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Adversarial domain adaptation methods. — SKADA : Scikit Adaptation

This example illustrates the adversarial methods from on a simple image classification task.

# Author: Théo Gnassounou
#
# License: BSD 3-Clause
# sphinx_gallery_thumbnail_number = 4

dataset = load_mnist_usps(n_classes=2, n_samples=0.5, return_dataset=True)
X, y, sample_domain = dataset.pack_train(as_sources=["mnist"], as_targets=["usps"])
X_test, y_test, sample_domain_test = dataset.pack_test(as_targets=["usps"])

/home/circleci/project/skada/datasets/_mnist_usps.py:72: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  mnist_target = torch.tensor(mnist_dataset.targets)

  epoch    train_loss     dur
-------  ------------  ------
      1        1.8338  5.6983
      2        0.6723  4.7002
      3        0.2520  8.5028
      4        0.1241  6.5940
      5        0.0752  7.4009

0.9389067524115756

model = DANN(
    MNISTtoUSPSNet(),
    layer_name="fc1",
    batch_size=128,
    max_epochs=5,
    train_split=False,
    reg=0.01,
    num_features=128,
    lr=1e-2,
)
model.fit(X, y, sample_domain=sample_domain)
model.score(X_test, y_test, sample_domain=sample_domain_test)

/home/circleci/.local/lib/python3.10/site-packages/torch/nn/modules/module.py:1736: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
  return self._call_impl(*args, **kwargs)
  epoch    train_loss      dur
-------  ------------  -------
      1        2.5349  13.5681
      2        1.4122  14.4012
      3        1.1506  13.5996
      4        1.0899  13.6968
      5        1.0557  16.1884

0.954983922829582

Total running time of the script: (1 minutes 49.256 seconds)

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