这段代码是训练dqn的·代码，请问我该如何保存训练好的模型，并且调用模型预测，训练和预测的输入参数都是J_num, M_num, O_num, J, Processing_time, D, A。怎么传入这些参数
import numpy as np
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "-1" #设置环境变量，以便在运行时仅使用CPU，而不是GPU
import random
from collections import deque
from tensorflow.keras import layers,models
import tensorflow as tf
from Job_Shop import Situation
from tensorflow.keras.optimizers import Adam
from Instance_Generator import Processing_time,A,D,M_num,Op_num,J,O_num,J_num
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif']=['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus']=False # 用来正常显示负号
'''
回放Memory处应该有点问题。需要改一下。
'''
class DQN:
def __init__(self,):
self.Hid_Size = 30
# ------------Hidden layer=5 30 nodes each layer--------------
model = models.Sequential()
model.add(layers.Input(shape=(7,)))
model.add(layers.Dense(self.Hid_Size, name='l1'))
model.add(layers.Dense(self.Hid_Size, name='l2'))
model.add(layers.Dense(self.Hid_Size, name='l3'))
model.add(layers.Dense(self.Hid_Size, name='l4'))
model.add(layers.Dense(self.Hid_Size, name='l5'))
model.add(layers.Dense(6, name='l6'))
model.compile(loss='mse',
optimizer=Adam(learning_rate=0.001))
# # model.summary()
self.model = model
#------------Q-network Parameters-------------
self.act_dim=[1,2,3,4,5,6] #神经网络的输出节点
self.obs_n=[0,0,0,0,0,0,0] #神经网路的输入节点
self.gama = 0.95 # γ经验折损率
# self.lr = 0.001 # 学习率
self.global_step = 0
self.update_target_steps = 200 # 更新目标函数的步长
self.target_model = self.model
#-------------------Agent-------------------
self.e_greedy=0.6
self.e_greedy_decrement=0.0001
self.L=2 #Number of training episodes L 训练集数!!!!40
#---------------Replay Buffer---------------
self.buffer=deque(maxlen=2000)
self.Batch_size=10 # Batch Size of Samples to perform gradient descent
def replace_target(self):
self.target_model.get_layer(name='l1').set_weights(self.model.get_layer(name='l1').get_weights())
self.target_model.get_layer(name='l2').set_weights(self.model.get_layer(name='l2').get_weights())
self.target_model.get_layer(name='l3').set_weights(self.model.get_layer(name='l3').get_weights())
self.target_model.get_layer(name='l4').set_weights(self.model.get_layer(name='l4').get_weights())
self.target_model.get_layer(name='l5').set_weights(self.model.get_layer(name='l5').get_weights())
self.target_model.get_layer(name='l6').set_weights(self.model.get_layer(name='l6').get_weights())
def replay(self):
if self.global_step % self.update_target_steps == 0:
self.replace_target()
# replay the history and train the modelUC1.#回放历史并训练模型
minibatch = random.sample(self.buffer, self.Batch_size)
for state, action, reward, next_state, done in minibatch:
target = reward
if not done:
k=self.target_model.predict(next_state)
target = (reward + self.gama *
np.argmax(self.target_model.predict(next_state)))
target_f = self.model.predict(state)
target_f[0][action] = target
self.model.fit(state, target_f, epochs=1, verbose=0)
self.global_step += 1
def Select_action(self,obs):
# obs=np.expand_dims(obs,0)
if random.random()<self.e_greedy:
act=random.randint(0,5)
else:
act=np.argmax(self.model.predict(obs))
self.e_greedy = max(
0.01, self.e_greedy - self.e_greedy_decrement) # 随着训练逐步收敛，探索的程度慢慢降低
return act
def _append(self, exp):
self.buffer.append(exp)
def main(self,J_num, M_num, O_num, J, Processing_time, D, A):
k = 0
x=[]
Total_tard=[]
TR=[]
for i in range(self.L):
Total_reward = 0
x.append(i+1)
print('-----------------------开始第',i+1,'次训练------------------------------')
obs=[0 for i in range(7)]
obs = np.expand_dims(obs, 0)
done=False
Sit = Situation(J_num, M_num, O_num, J, Processing_time, D, A)
for i in range(O_num):
k+=1
# print(obs)
at=self.Select_action(obs)
# print(at)
if at==0:
at_trans=Sit.rule1()
if at==1:
at_trans=Sit.rule2()
if at==2:
at_trans=Sit.rule3()
if at==3:
at_trans=Sit.rule4()
if at==4:
at_trans=Sit.rule5()
if at==5:
at_trans=Sit.rule6()
# at_trans=self.act[at]
print('这是第',i,'道工序>>','执行action:',at,' ','将工件',at_trans[0],'安排到机器',at_trans[1])
Sit.scheduling(at_trans)
obs_t=Sit.Features()
if i==O_num-1:
done=True
#obs = obs_t
obs_t = np.expand_dims(obs_t, 0)
# obs = np.expand_dims(obs, 0)
# print(obs,obs_t)
r_t = Sit.reward(obs[0][6],obs[0][5],obs_t[0][6],obs_t[0][5],obs[0][0],obs_t[0][0])
self._append((obs,at,r_t,obs_t,done))
if k>self.Batch_size:
# batch_obs, batch_action, batch_reward, batch_next_obs,done= self.sample()
self.replay()
Total_reward+=r_t
obs=obs_t
total_tadiness=0
Job=Sit.Jobs
E=0
K=[i for i in range(len(Job))]
End=[]
for Ji in range(len(Job)):
End.append(max(Job[Ji].End))
if max(Job[Ji].End)>D[Ji]:
total_tadiness+=abs(max(Job[Ji].End)-D[Ji])
print('<<<<<<<<<-----------------total_tardiness总延迟:',total_tadiness,'------------------->>>>>>>>>>')
Total_tard.append(total_tadiness)
print('<<<<<<<<<-----------------reward奖励:',Total_reward,'------------------->>>>>>>>>>')
TR.append(Total_reward)
plt.plot(K,End,color='y') #绘制每个工件的结束时间，黄色
plt.plot(K,D,color='r') #绘制每个工件的交货期，红色
#plt.show() #显示绘制的图形
plt.savefig("第%d次训练.png" % (i+1))
plt.clf() #清楚当前绘画，以防下次迭代叠加到后续图形上
plt.plot(x,Total_tard)
plt.xlabel(u'训练步数', fontsize=14, color='r')# x,y坐标轴名称设置,可以同时设置标签的字体大小颜色等
plt.ylabel(u'总延迟时间', fontsize=14, color='b')
plt.title(u"DQN在每个训练步骤中获得的总延迟时间", fontsize=14, color='k') # 设置图形标题
plt.savefig('总延迟.png')
plt.show()
return Total_reward
d=DQN()
d.main(J_num, M_num, O_num, J, Processing_time, D, A)
