## General approach¶

skorch provides several ways to persist your model. First it is possible to store the model using Python’s pickle function. This saves the whole model, including hyperparameters. This is useful when you don’t want to initialize your model before loading its parameters, or when your NeuralNet is part of an sklearn Pipeline:

net = NeuralNet(
module=MyModule,
criterion=torch.nn.NLLLoss,
)

model = Pipeline([
('my-features', get_features()),
('net', net),
])
model.fit(X, y)

# saving
with open('some-file.pkl', 'wb') as f:
pickle.dump(model, f)

with open('some-file.pkl', 'rb') as f:


The disadvantage of pickling is that if your underlying code changes, unpickling might raise errors. Also, some Python code (e.g. lambda functions) cannot be pickled.

For this reason, we provide a second method for persisting your model. To use it, call the save_params() and load_params() method on NeuralNet. Under the hood, this saves the module’s state_dict, i.e. only the weights and biases of the module. This is more robust to changes in the code but requires you to initialize a NeuralNet to load the parameters again:

net = NeuralNet(
module=MyModule,
criterion=torch.nn.NLLLoss,
)

model = Pipeline([
('my-features', get_features()),
('net', net),
])
model.fit(X, y)

net.save_params(f_params='some-file.pkl')

new_net = NeuralNet(
module=MyModule,
criterion=torch.nn.NLLLoss,
)
new_net.initialize()  # This is important!


In addition to saving the model parameters, the history and optimizer state can be saved by including the f_history and f_optimizer keywords to save_params() and load_params() on NeuralNet. This feature can be used to continue training:

net = NeuralNet(
module=MyModule
criterion=torch.nn.NLLLoss,
)

net.fit(X, y, epochs=2) # Train for 2 epochs

net.save_params(
f_params='model.pkl', f_optimizer='opt.pkl', f_history='history.json')

new_net = NeuralNet(
module=MyModule
criterion=torch.nn.NLLLoss,
)
new_net.initialize() # This is important!
f_params='model.pkl', f_optimizer='opt.pkl', f_history='history.json')

new_net.fit(X, y, epochs=2) # Train for another 2 epochs


Note

In order to use this feature, the history must only contain JSON encodable Python data structures. Numpy and PyTorch types should not be in the history.

Note

save_params() does not store learned attributes on the net. E.g., skorch.classifier.NeuralNetClassifier remembers the classes it encountered during training in the classes_ attribute. This attribute will be missing after load_params(). Therefore, if you need it, you should pickle.dump() the whole net.

## Using callbacks¶

skorch provides Checkpoint, TrainEndCheckpoint, and LoadInitState callbacks to handle saving and loading models during training. To demonstrate these features, we generate a dataset and create a simple module:

import numpy as np
from sklearn.datasets import make_classification
from torch import nn

X, y = make_classification(1000, 10, n_informative=5, random_state=0)
X = X.astype(np.float32)
y = y.astype(np.int64)

class MyModule(nn.Sequential):
def __init__(self, num_units=10):
super().__init__(
nn.Linear(10, num_units),
nn.ReLU(inplace=True),
nn.Dropout(0.2),
nn.Linear(num_units, 10),
nn.Linear(10, 2),
nn.Softmax(dim=-1)
)


Then we create two different checkpoint callbacks and configure them to save the model parameters, optimizer, and history into a directory named 'exp1':

# First run

from skorch.callbacks import Checkpoint, TrainEndCheckpoint
from skorch import NeuralNetClassifier

cp = Checkpoint(dirname='exp1')
train_end_cp = TrainEndCheckpoint(dirname='exp1')
net = NeuralNetClassifier(
MyModule, lr=0.5, callbacks=[cp, train_end_cp]
)

_ = net.fit(X, y)

# prints
epoch    train_loss    valid_acc    valid_loss    cp     dur
-------  ------------  -----------  ------------  ----  ------
1        0.6200       0.8209        0.4765     +  0.0232
2        0.3644       0.8557        0.3474     +  0.0238
3        0.2875       0.8806        0.3201     +  0.0214
4        0.2514       0.8905        0.3080     +  0.0237
5        0.2333       0.9154        0.2844     +  0.0203
6        0.2177       0.9403        0.2164     +  0.0215
7        0.2194       0.9403        0.2159     +  0.0220
8        0.2027       0.9403        0.2299        0.0202
9        0.1864       0.9254        0.2313        0.0196
10        0.2024       0.9353        0.2333        0.0221


By default, Checkpoint observes valid_loss metric and saves the model when the metric improves. This is indicated by the + mark in the cp column of the logs.

On our first run, the validation loss did not improve after the 7th epoch. We can lower the learning rate and continue training from this checkpoint by using LoadInitState:

from skorch.callbacks import LoadInitState

cp = Checkpoint(dirname='exp1')
net = NeuralNetClassifier(
)

_ = net.fit(X, y)

# prints

epoch    train_loss    valid_acc    valid_loss    cp     dur
-------  ------------  -----------  ------------  ----  ------
8        0.1939       0.9055        0.2626     +  0.0238
9        0.2055       0.9353        0.2031     +  0.0239
10        0.1992       0.9453        0.2101        0.0182
11        0.2033       0.9453        0.1947     +  0.0211
12        0.1825       0.9104        0.2515        0.0185
13        0.2010       0.9453        0.1927     +  0.0187
14        0.1508       0.9453        0.1952        0.0198
15        0.1679       0.9502        0.1905     +  0.0181
16        0.1516       0.9453        0.1864     +  0.0192
17        0.1576       0.9453        0.1804     +  0.0184


The LoadInitState callback is executed once in the beginning of the training procedure and initializes model, history, and optimizer parameters from a specified checkpoint (if it exists). In our case, the previous checkpoint was created at the end of epoch 7, so the second run resumes from epoch 8. With a lower learning rate, the validation loss was able to improve!

Notice that in the first run we included a TrainEndCheckpoint in the list of callbacks. As its name suggests, this callback creates a checkpoint at the end of training. As before, we can pass it to LoadInitState to continue training:

cp_from_final = Checkpoint(dirname='exp1', fn_prefix='from_train_end_')
net = NeuralNetClassifier(
)

_ = net.fit(X, y)

# prints

epoch    train_loss    valid_acc    valid_loss    cp     dur
-------  ------------  -----------  ------------  ----  ------
11        0.1663       0.9453        0.2166     +  0.0282
12        0.1880       0.9403        0.2237        0.0178
13        0.1813       0.9353        0.1993     +  0.0161
14        0.1744       0.9353        0.1955     +  0.0150
15        0.1538       0.9303        0.2053        0.0077
16        0.1473       0.9403        0.1947     +  0.0078
17        0.1563       0.9254        0.1989        0.0074
18        0.1558       0.9403        0.1877     +  0.0075
19        0.1534       0.9254        0.2318        0.0074
20        0.1779       0.9453        0.1814     +  0.0074


In this run, training started at epoch 11, continuing from the end of the first run which ended at epoch 10. We created a new Checkpoint callback with fn_prefix set to 'from_train_end_' to prefix the saved filenames with 'from_train_end_' to make sure this checkpoint does not override the checkpoint from the previous run.

Since our MyModule class allows num_units to be adjusted, we can start a new experiment by changing the dirname:

cp = Checkpoint(dirname='exp2')
net = NeuralNetClassifier(
MyModule, lr=0.5,
module__num_units=20,
)

_ = net.fit(X, y)

# prints

epoch    train_loss    valid_acc    valid_loss    cp     dur
-------  ------------  -----------  ------------  ----  ------
1        0.5256       0.8856        0.3624     +  0.0181
2        0.2956       0.8756        0.3416     +  0.0222
3        0.2280       0.9453        0.2299     +  0.0211
4        0.1948       0.9303        0.2136     +  0.0232
5        0.1800       0.9055        0.2696        0.0223
6        0.1605       0.9403        0.1906     +  0.0190
7        0.1594       0.9403        0.2027        0.0184
8        0.1319       0.9303        0.1910        0.0220
9        0.1558       0.9254        0.1923        0.0189
10        0.1432       0.9303        0.2219        0.0192


This stores the model into the 'exp2' directory. Since this is the first run, the LoadInitState callback does not do anything. If we were to run the above script again, the LoadInitState callback will load the model from the checkpoint.

In the run above, the last checkpoint was created at epoch 6, we can load this checkpoint to predict with it:

net = NeuralNetClassifier(
MyModule, lr=0.5, module__num_units=20,
)
net.initialize()

y_pred = net.predict(X)


In this case, it is important to initialize the neural net before running NeuralNet.load_params().