Dear All,

After reading different threads, I implemented a method which considered as the “standard one” to initialize the paramters ol all layers (see code below):

import torch

import torch.nn as nn

import torch.nn.functional as F

class Net(nn.Module):

```
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 5)
self.conv2 = nn.Conv2d(32, 64, 5)
self.conv3 = nn.Conv2d(64, 128, 5)
self.conv4 = nn.Conv2d(128, 256, 3)
self.conv5 = nn.Conv2d(256, 256, 2)
self.fc1 = nn.Linear(6400, 6400)
self.fc2 = nn.Linear(6400, 6400)
self.fc3 = nn.Linear(6400, 136)
def weights_init(m):
if isinstance(m, nn.Conv2d):
nn.init.xavier_normal_(m.weight.data)
nn.init.xavier_normal_(m.bias.data)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight,1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm1d):
nn.init.constant_(m.weight,1)
nn.init.constant_(m.bias, 0)
## feedforward behavior
def forward(self, x):
# check whether tanh is preferable here?
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.dropout(x, p = 0.1)
x = F.relu(F.max_pool2d(self.conv2(x), 2))
x = F.dropout(x, p = 0.2)
x = F.relu(F.max_pool2d(self.conv3(x), 2))
x = F.dropout(x, p = 0.3)
x = F.relu(F.max_pool2d(self.conv4(x), 2))
x = F.dropout(x, p = 0.4)
x = F.relu(F.avg_pool2d(self.conv5(x), 2))
x = F.dropout(x, p = 0.5)
x = x.view(x.size(0), -1)
x = F.relu(self.fc1(x))
x = F.dropout(x, p = 0.5)
x = F.relu(self.fc2(x))
x = F.dropout(x, p = 0.6)
y_pred = F.tanh(self.fc3(x))
return y_pred
```

but when i enter:

net.apply(weights_init)

I got the follwoing error:

NameError Traceback (most recent call last)

in ()

98 net = Net()

99 #BN_net.weights_init()

–> 100 net.apply(weights_init)

101 print (net)

NameError: name ‘weights_init’ is not defined

Can someone can tell me what is wrong here?

Thank you very much