8375adccdb984204a235e426d28ed3d7aebd6360,deepchem/models/tensorgraph/models/graph_models.py,GraphConvModel,default_generator,#GraphConvModel#Any#Any#Any#Any#Any#,771

Before Change

                        pad_batches=True):
    for epoch in range(epochs):
      if not predict:
        print("Starting epoch %i" % epoch)
      for ind, (X_b, y_b, w_b, ids_b) in enumerate(
          dataset.iterbatches(
              self.batch_size,
              pad_batches=pad_batches,
              deterministic=deterministic)):
        d = {}
        for index, label in enumerate(self.my_labels):
          if self.mode == "classification":
            d[label] = to_one_hot(y_b[:, index])
          if self.mode == "regression":
            d[label] = np.expand_dims(y_b[:, index], -1)
        d[self.my_task_weights] = w_b
        multiConvMol = ConvMol.agglomerate_mols(X_b)
        d[self.atom_features] = multiConvMol.get_atom_features()
        d[self.degree_slice] = multiConvMol.deg_slice

After Change

              self.batch_size,
              pad_batches=pad_batches,
              deterministic=deterministic)):
        d = {}
        if self.mode == "classification":
          d[self.labels[0]] = to_one_hot(y_b.flatten(), self.n_classes).reshape(
              -1, self.n_tasks, self.n_classes)
        else:
          d[self.labels[0]] = y_b
        d[self.task_weights[0]] = w_b
        multiConvMol = ConvMol.agglomerate_mols(X_b)
        d[self.atom_features] = multiConvMol.get_atom_features()
        d[self.degree_slice] = multiConvMol.deg_slice

Instances