Turn off dropout to see if that fixes tests

  classification_metric = dc.metrics.Metric(dc.metrics.accuracy_score)

  n_atom_feat = 75

  n_pair_feat = 14

  n_feat = 128

  batch_size = 10

  model = dc.models.WeaveModel(

      n_tasks,

      n_atom_feat=n_atom_feat,

      n_pair_feat=n_pair_feat,

      n_graph_feat=n_feat,

      batch_size=batch_size,

      learning_rate=0.001,

      use_queue=False,

      learning_rate=0.0003,

      dropout=0.0,

      mode="classification")

  # Fit trained model

  model.fit(dataset, nb_epoch=20)

  model.fit(dataset, nb_epoch=100)

  # Eval model on train

  scores = model.evaluate(dataset, [classification_metric])

  assert scores[classification_metric.name] > .65

@pytest.mark.slow

def test_weave_singletask_regression_overfit():

  """Test weave model overfits tiny data."""

  np.random.seed(123)

  regression_metric = dc.metrics.Metric(

      dc.metrics.pearson_r2_score, task_averager=np.mean)

  n_atom_feat = 75

  n_pair_feat = 14

  n_feat = 128

  batch_size = 10

  model = dc.models.WeaveModel(

      n_tasks,

      n_atom_feat=n_atom_feat,

      n_pair_feat=n_pair_feat,

      n_graph_feat=n_feat,

      batch_size=batch_size,

      learning_rate=0.001,

      use_queue=False,

      learning_rate=0.0003,

      dropout=0.0,

      mode="regression")

  # Fit trained model