So I’m using skorch for a Project. I find it super cool, the API is straightforward to work with. I trained my NN and I’m using a Checkpoint callback to save the best params, optimizer and history as described here: [https://skorch.readthedocs.io/en/stable/callbacks.html 3] , so now I want to load the saved model or rather parameters and I’m using the description in the docs:
load_params 1 . As I said, I saved the model using Checkpoint object so I thought why not using the same Checkpoint object to load the model but I think it is messy because I must declare the Checkpoint object again etc… I noticed when I load the model that somehow the fit function is called again and my model is starting a training process again! can someone tell me why it is happening or what is wrong with my code maybe? another thing is that I find it bothering to again write a Checkpoint object to load the model after saving it and I’m sure there is a better way that’s why I want to ask is there any way to do this like in pytorch by simply calling torch.load(model_path) ?
this is my code:

from skorch.callbacks import Checkpoint, TrainEndCheckpoint, LoadInitState
from skorch.dataset import CVSplit
from skorch.regressor import NeuralNetRegressor
from models import BearingCoarseNetwork, BearingDifferenceNetwork
import torch
from torch import optim
from main import get_datasets
device = ‘cuda’ if torch.cuda.is_available() else ‘cpu’
coarse_checkpoint = Checkpoint(dirname=‘results/bearing/coarse’)
coarse_train_end_checkpoint = TrainEndCheckpoint(dirname=‘results/bearing/coarse’)
bearing_coarse_model = NeuralNetRegressor(
module=BearingCoarseNetwork(n_hidden=32),
)
diff_checkpoint = Checkpoint(dirname=‘results/bearing/difference’)
diff_train_end_checkpoint = TrainEndCheckpoint(dirname=‘results/bearing/difference’)
bearing_difference_model = NeuralNetRegressor(
module=BearingDifferenceNetwork(n_hidden=32),
)
bearing_coarse_model.initialize()
bearing_difference_model.initialize()
bearing_coarse_model.load_params(checkpoint=coarse_checkpoint)
bearing_difference_model.load_params(checkpoint=diff_checkpoint)
trainset, testset = get_datasets()
coarse_preds = bearing_coarse_model.predict(trainset)
diff_preds = bearing_difference_model.predict(trainset)
y_final = coarse_preds + diff_preds
print(y_final.shape)