de7f5863338af5e1f92257cb29a6ca9a1c52c473,deepchem/metalearning/tests/test_maml.py,TestMAML,test_sine,#TestMAML#,12

Before Change

    new_maml = dc.metalearning.MAML(learner, model_dir=maml.model_dir)
    new_maml.fit(0, restore=True)
    feed_dict = {}
    for j in range(len(batch)):
      feed_dict[new_maml._input_placeholders[j]] = batch[j]
      feed_dict[new_maml._meta_placeholders[j]] = batch[j]
    new_loss = np.average(
        np.sqrt(new_maml._session.run(new_maml._loss, feed_dict=feed_dict)))
    assert new_loss == loss1[-1]

After Change

    new_maml = dc.metalearning.MAML(learner, model_dir=maml.model_dir)
    new_maml.restore()
    loss, outputs = new_maml.predict_on_batch(batch)
    assert np.sqrt(loss) == loss1[-1]

    // Do the same thing, only using the "restore" argument to fit().

Instances