![]() Hence, I first updated Anaconda Navigator to the latest version. A problem that can crop up is the error message: “ updating spyder is constricted by …” Anaconda stuck updating Spyder įor my case, it turns out that the version of Anaconda Navigator is outdated. Updating Spyder is constricted by …Īnother way to update Spyder is to type “conda update spyder” in the terminal. The process shows that it is “loading packages of /User/…/opt/anaconda3”. But the problem is that the process can take a very long time. One way to update Spyder is to open Anaconda Navigator and click the settings button which has an option to update Spyder. There are some problems that are commonly faced when it comes to updating Spyder. Model_ft = is a Python IDE that is bundled together with the Anaconda distribution. However loading model from torch hub works just fine: ![]() ~/anaconda/envs/cs231n/lib/python3.7/site-packages/scipy/stats/_continuous_distns.py in _norm_cdf(x) ~/anaconda/envs/cs231n/lib/python3.7/site-packages/scipy/stats/_continuous_distns.py in _norm_sf(x) ~/anaconda/envs/cs231n/lib/python3.7/site-packages/scipy/stats/_continuous_distns.py in _truncnorm_get_delta(a, b) > 7081 delta = _truncnorm_get_delta(a, b) ~/anaconda/envs/cs231n/lib/python3.7/site-packages/scipy/stats/_continuous_distns.py in _truncnorm_ppf(q, a, b) ~/anaconda/envs/cs231n/lib/python3.7/site-packages/numpy/lib/function_base.py in _vectorize_call(self, func, args) > 2091 return self._vectorize_call(func=func, args=vargs)Ģ093 def _get_ufunc_and_otypes(self, func, args): ~/anaconda/envs/cs231n/lib/python3.7/site-packages/numpy/lib/function_base.py in call(self, *args, **kwargs)Ģ089 vargs.extend( for _n in names]) ~/anaconda/envs/cs231n/lib/python3.7/site-packages/scipy/stats/_continuous_distns.py in vf_wrapper(*args) ~/anaconda/envs/cs231n/lib/python3.7/site-packages/scipy/stats/_continuous_distns.py in _ppf(self, q, a, b) ~/anaconda/envs/cs231n/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py in _rvs(self, *args)ĩ11 # Use basic inverse cdf algorithm for RV generation as default.ĩ12 U = self._random_state.random_sample(self._size) ![]() ~/anaconda/envs/cs231n/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py in rvs(self, *args, **kwds) ~/anaconda/envs/cs231n/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py in rvs(self, size, random_state)Ĥ62 kwds.update() –> 111 values = torch.as_tensor(X.rvs(m.weight.numel()), dtype=m.weight.dtype)ġ12 values = values.view(m.weight.size()) ~/anaconda/envs/cs231n/lib/python3.7/site-packages/torchvision/models/inception.py in init(self, num_classes, aux_logits, transform_input, inception_blocks)ġ09 stddev = m.stddev if hasattr(m, ‘stddev’) else 0.1ġ10 X = uncnorm(-2, 2, scale=stddev) ~/anaconda/envs/cs231n/lib/python3.7/site-packages/torchvision/models/inception.py in inception_v3(pretrained, progress, **kwargs)ĥ4 state_dict = load_state_dict_from_url(model_urls, > 1 model_ft = models.inception_v3(pretrained=use_pretrained) KeyboardInterrupt Traceback (most recent call last) When I interrupt the kernel, this is the output: The model parameters are downloaded before and it worked fine and I have restarted my computer and it is still not working. Model_ft = models.inception_v3(pretrained=use_pretrained) I am running simple loading of pre-trained model from my jupyter notebook and it takes forever to do it.
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