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src/p/y/pylearn2-HEAD/pylearn2/costs/dbm.py   pylearn2(Download)
            params = list(layer.get_params())
            fake_grads = pylearn2.utils.grad(
                cost=None,
                consider_constant=flatten(state_below),
                wrt=params,

src/p/y/pylearn2-HEAD/pylearn2/optimization/batch_gradient_descent.py   pylearn2(Download)
 
from pylearn2.utils import function
from pylearn2.utils import grad
from pylearn2.utils import safe_zip
from pylearn2.utils import sharedX
                g = self.gradients[param]
            else:
                g = grad(objective, param)
            param_to_grad_sym[param] = g
            if param.name is not None: