 # Bayesian regression nnet

Hi, I’m using a model that only has a nnet that predicts the mean of a distribution (and so I don’t have any `pyro.sample()` statements in my model), and an autodiagonalnormal guide.

When I run the SVI I get the following error: `RuntimeError: AutoDiagonalNormal found no latent variables; Use an empty guide instead`.

Shouldn’t it be picking up my priors on the nnet weights?

And why are these priors correctly mapped by the autoguide in the Bayesian regression tutorial Part I (cause there they also use a nnet and autoguide. Does the fact that they are explicitly sampling a stdev make the autoguide to pick up the nnet weight priors?) ?

Thanks!

``````class OneLayerRegressionModel(nn.Module):
def __init__(self, n_in):
# n_in = number of features
super(OneLayerRegressionModel, self).__init__()
n_out = 1
self.linear_1 = nn.Linear(n_in, n_out)

def forward(self, x):
# x * w + b

def model(dataset_total_length, x_data, y_data):
# priors
w_prior = Normal(torch.zeros(1, n_features), torch.ones(1, n_features)).to_event(1)
b_prior = Normal(torch.tensor([[0.]]), torch.tensor([[1.]])).to_event(1)
priors = {'linear.weight': w_prior, 'linear.bias': b_prior}
lifted_module = pyro.random_module('module', regression_model, priors)
lifted_module_sample = lifted_module()
with pyro.plate('map', dataset_total_length, subsample=x_data):
prediction_mean = lifted_module_sample(x_data).squeeze(-1)
pyro.sample('observations', Exponential(prediction_mean), obs=y_data)
return prediction_mean

regression_model = OneLayerRegressionModel(n_features)

guide = AutoDiagonalNormal(model)
``````

The call to `pyro.random_module` lifts the parameters in nn.Module to random variables.

This forum post will be helpful.

2 Likes

please include a more complete snippet of your code. what are `nn_model` and `regression_model`?

@jpchen Ive just edited my code to make it more clear. I changed the name of `nn_model` to `OneLayerRegressionModel` (its basically the instantiation of my nnet class).

your keys have to match the names of the attributes in the nn module. note how in the tutorial the keys in the dict match the names of the linear layers in the nn module. if you set `pyro.enable_validation(True)` there should be a warning thrown that tells you this. and yes, please in the future include the full relevant code with stack trace, it makes the diagnosis process much easier.

1 Like

thanks!! I thought the names of my nnet params were given by the fact I was using `nn.Linear` and thus being called “linear”, instead of the arbitrary name I used in my nnet class definition.

Sorry for the confusion, and thanks for clarifying.

the keys are the names if you printed:

``````for name, _ in nn_module.named_parameters():
print(name)
``````