I tried to find the answer but I can't.
I make a custom deep learning model using pytorch. For example,
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.nn_layers = nn.ModuleList()
self.layer = nn.Linear(2,3).double()
torch.nn.init.xavier_normal_(self.layer.weight)
self.bias = torch.nn.Parameter(torch.randn(3))
self.nn_layers.append(self.layer)
def forward(self, x):
activation = torch.tanh
output = activation(self.layer(x)) + self.bias
return output
If I print
model = Net()
print(list(model.parameters()))
it does not contains model.bias, so optimizer = optimizer.Adam(model.parameters()) does not update model.bias. How can I go through this? Thanks!
Saving and Loading Model Weights To load model weights, you need to create an instance of the same model first, and then load the parameters using load_state_dict() method. be sure to call model. eval() method before inferencing to set the dropout and batch normalization layers to evaluation mode.
PyTorch doesn't have a utility function (at least at the moment!) to count the number of model parameters, but there is a property of the model class that you can use to get the model parameters. model. parameters(): PyTorch modules have a a method called parameters() which returns an iterator over all the parameters.
The PyTorch parameter is a layer made up of nn or a module. A parameter that is assigned as an attribute inside a custom model is registered as a model parameter and is thus returned by the caller model. parameters(). We can say that a Parameter is a wrapper over Variables that are formed.
You need to register your parameters:
self.register_parameter(name='bias', param=torch.nn.Parameter(torch.randn(3)))
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With