Note
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Divergence domain adaptation methods.
This example illustrates the DeepCoral method from [1] on a simple image classification task.
# Author: Théo Gnassounou
#
# License: BSD 3-Clause
# sphinx_gallery_thumbnail_number = 4
from skorch import NeuralNetClassifier
from torch import nn
from skada.datasets import load_mnist_usps
from skada.deep import DeepCoral
from skada.deep.modules import MNISTtoUSPSNet
Load the image datasets
dataset = load_mnist_usps(n_classes=2, n_samples=0.5, return_dataset=True)
X, y, sample_domain = dataset.pack(
as_sources=["mnist"], as_targets=["usps"], mask_target_labels=True
)
X_test, y_test, sample_domain_test = dataset.pack(
as_sources=[], as_targets=["usps"], mask_target_labels=False
)
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/home/circleci/project/skada/datasets/_mnist_usps.py:72: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
mnist_target = torch.tensor(mnist_dataset.targets)
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Train a classic model
model = NeuralNetClassifier(
MNISTtoUSPSNet(),
criterion=nn.CrossEntropyLoss(),
batch_size=128,
max_epochs=5,
train_split=False,
lr=1e-2,
)
model.fit(X[sample_domain > 0], y[sample_domain > 0])
model.score(X_test, y=y_test)
epoch train_loss dur
------- ------------ -------
1 1.3530 12.9001
2 0.1990 10.1447
3 0.0838 8.7053
4 0.0470 12.6023
5 0.0345 9.0915
0.9228295819935691
Train a DeepCoral model
model = DeepCoral(
MNISTtoUSPSNet(),
layer_name="fc1",
batch_size=128,
max_epochs=5,
train_split=False,
reg=1,
lr=1e-2,
)
model.fit(X, y, sample_domain=sample_domain)
model.score(X_test, y_test, sample_domain=sample_domain_test)
epoch train_loss dur
------- ------------ -------
1 1.5227 11.5933
2 0.3119 11.2990
3 0.1370 13.2985
4 0.0785 12.7983
5 0.0707 10.6999
0.9614147909967846
Total running time of the script: (2 minutes 4.104 seconds)