我怎样才能传递额外的输入参数odeint函数TensorFlow

Question

我有一个系统的常微分方程与外部确定性输入（控件）和随机分量。除了初始状态，我怎样才能安全地（执行好的代码样式）通过tf.contrib.integrate.odeint()将这些额外的输入参数传递给方程？如果有这样的做法。或者在外部范围内定义它们并从等式函数中引用它们是迄今为止唯一的方法？

Answer 1

我尝试使用odeint求解器模拟Hindmarsh-Rose模型时遇到同样的问题。我想在等式中注入一个电流，但不知道如何去做。 这里一个基本的例子后：

import numpy as np 
import tensorflow as tf 
import matplotlib.pyplot as plt 
from mpl_toolkits.mplot3d import Axes3D 

## Model parameters 
# v' = u - a.v^3 + b.v^2 + I -z 
# u' = c - d.v^2 -u 
# z' = epsilon_z.(s.(v-v0)-z 

#parameters for v and u terms 
a = 1.0 
b = 3.0 
c = -3.0 
d = 5.0 
v0 = -1.4 
s = 4.0 
epsilon_z = 0.002 

# init tensions 
v_init = -3.0 
u_init = 0.0 
z_init = +0.9 

#injected current that is currently fixed 
Id = 5 

# What I would like to do : 
# def I(t): 
# if 0.0 <= t < 300.0: 
#  return 0.0 
# elif 300.0 <= t < 1700.0: 
#  return Iech 
# return 0.0 

def HR_equation(state, t): 
    v, u, z = tf.unstack(state)  
    dv = -a * v*v*v + b * v*v + u - z + I 
    du = -d * v*v - u + c 
    dz = epsilon_z * (s*(v - v0)- z 
    return tf.stack([dv, du, dz]) 

init_state = tf.constant([v_init, u_init, z_init], dtype=tf.float64) 

t = np.linspace(0, 2000, num=5000) 
tensor_state, tensor_info = tf.contrib.integrate.odeint(HR_equation,   
init_state, t, full_output=True) 

sess = tf.Session() 
state, info = sess.run([tensor_state, tensor_info]) 
v, u, z = state.T 
plt.plot(v, u)