud_backend,
recent_zone=10,
explor_rate=0.2,
acc_threshold=0.8,
reward_threshold=0.45):
# reward_threshold=0.35):
feature_extractor = MobileNetV2(include_top=False)
x = feature_extractor.output
x = AveragePooling2D(pool_size=(4, 4))(x)
self.feature_extractor = Model(inputs=feature_extractor.input,
outputs=x)
self.agent = DQN_Agent(s_dim=1280,
a_dim=10,
epsilon_decay=0.99,
epsilon_min=0.02,
gamma=0.95,
replay_batchsize=256)
self.agent.model = load_model(dqn_path)
self.dqn_path = dqn_path
self.agent.curr_exploration_rate = 0
self.STATUS = "INFERENCE" # INITIAL_TRAIN, INFERENCE, ESTIMATE, RETRAIN
# self.STATUS = "INITIAL_TRAIN"
self.STATUS = "RETRAIN"
self.cloud_backend = cloud_backend
self.banchmark_q = banchmark_q
self.explor_rate = explor_rate
self.recent_zone = recent_zone
self.reward_threshold = reward_threshold
self.acc_threshold = acc_threshold
self.grad_scale_factor = 3
self.agent_memory = defaultdict(list)
self.running_log = defaultdict(list)
self.last_env_step = None
self.step_count = 0
self.train_count = 0
self.load_model = 1
# self.flag = 'DNIM'
self.flag = 'FLIR'
(imagenet_train_path, img_class, image_name))
image_paths.append(sample_image_path)
return image_paths
imagenet_paths = _gen_sample_set_imagenet('/home/imagenet-data/train/', 2)
#MobileNetV2
# feature_extractor = MobileNetV2(include_top=False)
# x = feature_extractor.output
# x = AveragePooling2D(pool_size=(4, 4))(x)
# feature_extractor = Model(inputs=feature_extractor.input, outputs=x)
# agent
agent = DQN_Agent(s_dim=1280,
a_dim=10,
epsilon_decay=0.99,
epsilon_min=0.02,
gamma=0.95,
replay_batchsize=256)
agent1 = DQN_Agent(s_dim=1280,
a_dim=10,
epsilon_decay=0.99,
epsilon_min=0.02,
gamma=0.95,
replay_batchsize=256)
agent2 = DQN_Agent(s_dim=1280,
a_dim=10,
epsilon_decay=0.99,
epsilon_min=0.02,
gamma=0.95,
replay_batchsize=256)
agent3 = DQN_Agent(s_dim=1280,
