I am trying to build a classification model of MNIST data by using newton method of optimization.
After training my model. I am getting right prediction only for class 0. In test data there is 980 nos of O class samples. My model is giving test accuracy 9.8 % only. I checked pred=model(inputs). Found It is only correctly predicting for class O samples in test data.
Resnet18 with random weights is used
gradients are calculated with the help of
‘’'env_grads = torch.autograd.grad(loss, cnn.parameters(), retain_graph=True, create_graph=True)“”"
Hessian has been calculated with the help of "h_col=torch.autograd.grad(env_grads, cnn.parameters(), retain_graph=True, create_graph=False)
Training process has been initialized with following script
for batch, (X, y) in enumerate(train_loader):
wt1= torch.cat([gi.data.view(-1) for gi in cnn.parameters()]).view(-1, 1)
X = X.to(device)
y = y.to(device)
Please help me to find out the possible reasons behind this issue.