PyMC3 Bayesian Inference with NUTS initialization - python-2.7

Question

I'm trying to implement a simple Bayesian Inference using a ODE model. I want to use the NUTS algorithm to sample but it gives me an initialization error. I do not know much about the PyMC3 as I'm new to this. Please take a look and tell me what is wrong.
import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import odeint
import seaborn
import pymc3 as pm
import theano.tensor as T
from theano.compile.ops import as_op
#Actual Solution of the Differential Equation(Used to generate data)
def actual(a,b,x):
Y = np.exp(-b*x)*(a*np.exp(b*x)*(b*x-1)+a+b**2)/b**2
return Y
#Method For Solving the ODE
def lv(xdata, a=5.0, b=0.2):
def dy_dx(y, x):
return a*x - b*y
y0 = 1.0
Y, dict = odeint(dy_dx,y0,xdata,full_output=True)
return Y
#Generating Data for Bayesian Inference
a0, b0 = 5, 0.2
xdata = np.linspace(0, 21, 100)
ydata = actual(a0,b0,xdata)
# Adding some error to the ydata points
yerror = 10*np.random.rand(len(xdata))
ydata += np.random.normal(0.0, np.sqrt(yerror))
ydata = np.ravel(ydata)
#as_op(itypes=[T.dscalar, T.dscalar], otypes=[T.dvector])
def func(al,be):
Q = lv(xdata, a=al, b=be)
return np.ravel(Q)
# Number of Samples and Initial Conditions
nsample = 5000
y0 = 1.0
# Model for Bayesian Inference
model = pm.Model()
with model:
# Priors for unknown model parameters
alpha = pm.Uniform('alpha', lower=a0/2, upper=a0+a0/2)
beta = pm.Uniform('beta', lower=b0/2, upper=b0+b0/2)
# Expected value of outcome
mu = func(alpha,beta)
# Likelihood (sampling distribution) of observations
Y_obs = pm.Normal('Y_obs', mu=mu, sd=yerror, observed=ydata)
trace = pm.sample(nsample, nchains=1)
pm.traceplot(trace)
plt.show()
The error that I get is
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Initializing NUTS failed. Falling back to elementwise auto-assignment.
Any help would be really appreciated