Comments (2)
I assume you are referring to the notebooks from the paper
A high bias low-variance introduction to Machine Learning for physicists.
The paysage
repo has been updated since the initial version of the paper was published on the arXiv. The notebooks at the above link have also been updated. If you download the python
versions of the notebooks at the link above, you should see that the import statements are of the form:
# for Boltzmann machines
from paysage import preprocess as pre
from paysage.layers import BernoulliLayer, GaussianLayer
from paysage.models import BoltzmannMachine
from paysage import batch
from paysage import fit
from paysage import optimizers
from paysage import samplers
from paysage import backends as be
from paysage import schedules
from paysage import penalties as pen
However, it appears that the html
versions of the notebooks have not been updated. I don't maintain that site, but will ask them to update the html versions so that they match the python versions asap.
from paysage.
OK. Thanks !
from paysage.
Related Issues (20)
- Use common statistic calculation routines HOT 1
- Make backends consistent for vector inputs
- Import problem with 'backends'
- Hopfield Network [No hidden layer] HOT 1
- Partial fit
- Use of GPU in paysage
- mnist.h5 no longer available
- Supervised and Semi-supervised RBMs HOT 1
- Advanced real valued RBMs
- Offset values in the energy function HOT 1
- Multiple Connections in Layers
- Use sampled states to compute derivatives
- Trace sources of non-determinism HOT 1
- Heat capacity training metric HOT 1
- Dropout RBMs HOT 1
- Add TAP method for GRBMs HOT 1
- More stable gradient estimates HOT 1
- Deprecate marginal_free_energy HOT 1
- Heat Capacity metric is unstable
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from paysage.