I need to write in PyTorch the equivalent to Python weights and bias:

W1 = **np.random.randn**(n_x, n_h) *0.01

b1 = **np.zeros** ((1, n_h))

While it exists torch.nn.init.zeros for the bias, I don’t find the way to set random weights and how to multiply them by a constant like the option in Python…

Here https://pytorch.org/docs/stable/nn.init.html it doesn’t exist the option…

```
class Net(nn.Module):
def __init__(self):
super(Net,self).__init__()
self.input_layer = nn.Linear(n_x, n_h)
*# torch.nn.init.uniform_(self.input_layer.weight)* #how to set this to random??
torch.nn.init.zeros_(self.input_layer.bias)
self.hidden_layer = nn.Linear(n_h,n_y)
*# torch.nn.init.uniform_(self.hidden_layer.weight)*
torch.nn.init.zeros_(self.hidden_layer.bias)
self.output_layer = nn.Linear(n_y,n_classes)
def forward(self, x):
x = x.view(-1, dim)
x = F.relu(self.input_layer(x))
x = F.relu(self.hidden_layer(x))
x = self.output_layer(x)
return F.log_softmax(x, dim=1)
```