Hi, I am trying to initialize the weights of a conv net (with nn.Sequential) using a custom method.

When I do this initialization my network achieves an accuracy equal to ~10% (for CIFAR-10 this is equivalent to a random response => the network doesn’t learn anything). Without this initialization I get ~58% accuracy (the conv net can learn without this init).

I am sure that I am doing something wrong but I don’t know where is the problem. I would like to initialize the weights using the `weights_init`

, `random_weight`

, `zero_weight`

methods. Any help/advice is welcome . Thanks.

The code (some code is from the cs231 course from Stanford):

```
def random_weight(shape):
"""
Kaiming normalization: sqrt(2 / fan_in)
"""
if len(shape) == 2: # FC weight
fan_in = shape[0]
else:
fan_in = np.prod(shape[1:]) # conv weight [out_channel, in_channel, kH, kW]
w = torch.randn(shape, device=device, dtype=dtype) * np.sqrt(2. / fan_in)
w.requires_grad = True
return w
def zero_weight(shape):
return torch.zeros(shape, device=device, dtype=dtype, requires_grad=True)
def weights_init(m):
if type(m) in [nn.Conv2d, nn.Linear]:
m.weight.data = random_weight(m.weight.data.size())
m.bias.data = zero_weight(m.bias.data.size())
class Flatten(nn.Module):
def forward(self, x):
return flatten(x)
model = nn.Sequential(
nn.Conv2d(in_channel, channel_1, (5, 5), padding=2),
nn.ReLU(),
nn.Conv2d(channel_1, channel_2, (3, 3), padding=1),
nn.ReLU(),
Flatten(),
nn.Linear(channel_2 * 32 * 32, num_classes)
)
model.apply(weights_init)
optimizer = optim.SGD(model.parameters(), lr=learning_rate,
momentum=0.9, nesterov=True)
```