Investigating the input-output relations of a quantum process can be seen as a learning problem. For instance, we could wish to find the optimal parameters for some quantum device that let it best mimic our target process, or we could simply wish to construct the best possible mathematical model of the process. Experimentally, we send probes through the quantum channel and use the outputs as our training set. When learning the action of a continuous variable (CV) quantum process in this way, there will often be some restriction on the input states used. One experimentally simple way to probe CV channels is using low-energy coherent states. Learning a quantum channel in this way presents difficulties, since two channels may act similarly on low energy inputs but very differently for high energy inputs. They may also act similarly on coherent state inputs but differently on non-classical inputs. Extrapolating the behaviour of a channel for more general input states from its action on the far more limited set of low energy coherent states is a case of out-of-distribution generalisation. To be sure that such generalisation gives meaningful results, one needs to relate error bounds for the training set to bounds that are valid for all inputs. We show that for any pair of channels that act sufficiently similarly on low energy coherent state inputs, one can bound how different the input-output relations are for any (high energy or highly non-classical) input. This proves out-of-distribution generalisation is always possible for learning quantum channels using low energy coherent states.