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Simplify hessian_tensor_product, removing numpy dependency #348

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Simplify hessian_tensor_product, removing numpy dependency #348

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6 changes: 2 additions & 4 deletions autograd/differential_operators.py
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Original file line number Diff line number Diff line change
Expand Up @@ -83,10 +83,8 @@ def hessian_tensor_product(fun, argnum=0):
The returned function has arguments (*args, tensor, **kwargs), and for
vectors takes roughly 4x as long to evaluate as the original function."""
fun_grad = grad(fun, argnum)
def vector_dot_grad(*args, **kwargs):
args, vector = args[:-1], args[-1]
return np.tensordot(fun_grad(*args, **kwargs), vector, np.ndim(vector))
return grad(vector_dot_grad, argnum)
return (lambda *args, **kwargs:
make_vjp(fun_grad, argnum)(*args[:-1], **kwargs)[0](args[-1]))
hessian_vector_product = hessian_tensor_product

def tensor_jacobian_product(fun, argnum=0):
Expand Down