PYTORCH NN SEQUENTIAL HOW TO
PYTORCH NN SEQUENTIAL INSTALL
PYTORCH NN SEQUENTIAL CODE

PYTORCH NN SEQUENTIAL CODE

We will go through the code implementation. To provide this facility and to avoid retraining the model every time, we have the functionalities available in Pytorch to save and load model. Once you train your model and get the desired results, you want to save the model and using this model you would want to make predictions. We will see a code implementation of this process. Choice is yours how you wanna build the NN architecture – Here the modules will be added to it in the order they are passed in the constructor. Torch.nn has another handy class we can use to simply our code: Sequential.If you are familiar with Keras, this implementation you will find very similar. Follow this tutorial to do the set up in Colab.

PYTORCH NN SEQUENTIAL INSTALL

In case you a GPU, you need to install the GPU version of Pytorch, get the installation command from this link.ĭon’t feel bad if you don’t have a GPU, Google Colab is the life saver in that case. Glad that it works for your case.If you have gone through the last three posts of this series, now you should be able able to define the architecture of Deep Neural Network, define and optimize loss, you should also be now aware of few of over-fitting reduction techniques like – compare validation/test set performance with training set, addition of dropout in the architecture.Īlong with the ease of implementation in Pytorch, you also have exclusive GPU (even multiple GPUs) support in Pytorch. The names are conveniently returned as expected when using Predictive, e.g. Then, I can just reference model.parameter_names to get those names later. Setattr(m, name, PyroSample(prior=dist.Laplace(0., 3.) Pyro.nn.module.to_pyro_module_(self.shape) ('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):

I would recommend playing a bit with some PyTorch modules like Sequential to see how naming works in PyTorch.

If your nn.Module has two submodules mu and theta, then the name of parameters will be 'mu.linear.weight'. 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):

PYTORCH NN SEQUENTIAL HOW TO

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.

Pytorch nn sequential