Nn sequential use how to#
Nn sequential use full#

Then, I can just reference model.parameter_names to get those names later. Setattr(m, name, PyroSample(prior=dist.Laplace(0., 3.) Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately.

Nn sequential use how to#

Pyro.nn.module.to_pyro_module_(self.shape) torch.nn.BatchNorm2d View all torch analysis How to use the torch.nn.BatchNorm2d function in torch To help you get started, we’ve selected a few torch examples, based on popular ways it is used in public projects. ('shape_fc1L:final', nn.Linear(in_features=h1,out_features=out_features))įor name, param in _parameters(): ('shape_fc0', nn.Linear(in_features=in_features,out_features=h1)), Self.mu_func_call = self.mu_func(in_features, h1 = h1, out_features = out_features) Using idea from here from collections import OrderedDict I guess you can do theta = nn.Sequential( Your code def _init_(self, in_features, h1 = 2, out_features = 1): You can assign the submodules as regular attributes:: import torch.nn as nn. The constructor of torch.nn.Sequential accepts an OrderedDict object and that of torch.nn.ModuleList accepts a list object. I would recommend playing a bit with some PyTorch modules like Sequential to see how naming works in PyTorch. For such :class:Module, you should use :func:torch. class Net (nn.Module): def init (self, hiddendim, in. I find that when I don’t use the sequential module, my test accuracy is always worse than if I do use it. I am toggling between using nn.Sequential and not using it with the variable usesequential.

Nn sequential use full#

If your nn.Module has two submodules mu and theta, then the name of parameters will be 'mu.linear.weight'. Here is a code snippet I’ve attached the full code at the end for your reference. It is just the same as the way you use m.named_parameters(recurse=False) in your code. With a = A(), I think you can do a.named_parameters() to get names of parameters of theta. In general though, is there a way to get all possible options to use in return_sites? I looked at poutine but could not get that to work.ĭef _init_(self, in_features, h1 = 2, out_features = 1): But I can’t figure out how theta gets named and how to access that distribution. With mu for example, if I use self.linear = PyroModule(.), I can use Predictive(model, guide, num_samples, return_sites = ('linear.weight')). How do the sites get named for theta? I’d like to look at the distributions of those parameters using Predictive. Obs = pyro.sample('obs', GammaHurdle(concentration = shape, rate = shape / mu, theta = theta), obs=y) Setattr(m, name, PyroSample(prior=dist.Laplace(0, 2) Pyro.nn.module.to_pyro_module_(self.theta)įor name, value in list(m.named_parameters(recurse=False)): Relevant code snippet (some lines removed to make it more clear): def _init_(self, in_features, h1 = 2, out_features = 1):

I’ve now updated theta to be modeled as a two layer nn.Sequential. I’m continuing on the model I’ve described here, adding complexity bit by bit.