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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!
845
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)))
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