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for name, param in model_new.named_parameters():
print(name, ':', param.requires_grad)
model_new = torch.load('entire_model.pth')
torch.save(model, 'entire_model.pth')
for name, param in model_new.named_parameters():
print(name, ':', param.requires_grad)
model_new = NeuralNet()
model_new.load_state_dict(torch.load('weights_only.pth'))
torch.save(model.state_dict(), 'weights_only.pth')
for key in model.fc.state_dict():
print('key: ', key)
param = model.fc.state_dict()[key]
print('param.shape: ', param.shape)
print('param.requires_grad: ', param.requires_grad)
print('param.shape, param.requires_grad: ', param.shape, param.requires_grad)
print('isinstance(param, nn.Module) ', isinstance(param, nn.Module))
print('isinstance(param, nn.Parameter) ', isinstance(param, nn.Parameter))
print('isinstance(param, torch.Tensor): ', isinstance(param, torch.Tensor))
print('=====')
for name, child in model.named_children():
print('name: ', name)
print('isinstance({}, nn.Module): '.format(name), isinstance(child, nn.Module))
print('=====')
isinstance(model.fc, nn.Module)