AttributeError: 'Tensor' 对象在 GPflow 中没有属性 'ndim'

如何解决AttributeError: 'Tensor' 对象在 GPflow 中没有属性 'ndim'

我运行以下代码以生成一个图表，其中按照 https://gpflow.readthedocs.io/en/stable/notebooks/basics/regression.html 绘制了均值函数、95% 置信区间和 10 个后验样本：

# GPflow
Y = np.asarray(p.metrics,dtype=float).reshape(-1,1)
X = np.asarray(p.x,1)

 

# scaling X and Y to magnify effects of variance
X *= .01
Y *= .01

 

plt.plot(X,Y,'kx',mew=2)

 

k = gpflow.kernels.Matern52(lengthscales=0.05)
# k = gpflow.kernels.polynomial(degree=4,variance=1)

 

m = gpflow.models.GPR((X,Y),k)
m.likelihood.variance.assign(0.01)

 

# code below is from the docs:
# https://gpflow.readthedocs.io/en/docupdate/notebooks/regression.html
def plot(m):
    xx = np.linspace(min(X),max(X),150)#[:,None]
    mean,var = m.predict_y(xx)
    plt.figure(figsize=(12,6))
    plt.plot(X,mew=2)
    plt.plot(xx,mean,'b',lw=2)
    plt.fill_between(xx[:,0],mean[:,0] - 2*np.sqrt(var[:,0]),0] + 2*np.sqrt(var[:,color='blue',alpha=0.2)

 

plot(m)
plt.show()

 


## generate test points for prediction
xx = np.linspace(min(X),100).reshape(-1,1)  # test points must be of shape (N,D)

 

## predict mean and variance of latent GP at test points
mean,var = m.predict_f(xx)

 

## generate 10 samples from posterior
tf.random.set_seed(1)  # for reproducibility
samples = m.predict_f_samples(xx,10)  # shape (10,100,1)

 


## plot
plt.figure(figsize=(12,6))
plt.plot(X,"kx",mew=2)
plt.plot(xx,"C0",lw=2)
plt.fill_between(
    xx[:,0] - 1.96 * np.sqrt(var[:,0] + 1.96 * np.sqrt(var[:,color="C0",alpha=0.2,)

 

plt.plot(xx,samples[:,:,0].numpy().T,linewidth=0.5)
plt.show()

运行后，出现以下错误：

AttributeError                            Traceback (most recent call last)
<ipython-input-28-e1bf5ef03fcf> in <module>
     22 plt.figure(figsize=(12,6))
     23 plt.plot(X,mew=2)
---> 24 plt.plot(xx,lw=2)
     25 plt.fill_between(
     26     xx[:,~/miniconda3/lib/python3.7/site-packages/matplotlib/pyplot.py in plot(scalex,scaley,data,*args,**kwargs)
   2787     return gca().plot(
   2788         *args,scalex=scalex,scaley=scaley,**({"data": data} if data
-> 2789         is not None else {}),**kwargs)
   2790 
   2791 

~/miniconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py in plot(self,scalex,**kwargs)
   1663         """
   1664         kwargs = cbook.normalize_kwargs(kwargs,mlines.Line2D._alias_map)
-> 1665         lines = [*self._get_lines(*args,data=data,**kwargs)]
   1666         for line in lines:
   1667             self.add_line(line)

~/miniconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py in __call__(self,**kwargs)
    223                 this += args[0],224                 args = args[1:]
--> 225             yield from self._plot_args(this,kwargs)
    226 
    227     def get_next_color(self):

~/miniconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py in _plot_args(self,tup,kwargs)
    385         if len(tup) == 2:
    386             x = _check_1d(tup[0])
--> 387             y = _check_1d(tup[-1])
    388         else:
    389             x,y = index_of(tup[-1])

~/miniconda3/lib/python3.7/site-packages/matplotlib/cbook/__init__.py in _check_1d(x)
   1400     else:
   1401         try:
-> 1402             ndim = x[:,None].ndim
   1403             # work around https://github.com/pandas-dev/pandas/issues/27775
   1404             # which mean the shape is not as expected. That this ever worked

AttributeError: 'Tensor' object has no attribute 'ndim'

X 和 Y 是两个给定的数组，例如：

Y = array([[14.13],[14.01],[13.59],[12.63],[11.44],[10.34],[ 9.3 ],[ 8.38],[ 7.49],[ 6.9 ],[ 6.72],[ 6.87],[ 7.07],[ 7.24],[ 8.36],[ 9.78],[10.64],[12.21],[12.88],[13.37],[13.57],[13.44],[13.2 ]])

X = array([[0.01],[0.02],[0.03],[0.04],[0.05],[0.06],[0.07],[0.08],[0.09],[0.1 ],[0.11],[0.12],[0.13],[0.14],[0.15],[0.16],[0.17],[0.18],[0.19],[0.2 ],[0.21],[0.22],[0.23]])