elbo = TraceMeanField_ELBO(num_particles=10,vectorize_particles=True)，Error
vectorize_particles set to False，How to solve this problem
Traceback (most recent call last):
File “/home/tranmap/miniconda3/envs/lq/lib/python3.6/site-packages/pyro/poutine/trace_messenger.py”, line 174, in call
ret = self.fn(*args, **kwargs)
File “/home/tranmap/miniconda3/envs/lq/lib/python3.6/site-packages/pyro/poutine/messenger.py”, line 12, in _context_wrap
return fn(*args, **kwargs)
File “/home/tranmap/miniconda3/envs/lq/lib/python3.6/site-packages/pyro/poutine/messenger.py”, line 12, in _context_wrap
return fn(*args, **kwargs)
File “/home/tranmap/miniconda3/envs/lq/lib/python3.6/site-packages/pyro/nn/module.py”, line 426, in call
return super().call(*args, **kwargs)
File “/home/tranmap/miniconda3/envs/lq/lib/python3.6/site-packages/torch/nn/modules/module.py”, line 1102, in _call_impl
return forward_call(*input, **kwargs)
File “/home/tranmap/liquan/万寿西宫/模型/火焰兵/第二种.py”, line 139, in forward
self.lstm.flatten_parameters()
File “/home/tranmap/miniconda3/envs/lq/lib/python3.6/site-packages/torch/nn/modules/rnn.py”, line 179, in flatten_parameters
self.batch_first, bool(self.bidirectional))
RuntimeError: shape ‘[2400, 1]’ is invalid for input of size 24000
Here’s my model
class BayesianLSTM(PyroModule):
def init(self, input_size, hidden_size=50, num_layers=2, dropout_prob=0.2):
super().init()
self.hidden_size = hidden_size
self.num_layers = num_layers
# 使用PyTorch内置LSTM
self.lstm = PyroModule[nn.LSTM](
input_size=input_size,
hidden_size=hidden_size,
num_layers=num_layers,
dropout=dropout_prob,
batch_first=True
)
# 收集所有参数名称
param_names = list(self.lstm.named_parameters())
# 现在为每个参数设置先验分布
for name, param in param_names:
if 'weight_ih' in name:
setattr(self.lstm, name, PyroSample(
dist.Normal(0, torch.tensor(0.15, device=device)).expand(param.shape).to_event(param.dim())
))
elif 'weight_hh' in name:
setattr(self.lstm, name, PyroSample(
dist.Normal(0, torch.tensor(0.1, device=device)).expand(param.shape).to_event(param.dim())
))
elif 'bias' in name:
setattr(self.lstm, name, PyroSample(
dist.Normal(0, torch.tensor(0.05, device=device)).expand(param.shape).to_event(param.dim())
))
# Dropout 层（额外在 LSTM 输出后）
self.dropout = nn.Dropout(p=dropout_prob)
# 改为普通 nn.Linear 层
self.linear = nn.Linear(hidden_size, 1)
# self.linear = PyroModule[nn.Linear](hidden_size, 1)
# 设置线性层的贝叶斯参数
# self.linear.weight = PyroSample(
# dist.Normal(0, torch.tensor(0.1, device=device)).expand(self.linear.weight.shape).to_event(
# self.linear.weight.dim())
# )
# self.linear.bias = PyroSample(
# dist.Normal(0, torch.tensor(0.05, device=device)).expand(self.linear.bias.shape).to_event(
# self.linear.bias.dim())
# )
def forward(self, x, y=None,annealing_factor=1.0):
# 确保参数连续性
self.lstm.flatten_parameters()
# 初始化隐藏状态
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size, device=device)
c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size, device=device)
lstm_out, _ = self.lstm(x, (h0, c0))
dropout_out = self.dropout(lstm_out[:, -1, :])
mu = self.linear(dropout_out).squeeze(-1)
pyro.deterministic("mu", mu)
if y is not None:
#对数均值，对数标准差
sigma = pyro.sample("sigma", dist.LogNormal(loc=torch.tensor(-1.0,device=device), scale=torch.tensor(0.5,device=device)))
with pyro.plate("data", x.size(0)):
pyro.sample("obs", dist.Normal(mu, sigma), obs=y.squeeze(-1),infer={"scale": annealing_factor}) #obs，真实数据+噪声
return mu
And my loss is basically tens of thousands, what is the reason?