# GaussianRandomWalk from the example notebook errors out with ValueError: Input dimension mis-match
**URL:** https://discourse.pymc.io/t/gaussianrandomwalk-from-the-example-notebook-errors-out-with-valueerror-input-dimension-mis-match/6327
**Category:** Questions
**Created:** [November 30, 2020, 5:56am UTC](https://discourse.pymc.io/t/gaussianrandomwalk-from-the-example-notebook-errors-out-with-valueerror-input-dimension-mis-match/6327 "2020-11-30T05:56:15Z")
**Posts on this page:** 3
**Page:** 1
### Author:  [@gunrr](https://discourse.pymc.io/u/gunrr)
#### Post date: [November 30, 2020, 5:56am UTC](https://discourse.pymc.io/t/gaussianrandomwalk-from-the-example-notebook-errors-out-with-valueerror-input-dimension-mis-match/6327/1 "2020-11-30T05:56:15Z")
I am experimenting with the code from [https://github.com/twiecki/WhileMyMCMCGentlySamples/blob/master/content/downloads/notebooks/random\_walk\_deep\_net.ipynb](https://github.com/twiecki/WhileMyMCMCGentlySamples/blob/master/content/downloads/notebooks/random_walk_deep_net.ipynb) but the GaussianRandomWalk line errors out with ‘ValueError: Input dimension mis-match. (input[0].shape[1] = 1, input[1].shape[1] = 2)’. Can someone let me know what I’m missing or if there’s any updated notebooks. I installed pymc with conda forge command and there are no import issues.
## Detailed log :
ValueError Traceback (most recent call last)
in
8
9 # This is the central trick, PyMC3 already comes with this distribution
—\> 10 w = pm.GaussianRandomWalk(‘w’, sd=step\_size,
11 shape=(interval, 2))
12
D:\Softwares\Anaconda3\lib\site-packages\pymc3\distributions\distribution.py in **new** (cls, name, \*args, \*\*kwargs)
45 total\_size = kwargs.pop(‘total\_size’, None)
46 dist = cls.dist(\*args, \*\*kwargs)
—\> 47 return model.Var(name, dist, data, total\_size)
48 else:
49 raise TypeError(“Name needs to be a string but got: {}”.format(name))
D:\Softwares\Anaconda3\lib\site-packages\pymc3\model.py in Var(self, name, dist, data, total\_size)
919 if getattr(dist, “transform”, None) is None:
920 with self:
–\> 921 var = FreeRV(name=name, distribution=dist,
922 total\_size=total\_size, model=self)
923 self.free\_RVs.append(var)
D:\Softwares\Anaconda3\lib\site-packages\pymc3\model.py in **init** (self, type, owner, index, name, distribution, total\_size, model)
1368 self.tag.test\_value = np.ones(
1369 distribution.shape, distribution.dtype) \* distribution.default()
-\> 1370 self.logp\_elemwiset = distribution.logp(self)
1371 # The logp might need scaling in minibatches.
1372 # This is done in `Factor`.
D:\Softwares\Anaconda3\lib\site-packages\pymc3\distributions\timeseries.py in logp(self, x)
230 x\_i = x[1:]
231 mu, sigma = self.\_mu\_and\_sigma(self.mu, self.sigma)
–\> 232 innov\_like = Normal.dist(mu=x\_im1 + mu, sigma=sigma).logp(x\_i)
233 return self.init.logp(x[0]) + tt.sum(innov\_like)
234 return self.init.logp(x)
D:\Softwares\Anaconda3\lib\site-packages\pymc3\distributions\continuous.py in logp(self, value)
516 mu = self.mu
517
–\> 518 return bound((-tau \* (value - mu)\*\*2 + tt.log(tau / np.pi / 2.)) / 2.,
519 sigma \> 0)
520
D:\Softwares\Anaconda3\lib\site-packages\theano\tensor\var.py in **mul** (self, other)
153 # and the return value in that case
154 try:
–\> 155 return theano.tensor.mul(self, other)
156 except (NotImplementedError, AsTensorError):
157 return NotImplemented
D:\Softwares\Anaconda3\lib\site-packages\theano\gof\op.py in **call** (self, \*inputs, \*\*kwargs)
672 thunk.outputs = [storage\_map[v] for v in node.outputs]
673
–\> 674 required = thunk()
675 assert not required # We provided all inputs
676
D:\Softwares\Anaconda3\lib\site-packages\theano\gof\op.py in rval()
860
861 def rval():
–\> 862 thunk()
863 for o in node.outputs:
864 compute\_map[o][0] = True
D:\Softwares\Anaconda3\lib\site-packages\theano\gof\cc.py in **call** (self)
1737 print(self.error\_storage, file=sys.stderr)
1738 raise
-\> 1739 reraise(exc\_type, exc\_value, exc\_trace)
1740
1741
D:\Softwares\Anaconda3\lib\site-packages\six.py in reraise(tp, value, tb)
701 if value. **traceback** is not tb:
702 raise value.with\_traceback(tb)
–\> 703 raise value
704 finally:
705 value = None
ValueError: Input dimension mis-match. (input[0].shape[1] = 1, input[1].shape[1] = 2)
Any help is appreciated. Thanks.
---
### Author:  [@luisroque](https://discourse.pymc.io/u/luisroque)
#### Post date: [November 30, 2020, 3:48pm UTC](https://discourse.pymc.io/t/gaussianrandomwalk-from-the-example-notebook-errors-out-with-valueerror-input-dimension-mis-match/6327/2 "2020-11-30T15:48:48Z")
Hmmm, the problem seems to be in the step\_size shape. I would just make it into a matrix with (1, n\_dim) but it is not broadcasting I don’t know why.
Nevertheless, I just reshaped a bit to ensure that the shapes matched and it works.
```auto
step_size = pm.HalfNormal('step_size',
sd=np.ones(n_dim),
shape=(1,n_dim))
step_size_ = tt.tile(step_size, (interval,1))
# This is the central trick, PyMC3 already comes with this distribution
w = pm.GaussianRandomWalk('w', sd=step_size_,
shape=(interval, 2))
```
Hope it helps!
---
### Author:  [@gunrr](https://discourse.pymc.io/u/gunrr)
#### Post date: [December 8, 2020, 10:30pm UTC](https://discourse.pymc.io/t/gaussianrandomwalk-from-the-example-notebook-errors-out-with-valueerror-input-dimension-mis-match/6327/3 "2020-12-08T22:30:42Z")
Apologies for the late response. This one works. Thank you very much!