Hi all,
I am new to pyro and I am trying to use bayesian logistic regression where the prior distributions are Poisson (FYI, I got most of the code from internet 
). My input data size (x_) is [20000, 88] and my output data size (y_) is [20000,]. I could be able to run the code when the distributions are Normal. Here is the code that I am running. I tried to look at the similar issues other had but couldn’t apply it to my problem.
import pyro
from pyro.distributions import Normal, Categorical, Poisson
from pyro.infer import SVI, Trace_ELBO
from pyro.optim import Adam
class LogisticRegression(torch.nn.Module):
def __init__(self, input_dim, output_dim):
super(LogisticRegression, self).__init__()
self.fc1 = torch.nn.Linear(input_dim, output_dim)
def forward(self, x):
outputs = self.fc1(x)
return outputs
input_size = len(df.columns) - 1
net = LogisticRegression(input_size,7)
net = net.float()
log_softmax = nn.LogSoftmax(dim=1)
def model(x_data, y_data):
fc1w_prior = Poisson(rate=torch.ones_like(net.fc1.weight))
fc1b_prior = Poisson(rate=torch.ones_like(net.fc1.bias))
priors = {'fc1.weight': fc1w_prior, 'fc1.bias': fc1b_prior}
# lift module parameters to random variables sampled from the priors
lifted_module = pyro.random_module("module", net, priors)
# sample a regressor (which also samples w and b)
lifted_reg_model = lifted_module()
lhat = log_softmax(lifted_reg_model(x_data))
pyro.sample("obs", Categorical(logits=lhat), obs=y_data)
def guide(x_data, y_data):
# First layer weight distribution priors
fc1w_rate = torch.randn_like(net.fc1.weight)
fc1w_rate_param = softplus(pyro.param("fc1w_rate", fc1w_rate))
fc1w_prior = Poisson(rate=fc1w_rate_param)
# First layer bias distribution priors
fc1b_rate = torch.randn_like(net.fc1.bias)
fc1b_rate_param = softplus(pyro.param("fc1b_rate", fc1b_rate))
fc1b_prior = Poisson(rate=fc1b_rate_param).independent(1)
priors = {'fc1.weight': fc1w_prior, 'fc1.bias': fc1b_prior}
lifted_module = pyro.random_module("module", net, priors)
return lifted_module()
optim = Adam({"lr": 0.001})
svi = SVI(model, guide, optim, loss=Trace_ELBO())
target_col = 'dependent_feature'
num_iterations = 250
loss = 0
batch_size = 10000
batch_list = [i for i in range(0,len(df)+1,batch_size)]
for j in range(num_iterations):
loss = 0
for i in range(len(batch_list)-1):
data = df.iloc[batch_list[i]:batch_list[i+1]]
x_ = torch.from_numpy(data.drop([target_col], axis=1).values).float()
y_ = torch.from_numpy(data[target_col].values).float()
# calculate the loss and take a gradient step
loss += svi.step(x_, y_)
normalizer_train = len(temp1_p)
total_epoch_loss_train = loss / normalizer_train
print("Epoch ", j, " Loss ", total_epoch_loss_train)
I am getting the following error:
The size of tensor a (88) must match the size of tensor b (7) at non-singleton dimension 1
And here is the complete error code,
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<command-726574161117256> in <module>()
12 y_ = torch.from_numpy(data[target_col].values).float()
13 # calculate the loss and take a gradient step
---> 14 loss += svi.step(x_, y_)
15 normalizer_train = len(temp1_p)
16 total_epoch_loss_train = loss / normalizer_train
/databricks/python/lib/python3.5/site-packages/pyro/infer/svi.py in step(self, *args, **kwargs)
96 # get loss and compute gradients
97 with poutine.trace(param_only=True) as param_capture:
---> 98 loss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)
99
100 params = set(site["value"].unconstrained()
/databricks/python/lib/python3.5/site-packages/pyro/infer/trace_elbo.py in loss_and_grads(self, model, guide, *args, **kwargs)
122 # grab a trace from the generator
123 for model_trace, guide_trace in self._get_traces(model, guide, *args, **kwargs):
--> 124 loss_particle, surrogate_loss_particle = self._differentiable_loss_particle(model_trace, guide_trace)
125 loss += loss_particle / self.num_particles
126
/databricks/python/lib/python3.5/site-packages/pyro/infer/trace_elbo.py in _differentiable_loss_particle(self, model_trace, guide_trace)
91 if not is_identically_zero(score_function_term):
92 if log_r is None:
---> 93 log_r = _compute_log_r(model_trace, guide_trace)
94 site = log_r.sum_to(site["cond_indep_stack"])
95 surrogate_elbo_particle = surrogate_elbo_particle + (site * score_function_term).sum()
/databricks/python/lib/python3.5/site-packages/pyro/infer/trace_elbo.py in _compute_log_r(model_trace, guide_trace)
18 if not model_site["is_observed"]:
19 log_r_term = log_r_term - guide_trace.nodes[name]["log_prob"]
---> 20 log_r.add((stacks[name], log_r_term.detach()))
21 return log_r
22
/databricks/python/lib/python3.5/site-packages/pyro/infer/util.py in add(self, *items)
110 assert all(f.dim < 0 and -value.dim() <= f.dim for f in frames)
111 if frames in self:
--> 112 self[frames] = self[frames] + value
113 else:
114 self[frames] = value
RuntimeError: The size of tensor a (88) must match the size of tensor b (7) at non-singleton dimension 1
Note: I also removed the independent(1) but I got the same error.