I was hoping that the following two scripts would get the same model, but it seems that due to the randomness of the dropout, I am not able to get the same model.

The code abstraction is as follows

```
# script 1
for t in range(100):
vec = torch.randn([1,500])
s = compute_score(method="std", vec=vec)
some_functions(s)
TrainNetwork(model)
# script 2
for t in range(100):
vec = torch.randn([1,500])
s = compute_score(method="NoE", vec=vec)
some_functions(s)
TrainNetwork(model)
```

In the function compute_score I set `nu=0`

, so the two different methods, “NoE” and “std”, should give the same result.

```
import torch
from torch import nn
class NetworkFC2(nn.Module):
def __init__(self) -> None:
super().__init__()
self.fc1 = nn.Linear(500, 100)
self.fc2 = nn.Linear(100, 1)
self.dropout = nn.Dropout(p = 0.2)
def forward(self, vec):
"""
vec.shape = (1, 500)
"""
o1 = self.dropout(self.fc1(vec))
return self.fc2(o1)
torch.manual_seed(123)
model = NetworkFC2()
def compute_score(method, vec):
if method == "std":
model.train()
score_v = torch.zeros(20)
for i in range(20):
score_v[i] = model(vec)
std = score_v.std()
model.eval()
s0 = model(vec)
nu = 0
score = s0 + nu * std
elif method == "NoE":
model.eval()
score = model(vec)
return score
def TrainNetwork(model):
pass
def some_functions(score):
pass
```

But since the number of dropout runs in the “std” method is more than in the “NoE” method, the randomness of the dropout is different and I can’t get the same model.

Is there a way to solve this problem?

Thanks in advance for any help/suggestion.