490b5acedeb65d17c9d6fd965f28e8146e673656,deepchem/models/tensorgraph/models/graph_models.py,WeaveModel,default_generator,#WeaveModel#Any#Any#Any#Any#Any#,151

Before Change

        feed_dict = dict()
        if y_b is not None:
          for index, label in enumerate(self.labels_fd):
            if self.mode == "classification":
              feed_dict[label] = to_one_hot(y_b[:, index])
            if self.mode == "regression":
              feed_dict[label] = y_b[:, index:index + 1]
        if w_b is not None:
          feed_dict[self.weights] = w_b

        atom_feat = []
        pair_feat = []

After Change

      weighted_loss = ReduceSum(L2Loss(in_layers=[labels, output, weights]))
      self.set_loss(weighted_loss)

  def default_generator(self,
                        dataset,
                        epochs=1,
                        predict=False,
                        deterministic=True,
                        pad_batches=True):
    TensorGraph style implementation 
    for epoch in range(epochs):
      if not predict:
        print("Starting epoch %i" % epoch)
      for (X_b, y_b, w_b, ids_b) in dataset.iterbatches(
          batch_size=self.batch_size,
          deterministic=deterministic,
          pad_batches=pad_batches):

        feed_dict = dict()
        if y_b is not None:
          if self.mode == "classification":
            feed_dict[self.labels[0]] = to_one_hot(y_b.flatten(),
                                                   self.n_classes).reshape(
                                                       -1, self.n_tasks,
                                                       self.n_classes)
          else:
            feed_dict[self.labels[0]] = y_b
        if w_b is not None:
          feed_dict[self.task_weights[0]] = w_b

        atom_feat = []
        pair_feat = []

Instances