def main(_):
action_reward_dict = {}
policy_sess = tf.Session()
#K.set_session(policy_sess)
manager = NetworkManager(FLAGS, clip_rewards=CLIP_REWARDS)
size = [len(LAYER_SIZES)] * NUM_LAYERS
reward_space = np.zeros((size))
#print(reward_space.shape)
for i in range(NUM_ENUM):
for idx, val in np.ndenumerate(reward_space):
action = [LAYER_SIZES[i] for i in idx]
#print(action)
with policy_sess.as_default():
_, acc = manager.get_rewards(model_fn, action)
print(action, acc)
acc = round(acc * JSON_SCALE, 2)
action = str(tuple(action))
if action not in action_reward_dict:
action_reward_dict[action] = [acc]
else:
action_reward_dict[action].append(acc)
action_average_reward_dict = {}
for k in action_reward_dict.keys():
action_average_reward_dict[k] = round(np.mean(action_reward_dict[k]),
2)
with open('action_reward_dict.json', 'w') as f:
json.dump(action_reward_dict, f)
f.close()
with open('action_average_reward_dict.json', 'w') as f:
json.dump(action_average_reward_dict, f)
f.close()
def main(_):
CLIP_REWARDS = False
value = [30,60,100,144]
state_space = [v for v in itertools.product(value, repeat=3)]
data = defaultdict(list)
for itr in xrange(500):
for state in state_space:
states = list(state)
manager = NetworkManager(FLAGS, clip_rewards=CLIP_REWARDS)
reward, previous_acc = manager.get_rewards(model_fn, states)
previous_acc = round(previous_acc*100000,2)
data[str(state)].append(previous_acc)
with open('data.json', 'w') as outfile:
json.dump(data, outfile)
def main(_):
CLIP_REWARDS = False
#filter_space = [v for v in itertools.product(filter_val, repeat=3)]
#stride_space = [v for v in itertools.product(stride_val, repeat=3)]
#kernal_space = [v for v in itertools.product(kernel_val, repeat=3)]
data = defaultdict(list)
with open('result.txt', 'w') as out:
for state in states:
print(state)
manager = NetworkManager(FLAGS, clip_rewards=CLIP_REWARDS)
reward, previous_acc = manager.get_rewards(model_fn_cnn, state)
previous_acc = round(previous_acc * 100000, 2)
print(previous_acc)
data[str(state)].append(previous_acc)
out.write("{} {}\n".format(state, previous_acc))
with open('data.json', 'w') as outfile:
json.dump(data, outfile)
def main(_):
CLIP_REWARDS = False
data = defaultdict(list)
with open('main_result.txt', 'w') as out:
for ite in xrange(3):
ite += 1
print('outter iteration:', ite)
iteration = 0
for state in states:
iteration += 1
print(iteration, state)
manager = NetworkManager(FLAGS, clip_rewards=CLIP_REWARDS)
reward, previous_acc = manager.get_rewards(model_fn_cnn, state)
previous_acc = round(previous_acc * 100000, 2)
print(previous_acc)
data[str(state)].append(previous_acc)
out.write("{} {}\n".format(state, previous_acc))
with open('main_sample.json', 'w') as outfile:
json.dump(data, outfile)
def main(_):
# create a shared session between Keras and Tensorflow
policy_sess = tf.Session()
K.set_session(policy_sess)
NUM_LAYERS = 3 # number of layers of the state space
MAX_TRIALS = 250 # maximum number of models generated
MAX_EPOCHS = 60 # maximum number of epochs to train
BATCHSIZE = 100 # batchsize
EXPLORATION = 0.5 # high exploration for the first 1000 steps
REGULARIZATION = 1e-3 # regularization strength
CONTROLLER_CELLS = 32 # number of cells in RNN controller
CLIP_REWARDS = False # clip rewards in the [-0.05, 0.05] range
RESTORE_CONTROLLER = True # restore controller to continue training
# construct a state space
state_space = StateSpace()
# add states
#state_space.add_state(name='kernel', values=[3])
state_space.add_state(name='filters', values=[30, 60, 100, 144])
#state_space.add_state(name='stride', values=[1])
# print the state space being searched
state_space.print_state_space()
previous_acc = 0.0
total_reward = 0.0
with policy_sess.as_default():
# create the Controller and build the internal policy network
controller = Controller(policy_sess,
NUM_LAYERS,
state_space,
reg_param=REGULARIZATION,
exploration=EXPLORATION,
controller_cells=CONTROLLER_CELLS,
restore_controller=RESTORE_CONTROLLER)
print('done')
# create the Network Manager
manager = NetworkManager(FLAGS, clip_rewards=CLIP_REWARDS)
# get an initial random state space if controller needs to predict an
# action from the initial state
state = state_space.get_random_state_space(NUM_LAYERS)
print("Initial Random State : ", state_space.parse_state_space_list(state))
#print()
# train for number of trails
for trial in range(MAX_TRIALS):
with policy_sess.as_default():
actions = controller.get_action(
state) # get an action for the previous state
# print the action probabilities
state_space.print_actions(actions)
print("Predicted actions : ",
state_space.parse_state_space_list(actions))
# build a model, train and get reward and accuracy from the network manager
reward, previous_acc = manager.get_rewards(
model_fn_cnn, state_space.parse_state_space_list(actions))
print("Rewards : ", reward, "Accuracy : ", previous_acc)
with policy_sess.as_default():
total_reward += reward
print("Total reward : ", total_reward)
# actions and states are equivalent, save the state and reward
state = actions
controller.store_rollout(state, reward)
# train the controller on the saved state and the discounted rewards
loss = controller.train_step()
print("Trial %d: Controller loss : %0.6f" % (trial + 1, loss))
# write the results of this trial into a file
with open('train_history.csv', mode='a+') as f:
data = [previous_acc, reward]
data.extend(state_space.parse_state_space_list(state))
writer = csv.writer(f)
writer.writerow(data)
print()
print("Total Reward : ", total_reward)
# clear the previous files
controller.remove_files()
# train for number of trails
for trial in range(MAX_TRIALS):
with policy_sess.as_default():
K.set_session(policy_sess)
actions = controller.get_action(
state) # get an action for the previous state
# print the action probabilities
state_space.print_actions(actions)
print("Predicted actions : ", state_space.parse_state_space_list(actions))
# build a model, train and get reward and accuracy from the network manager
reward, previous_acc = manager.get_rewards(
model_fn, state_space.parse_state_space_list(actions))
print("Rewards : ", reward, "Accuracy : ", previous_acc)
with policy_sess.as_default():
K.set_session(policy_sess)
total_reward += reward
print("Total reward : ", total_reward)
# actions and states are equivalent, save the state and reward
state = actions
controller.store_rollout(state, reward)
# train the controller on the saved state and the discounted rewards
loss = controller.train_step()
print("Trial %d: Controller loss : %0.6f" % (trial + 1, loss))
else:
k = K_
actions = controller.get_actions(
top_k=k) # get all actions for the previous state
rewards = []
for t, action in enumerate(actions):
# print the action probabilities
state_space.print_actions(action)
print("Model #%d / #%d" % (t + 1, len(actions)))
print("Predicted actions : ",
state_space.parse_state_space_list(action))
# build a model, train and get reward and accuracy from the network manager
reward = manager.get_rewards(
model_fn, state_space.parse_state_space_list(action))
print("Final Accuracy : ", reward)
rewards.append(reward)
print("\nFinished %d out of %d models ! \n" % (t + 1, len(actions)))
# write the results of this trial into a file
with open('train_history.csv', mode='a+', newline='') as f:
data = [reward]
data.extend(state_space.parse_state_space_list(action))
writer = csv.writer(f)
writer.writerow(data)
with policy_sess.as_default():
K.set_session(policy_sess)
# train the controller on the saved state and the discounted rewards
state = state_space.get_random_state_space(NUM_LAYERS)
print("Initial Random State : ", state_space.parse_state_space_list(state))
print()
# train for number of trails
for trial in range(MAX_TRIALS):
with policy_sess.as_default():
K.set_session(policy_sess)
actions = controller.get_action(state) # get an action for the previous state
# print the action probabilities
state_space.print_actions(actions)
print("Predicted actions : ", state_space.parse_state_space_list(actions))
# build a model, train and get reward and accuracy from the network manager
reward, previous_acc = manager.get_rewards(model_fn, state_space.parse_state_space_list(actions))
print("Rewards : ", reward, "Accuracy : ", previous_acc)
with policy_sess.as_default():
K.set_session(policy_sess)
total_reward += reward
print("Total reward : ", total_reward)
# actions and states are equivalent, save the state and reward
state = actions
controller.store_rollout(state, reward)
# train the controller on the saved state and the discounted rewards
loss = controller.train_step()
print("Trial %d: Controller loss : %0.6f" % (trial + 1, loss))
k = None
else:
k = K
actions = controller.get_actions(
top_k=k) # get all actions for the previous state
rewards = []
for t, action in enumerate(actions):
# print the action probabilities
state_space.print_actions(action)
print("Model #%d / #%d" % (t + 1, len(actions)))
print(" ", state_space.parse_state_space_list(action))
# build a model, train and get reward and accuracy from the network manager
reward = manager.get_rewards(
ModelGenerator, state_space.parse_state_space_list(action))
print("Final Accuracy : ", reward)
rewards.append(reward)
print("\nFinished %d out of %d models ! \n" % (t + 1, len(actions)))
# write the results of this trial into a file
with open('train_history.csv', mode='a+', newline='') as f:
data = [reward]
data.extend(state_space.parse_state_space_list(action))
writer = csv.writer(f)
writer.writerow(data)
loss = controller.train_step(rewards)
print("Trial %d: ControllerManager loss : %0.6f" % (trial + 1, loss))
print("This is time to explore design", end - start, "\n")
start = time.time()
time_performance = M_SCHEDULE.schedule_run(layers, layersname)
# We set performance according to the previous iteration
#OPT_TIMEPERFORMANCE = min(time_performance,OPT_TIMEPERFORMANCE)
end = time.time()
print("PPPPPPPPPPPPPPPPPPPPP This is time performance", time_performance,
"\n", "This is time to evaluate time formance", end - start, "\n",
"\n", "\n")
per_list.append(time_performance)
# build a model, train and get reward and accuracy from the network manager
reward, previous_acc = manager.get_rewards(
model_fn, state_space.parse_state_space_list(actions),
time_performance,
OPT_TIMEPERFORMANCE) # CNN train and return the best accura
print("Rewards : ", reward, "Accuracy : ", previous_acc)
acc_list.append(previous_acc)
rew_list.append(reward)
print("===============+WWW+=================")
print("++++++++++Acc History", acc_list)
print("++++++++++Per History", per_list)
print("++++++++++Rew History", rew_list)
print("=====================================")
#OPT_TIMEPERFORMANCE = min(time_performance*(1+(old_acc-previous_acc)),OPT_TIMEPERFORMANCE)
old_acc = previous_acc
with policy_sess.as_default():
K.set_session(policy_sess)
def main_training():
# Number of GPUs available. Use 0 for CPU mode.
ngpu = torch.cuda.device_count()
# Decide which device we want to run on
device = torch.device("cuda:0" if (
torch.cuda.is_available() and ngpu > 0) else "cpu")
train_loader, val_loader, test_loader = get_dataset(
CHILD_BATCHSIZE, '/tmp/datasets/cifar-10')
dataloaders = [train_loader, val_loader, test_loader]
# construct a state space
state_space = StateSpace()
# add states
state_space.add_state(name='kernel', values=[1, 3, 5, 7])
state_space.add_state(name='filters', values=[24, 36, 48, 64])
previous_acc = 0.0
total_reward = 0.0
controller = Controller(NUM_LAYERS, state_space)
manager = NetworkManager(dataloaders, device, epochs=2)
# get an initial random state space if controller needs to predict an action from the initial state
state = state_space.get_random_state_space(NUM_LAYERS)
print("Initial Random State : ", state_space.parse_state_space_list(state))
for trial in range(MAX_TRIALS):
actions, prob_actions = controller.get_action(
state) # get an action for the previous state
# print the action probabilities
state_space.print_actions(actions)
print("Predicted actions : ",
state_space.parse_state_space_list(actions))
# build a model, train and get reward and accuracy from the network manager
model = ChildNetwork(state_space.parse_state_space_list(actions))
reward, previous_acc = manager.get_rewards(model)
print("Rewards : ", reward, "Accuracy : ", previous_acc)
total_reward += reward
print("Total reward : ", total_reward)
# actions and states are equivalent, save the state and reward
state = actions
prob_state = prob_actions
controller.store_rollout(state, reward, prob_state)
# train the controller on the saved state and the discounted rewards
loss = controller.update_policy()
print("Trial %d: Controller loss : %0.6f" % (trial + 1, loss))
# write the results of this trial into a file
with open('train_history.csv', mode='a+') as f:
data = [previous_acc, reward]
data.extend(state_space.parse_state_space_list(state))
writer = csv.writer(f)
writer.writerow(data)
print()
print("Total Reward : ", total_reward)