# Cannot convert \<class 'numpy.ndarray'\> to TensorType'

**URL:** <https://discourse.pymc.io/t/cannot-convert-class-numpy-ndarray-to-tensortype/4432>\
**Category:** Questions\
**Created:** [January 30, 2020, 4:04pm UTC](https://discourse.pymc.io/t/cannot-convert-class-numpy-ndarray-to-tensortype/4432 "2020-01-30T16:04:42Z")\
**Posts on this page:** 6\
**Page:** 1

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**Author:** ![Dr\_Z](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/dr_z/32/2445_2.png) [@Dr\_Z](https://discourse.pymc.io/u/Dr_Z)\
**Post date:** [January 30, 2020, 4:04pm UTC](https://discourse.pymc.io/t/cannot-convert-class-numpy-ndarray-to-tensortype/4432/1 "2020-01-30T16:04:42Z")

</div>

Sorry if this is a simple question, but I don’t seem to be able to figure it out.  
I provide a minimal working example of a code, which creates a multivariate normal distribution with a covariance that depends on random variables.

> import pymc3 as pm  
> import numpy as np
> 
> with pm.Model() as model:  
> n1 = pm.Gamma(‘n1’, alpha=2, beta=1 / 1.3, transform=None)  
> n2 = pm.Gamma(‘n2’, alpha=2, beta=1 / 1.3, transform=None)  
> D2 = pm.Gamma(‘D2’, alpha=2, beta=1 / 1.3, transform=None)
> 
> ```
> mu = [0,0]
> cov = np.array([[n1, n2], [n2, D2]])
> pm.MvNormal('test', mu=mu, cov=cov, observed = [1, 10])
> 
> ```

However, the last line fails with the following error.

> WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.  
> Traceback (most recent call last):  
> File “C:\Python37\lib\site-packages\theano\tensor\type.py”, line 269, in dtype\_specs  
> }[self.dtype]  
> KeyError: ‘object’
> 
> During handling of the above exception, another exception occurred:
> 
> Traceback (most recent call last):  
> File “C:\Python37\lib\site-packages\theano\tensor\basic.py”, line 246, in constant  
> ttype = TensorType(dtype=x\_.dtype, broadcastable=bcastable)  
> File “C:\Python37\lib\site-packages\theano\tensor\type.py”, line 51, in **init**  
> self.dtype\_specs() # error checking is done there  
> File “C:\Python37\lib\site-packages\theano\tensor\type.py”, line 272, in dtype\_specs  
> % (self. **class**. **name** , self.dtype))  
> TypeError: Unsupported dtype for TensorType: object
> 
> During handling of the above exception, another exception occurred:
> 
> Traceback (most recent call last):  
> File “C:\Python37\lib\site-packages\theano\tensor\basic.py”, line 194, in as\_tensor\_variable  
> return constant(x, name=name, ndim=ndim)  
> File “C:\Python37\lib\site-packages\theano\tensor\basic.py”, line 266, in constant  
> raise TypeError(“Could not convert %s to TensorType” % x, type(x))  
> TypeError: (‘Could not convert [[n1 n2]\n [D1 D2]] to TensorType’, \<class ‘numpy.ndarray’\>)
> 
> During handling of the above exception, another exception occurred:
> 
> Traceback (most recent call last):  
> File “C:\Program Files (x86)\JetBrains\PyCharm Community Edition 2019.1.2\helpers\pydev\pydevd.py”, line 1758, in   
> main()  
> File “C:\Program Files (x86)\JetBrains\PyCharm Community Edition 2019.1.2\helpers\pydev\pydevd.py”, line 1752, in main  
> globals = debugger.run(setup[‘file’], None, None, is\_module)  
> File “C:\Program Files (x86)\JetBrains\PyCharm Community Edition 2019.1.2\helpers\pydev\pydevd.py”, line 1147, in run  
> pydev\_imports.execfile(file, globals, locals) # execute the script  
> File “C:\Program Files (x86)\JetBrains\PyCharm Community Edition 2019.1.2\helpers\pydev\_pydev\_imps\_pydev\_execfile.py”, line 18, in execfile  
> exec(compile(contents+“\n”, file, ‘exec’), glob, loc)  
> File “D:/temp.py”, line 43, in   
> pm.MvNormal(‘test’, mu=mu, cov=cov, observed = [1, 10])  
> File “C:\Python37\lib\site-packages\pymc3\distributions\distribution.py”, line 46, in **new**  
> dist = cls.dist(\*args, \*\*kwargs)  
> File “C:\Python37\lib\site-packages\pymc3\distributions\distribution.py”, line 57, in dist  
> dist. **init** (\*args, \*\*kwargs)  
> File “C:\Python37\lib\site-packages\pymc3\distributions\multivariate.py”, line 222, in **init**  
> super(). **init** (mu=mu, cov=cov, tau=tau, chol=chol, lower=lower, \*args, \*\*kwargs)  
> File “C:\Python37\lib\site-packages\pymc3\distributions\multivariate.py”, line 59, in **init**  
> cov = tt.as\_tensor\_variable(cov)  
> File “C:\Python37\lib\site-packages\theano\tensor\basic.py”, line 200, in as\_tensor\_variable  
> raise AsTensorError(“Cannot convert %s to TensorType” % str\_x, type(x))  
> theano.tensor.var.AsTensorError: (‘Cannot convert [[n1 n2]\n [D1 D2]] to TensorType’, \<class ‘numpy.ndarray’\>)

I was following an example given here  
[https://docs.pymc.io/api/distributions/multivariate.html#pymc3.distributions.multivariate.MvNormal](https://docs.pymc.io/api/distributions/multivariate.html#pymc3.distributions.multivariate.MvNormal)  
but I must be doing something wrong. Can someone advise?

It seems like theano is unable to convert ` cov` from a numpy array to a tensor, but in it seems to be the case in the example above as well.

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**Author:** ![AlexAndorra](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/alexandorra/32/9142_2.png) [@AlexAndorra](https://discourse.pymc.io/u/AlexAndorra)\
**Post date:** [January 30, 2020, 4:25pm UTC](https://discourse.pymc.io/t/cannot-convert-class-numpy-ndarray-to-tensortype/4432/2 "2020-01-30T16:25:02Z")

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Hi!  
I think the problem comes from the fact that you’re mixing numpy arrays and theano tensors in your covariance.

In the example you’re refering to, notice that, when the covariance matrix is known, it is a genuine numpy array, with floats in it, not theano tensors: `np.array([[1., 0.5], [0.5, 2]])`.

If you’re trying to estimate your covariance matrix through MCMC, then you need to define your cov as a tensor and not as an array, as explained later in the example. Usually, for computational reasons, you’ll use the Cholesky decomposition: `chol_packed = pm.LKJCholeskyCov('chol_packed', n=3, eta=2, sd_dist=sd_dist)`.

Hope this helps 🖖

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**Author:** ![Dr\_Z](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/dr_z/32/2445_2.png) [@Dr\_Z](https://discourse.pymc.io/u/Dr_Z)\
**Post date:** [January 30, 2020, 5:31pm UTC](https://discourse.pymc.io/t/cannot-convert-class-numpy-ndarray-to-tensortype/4432/3 "2020-01-30T17:31:12Z")

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Hi!

Thanks for the suggestions!

Could you point to the exact spot in the example, where _cov [is defined] as a tensor and not as an array, as explained later in the example_? Let us assume I would like to use the covariance and not the Cholesky decomposition for the moment.

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<div class="post-metadata">

**Author:** ![Dr\_Z](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/dr_z/32/2445_2.png) [@Dr\_Z](https://discourse.pymc.io/u/Dr_Z)\
**Post date:** [January 31, 2020, 9:10am UTC](https://discourse.pymc.io/t/cannot-convert-class-numpy-ndarray-to-tensortype/4432/4 "2020-01-31T09:10:57Z")

</div>

I guess you suggest I replace

> cov = np.array([[n1, n2], [n2, D2]])

with

> cov = tt.stacklists([[n1, n2], [n2, D2]])

This seems to solve the problem, thank you. I just get a warning now when I try to estimate MAP which I don’t understand:

> C:\Python37\lib\site-packages\theano\tensor\basic.py:6611: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.  
> result[diagonal\_slice] = x  
> C:\Python37\lib\site-packages\theano\tensor\basic.py:6611: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.  
> result[diagonal\_slice] = x  
> C:\Python37\lib\site-packages\theano\tensor\basic.py:6611: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.  
> result[diagonal\_slice] = x  
> C:\Python37\lib\site-packages\theano\tensor\basic.py:6611: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.  
> result[diagonal\_slice] = x

Do you know what it referst to?

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**Author:** ![AlexAndorra](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/alexandorra/32/9142_2.png) [@AlexAndorra](https://discourse.pymc.io/u/AlexAndorra)\
**Post date:** [January 31, 2020, 10:26am UTC](https://discourse.pymc.io/t/cannot-convert-class-numpy-ndarray-to-tensortype/4432/5 "2020-01-31T10:26:16Z")

</div>

I did point to the spot of the example: `chol_packed = pm.LKJCholeskyCov('chol_packed', n=3, eta=2, sd_dist=sd_dist)`.

What you’re doing now is working because you’re using theano operators to define your cov matrix. In other words, when your cov matrix is unknown and must be estimated through MCMC, you need to put a prior distribution over it. Usually it’s done in terms of a distribution of correlations matrices (`LKJCorr`) or a distribution of Cholesky factors (`LKJCholeskyCov`, which works best computationally).

I’m not sure how your parametrization will fit, as it’s quite unusual, but if it makes sense scientifically and in your use-case, you’re right to try!

The warning is theano related and you can [safely ignore it](https://github.com/Theano/Theano/issues/6667) for now. Also, note that [using MAP is discouraged](https://discourse.pymc.io/t/frequently-asked-questions/74) by the PyMC team – unless you know what you’re doing.

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**Author:** ![Dr\_Z](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/dr_z/32/2445_2.png) [@Dr\_Z](https://discourse.pymc.io/u/Dr_Z)\
**Post date:** [February 13, 2020, 2:23pm UTC](https://discourse.pymc.io/t/cannot-convert-class-numpy-ndarray-to-tensortype/4432/6 "2020-02-13T14:23:14Z")

</div>

Hi Alex,

Thanks for your reply.

You were right: the problem was due to that I had to supply theano tensors and not numpy arrays of theano variables. It seems that a numpy array of theano variables was not automatically converted into a theano tensor, which caused the error I was seeing. After the correction, it works fine, except that I have discovered that it is extremely slow, so I had to find a way to make my calculations outside pymc and theano anyway. They are much faster without theano differentiation.

To answer your other remarks.

Thanks for pointing me to the Cholesky distribution for the covariance matrix coefficient. I guess, however, it only makes sense if they are Cholesky distributed… In my case, I don’t think it is a valid assumption, but I have reasonable assumptions on each coefficient, which explains the expressions I used in the example.

As for the MAP, thank you, I’ve indeed seen the warning, but my plan is to find the marginal likelihood using the Laplace approximation, and I definitely need to know mode of the distribution of that.

Thanks again for your time!