Pyro更改AutodiagonalNormal设置

Question

我使用pyro-ppl 3.0进行概率编程。当我看完贝叶斯回归的教程时。我使用AutoGuide和pyro.random_module将一个正常的前馈网络转换为贝叶斯网络。

# linear regression
class RegressionModel(nn.Module):
    def __init__(self, p):
        # p number of feature
        super(RegressionModel, self).__init__()
        self.linear1 = nn.Linear(p, 2)
        self.linear2 = nn.Linear(2, 1)
        self.softplus = nn.Softplus()

    def forward(self, x):
        x = self.softplus(self.linear1(x))
        return self.linear2(x)

# model
def model(x_data, y_data):
    # weight and bias prior
    w1_prior = Normal(torch.zeros(2,2), torch.ones(2,2)).to_event(2)
    b1_prior = Normal(torch.ones(2)*8, torch.ones(2)*1000).to_event(1)
    w2_prior = Normal(torch.zeros(1,2), torch.ones(1,2)).to_event(1)
    b2_prior = Normal(torch.ones(1)*3, torch.ones(1)*500).to_event(1)

    priors = {'linear1.weight': w1_prior, 'linear1.bias': b1_prior,
             'linear2.weight': w2_prior, 'linear2.bias': b2_prior}

    scale = pyro.sample("sigma", Uniform(0., 10.))

    # lift module parameters to random variables sampled from the priors
    lifted_module = pyro.random_module("module", regression_model, priors)
    # sample a nn (which also samples w and b)
    lifted_reg_model = lifted_module()
    with pyro.plate("map", len(x_data)):
        # run the nn forward on data
        prediction_mean = lifted_reg_model(x_data).squeeze(-1)
        # condition on the observed data
        pyro.sample("obs",
                    Normal(prediction_mean, scale),
                    obs=y_data)
        return prediction_mean

guide = AutoDiagonalNormal(model)

#================

#================

# inference
optim = Adam({"lr": 0.03})
svi = SVI(model, guide, optim, loss=Trace_ELBO(), num_samples=1000)

def train():
    pyro.clear_param_store()
    for j in range(num_iterations):
        # calculate the loss and take a gradient step
        loss = svi.step(x_data, y_data)
        if j % 100 == 0:
            print("[iteration %04d] loss: %.4f" % (j + 1, loss / len(data)))

train()

for name, value in pyro.get_param_store().items():
    print(name, pyro.param(name))

结果如下: auto_loc张量(-2.1585，-0.9799，-0.0378，-0.5000，-1.0241，2.6091，-1.3760，1.6920，0.2553，4.5768，requires_grad=True) auto_scale张量(0.1432，0.1017，0.0368，0.7588，0.4160，0.0624，0.6657，0.0431，0.2972，0.0901，grad_fn=)

潜变量的个数自动设置为10，我想更改这个数字。正如本教程中所提到的，我添加了

##
latent_dim = 5
pyro.param("auto_loc", torch.randn(latent_dim))
pyro.param("auto_scale", torch.ones(latent_dim),
           constraint=constraints.positive)

在上面提到的#=之间。

但是结果还是一样的。数字是不变的。那么，如何设置AutoDiagnalNormal函数来改变潜在变量的数量呢

Answer 1

我认为你只需要在训练设置之间进行pyro.clear_param_store()。我相信发生的情况是，您正在使用latent_dim=5进行训练，然后当您设置latent_dim=10时，旧的参数仍然在Pyro的全局参数存储中。请注意，pyro.param()语句的torch.randn(latent_dim)参数仅用于初始化，如果该参数已经初始化(可以在全局参数存储中找到)，则该参数将被忽略。