def train(mnist):
global args
sess = tf.Session()
global_step = tf.Variable(0, trainable=False)
starter_learning_rate = 0.1
learning_rate = tf.train.exponential_decay(0.99,
global_step,
500,
0.96,
staircase=True)
optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate)
###args.maxlayers set in parse_args and policy_network is function
reinforce = Reinforce(sess, optimizer, policy_network, args.max_layers,
global_step)
###define data and how to handle
net_manager = NetManager(num_input=3072,
num_classes=10,
learning_rate=0.001,
mnist=mnist,
bathc_size=100,
max_step_per_action=1000)
MAX_EPISODES = 500
step = 0
state = np.array([[10.0, 128.0, 1.0, 1.0] * args.max_layers],
dtype=np.float32)
pre_acc = 0.0
total_rewards = 0
for i_episode in range(MAX_EPISODES):
action = reinforce.get_action(state)
print("ca:", action)
###Also a for loop
if all(ai > 0 for ai in action[0][0]):
reward, pre_acc = net_manager.get_reward(action, step, pre_acc)
print("=====>", reward, pre_acc)
else:
reward = -1.0
total_rewards += reward
# In our sample action is equal state
state = action[0]
reinforce.storeRollout(state, reward)
step += 1
ls = reinforce.train_step(1)
log_str = "current time: " + str(
datetime.datetime.now().time()) + " episode: " + str(
i_episode) + " loss: " + str(ls) + " last_state: " + str(
state) + " last_reward: " + str(reward) + "\n"
log = open("lg3.txt", "a+")
log.write(log_str)
log.close()
print(log_str)
def train(mnist):
global args
sess = tf.Session()
global_step = tf.Variable(0, trainable=False)
learning_rate = tf.train.exponential_decay(0.99,
global_step,
500,
0.96,
staircase=True)
optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate)
reinforce = Reinforce(sess, optimizer, policy_network, args.max_layers,
global_step)
net_manager = NetManager(num_input=784,
num_classes=10,
learning_rate=0.001,
mnist=mnist,
batch_size=100)
MAX_EPISODES = 2500
step = 0
state = np.array([[10.0, 128.0, 1.0, 1.0] * args.max_layers],
dtype=np.float32)
pre_acc = 0.0
total_rewards = 0
for i_episode in range(MAX_EPISODES):
action = reinforce.get_action(state)
print("action:", action)
if all(ai > 0 for ai in action[0][0]):
reward, pre_acc = net_manager.get_reward(action, step, pre_acc)
print('====>', reward, pre_acc)
else:
reward = -1.0
total_rewards += reward
# In our sample action is equal state
state = action[0]
reinforce.store_rollout(state, reward)
step += 1
ls = reinforce.train_step(1)
log_str = 'current time: ' + str(
datetime.datetime.now().time()) + ' episode: ' + str(
i_episode) + ' loss: ' + str(ls) + ' last_state: ' + str(
state) + ' last_reward: ' + str(reward) + '\n'
log = open('log.txt', 'a+')
log.write(log_str)
log.close()
print(log_str)
def train(dataset,
learning_rate=0.001,
batch_size=100,
num_input=784,
num_classes=10,
train_size=100000,
test_size=10000):
global args
sess = tf.Session()
global_step = tf.Variable(0, trainable=False)
starter_learning_rate = 0.1
learning_rate_op = tf.train.exponential_decay(0.99,
global_step,
500,
0.96,
staircase=True)
optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate_op)
reinforce = Reinforce(sess, optimizer, policy_network, args.max_layers,
global_step)
net_manager = NetManager(num_input=num_input,
num_classes=num_classes,
learning_rate=learning_rate,
dataset=dataset,
bathc_size=batch_size,
train_size=train_size,
test_size=test_size)
MAX_EPISODES = 2500
step = 0
state = np.array([[10.0, 128.0, 1.0, 1.0] * args.max_layers],
dtype=np.float32)
pre_acc = 0.0
total_rewards = 0
max_acc = 0.0
max_action = None
for i_episode in range(MAX_EPISODES):
action = reinforce.get_action(state)
print("ca:", action)
if all(ai > 0 for ai in action[0][0]):
reward, pre_acc = net_manager.get_reward(action, step, pre_acc)
print("=====>", reward, pre_acc)
else:
reward = -1.0
total_rewards += reward
# In our sample action is equal state
state = action[0]
reinforce.storeRollout(state, reward)
step += 1
ls = reinforce.train_step(1)
log_str = "current time: " + str(
datetime.datetime.now().time()) + " episode: " + str(
i_episode) + " loss: " + str(ls) + " last_state: " + str(
state) + " last_reward: " + str(reward) + "\n"
log_max_str = "current time: " + str(datetime.datetime.now().time(
)) + " episode: " + str(i_episode) + " max accuracy: " + str(
max_acc) + " action: " + str(max_action) + "\n"
log = open("lg3.txt", "a+")
log.write(log_str)
log.write(log_max_str)
log.close()
print(log_str)
print(log_max_str)
def train(mnist):
# use globa varialbe args
global args
# create session to run code
sess = tf.Session()
# make global_step
global_step = tf.Variable(0, trainable=False)
# intial lerarning rate # TODO NOT USED VARIALBE. MAY BE USE TO LEARING_RATE at below line's first param
starter_learning_rate = 0.1
# apply exponential learning rate decay, start_lr=0.99, global_step, decay_step=500, decay_rate=0.96, staircase= True
# staircase => divide by int
learning_rate = tf.train.exponential_decay(0.99, global_step,
500, 0.96, staircase=True)
# RMSPropOptimizer use above lr
optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate)
# make reinfoce env
reinforce = Reinforce(sess, optimizer, policy_network, args.max_layers, global_step)
# network manager for training subnetworks with Reinforcement Learning
net_manager = NetManager(num_input=784, # input dim 28x28 mnist
num_classes=10, # number of classes 10 mnist
learning_rate=0.001, # intial learning rate
mnist=mnist, # dataset mnist (tensorflow dataset object)
bathc_size=100) # mini-batch size
# maximum episodes to training
MAX_EPISODES = 2500
# step start from 0
step = 0
# intial state [cnn_filter_size, cnn_filter_num, maxpool_ksize, dropout_rate] * max_layer
state = np.array([[10.0, 128.0, 1.0, 1.0]*args.max_layers], dtype=np.float32)
# init previous accuracy and total rewards
pre_acc = 0.0
total_rewards = 0
# run episodes
for i_episode in range(MAX_EPISODES):
# get next action from reinforce
action = reinforce.get_action(state)
# print action
print("ca:", action)
# if actions value is all biger then 0 (valid) get reworkd else...
if all(ai > 0 for ai in action[0][0]):
reward, pre_acc = net_manager.get_reward(action, step, pre_acc)
print("=====>", reward, pre_acc)
# else reword -1
else:
reward = -1.0
# sum all rewards
total_rewards += reward
# In our sample action is equal state
state = action[0]
# rollout state. See reinforce code
reinforce.storeRollout(state, reward)
# step
step += 1
# train step
ls = reinforce.train_step(1)
# logging
log_str = "current time: "+str(datetime.datetime.now().time())+" episode: "+str(i_episode)+" loss: "+str(ls)+" last_state: "+str(state)+" last_reward: "+str(reward)+"\n"
log = open("lg3.txt", "a+")
log.write(log_str)
log.close()
print(log_str)
def train():
global args
sess = tf.Session()
global_step = tf.Variable(0, trainable=False)
starter_learning_rate = 0.1
num_of_hyperparameters = 1
learning_rate = tf.train.exponential_decay(0.99,
global_step,
500,
0.96,
staircase=True)
optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate)
reinforce = Reinforce(sess, optimizer, policy_network, args.max_layers,
global_step, num_of_hyperparameters)
workflow_manager = WorkflowManager(num_of_hyperparameters,
ser_url=None,
usr_name=None,
password=None)
MAX_EPISODES = 2500
step = 0
state = np.array([[10.0] * num_of_hyperparameters * args.max_layers],
dtype=np.float32)
pre_acc = 0.0
total_rewards = 0
min_action = 0
max_action = 30
for i_episode in range(MAX_EPISODES):
action = reinforce.get_action(state)
print("ca:", action)
if all(ai > min_action
for ai in action[0][0]) and all(ai < max_action
for ai in action[0][0]):
reward, pre_acc = workflow_manager.get_reward(
action, step, pre_acc)
print("=====>", reward, pre_acc)
else:
reward = -1.0
total_rewards += reward
# In our sample action is equal state
print('action', action)
state = action[0]
reinforce.storeRollout(state, reward)
print('state', state)
step += 1
ls = reinforce.train_step(1)
log_str = "current time: " + str(
datetime.datetime.now().time()) + " episode: " + str(
i_episode) + " loss: " + str(ls) + " last_state: " + str(
state) + " last_reward: " + str(reward) + "\n"
log = open("lg3.txt", "a+")
log.write(log_str)
log.close()
print(log_str)