gradient.py

# This function calculates, loss, predictions and gradients 
def covnet(t,params):
  test_acc,target_class, predicted_class = accuracy(params, shape_as_image(test_images, test_labels))
  test_loss = loss(params, shape_as_image(test_images, test_labels),test=t)
  grads = grad(lo)(shape_as_image(test_images, test_labels),params)
  if(t==1):
      print('Test set loss, accuracy (%): ({:.2f}, {:.2f})'.format(test_loss, 100 * test_acc))
      print('predicted_class,target_class', predicted_class,target_class)
  return grads, test_acc