odeint_adjoint simply wraps around odeint, but will use only O(1) memory in exchange for solving an adjoint ODE in the backward call. The biggest gotcha is that func must be a nn.Module when using the adjoint method. This is used to collect parameters of the differential equation. Differen...
from torchdiffeq import odeint_adjoint as odeint class ODEfunc(nn.Module): def __init__(self, dim): super(ODEfunc, self).__init__() self.seq = nn.Sequential(nn.Linear(dim, 124), nn.ReLU(), nn.Linear(124, 124), nn.ReLU(), nn.Linear(124, dim), nn.Tanh()) def forward(sel...
odeint_adjoint simply wraps around odeint, but will use only O(1) memory in exchange for solving an adjoint ODE in the backward call. The biggest gotcha is that func must be a nn.Module when using the adjoint method. This is used to collect parameters of the differential equation. Keyword...
odeint_adjointsimply wraps aroundodeint, but will use only O(1) memory in exchange for solving an adjoint ODE in the backward call. The biggestgotchais thatfuncmust be ann.Modulewhen using the adjoint method. This is used to collect parameters of the differential equation. ...
odeint_adjoint simply wraps around odeint, but will use only O(1) memory in exchange for solving an adjoint ODE in the backward call.The biggest gotcha is that func must be a nn.Module when using the adjoint method. This is used to collect parameters of the differential equation....
odeint_adjointsimply wraps aroundodeint, but will use only O(1) memory in exchange for solving an adjoint ODE in the backward call. The biggestgotchais thatfuncmust be ann.Modulewhen using the adjoint method. This is used to collect parameters of the differential equation. ...
Backpropagation through odeint goes through the internals of the solver, but this is not supported for all solvers. Instead, we encourage the use of the adjoint method explained in [1], which will allow solving with as many steps as necessary due to O(1) memory usage....
odeint_adjoint simply wraps around odeint, but will use only O(1) memory in exchange for solving an adjoint ODE in the backward call. The biggest gotcha is that func must be a nn.Module when using the adjoint method. This is used to collect parameters of the differential equation. Keyword...
odeint_adjointsimply wraps aroundodeint, but will use only O(1) memory in exchange for solving an adjoint ODE in the backward call. The biggestgotchais thatfuncmust be ann.Modulewhen using the adjoint method. This is used to collect parameters of the differential equation. ...
odeint_adjointsimply wraps aroundodeint, but will use only O(1) memory in exchange for solving an adjoint ODE in the backward call. The biggestgotchais thatfuncmust be ann.Modulewhen using the adjoint method. This is used to collect parameters of the differential equation. ...