387e650e16a5405691fb18ce05b34bd90239180e,deepchem/metalearning/maml.py,MAML,fit,#MAML#Any#Any#Any#Any#,202

Before Change

        inputs = self.learner.get_batch()
        feed_dict = {}
        feed_dict[self._global_step] = i
        for k in range(len(inputs)):
          feed_dict[self._input_placeholders[k]] = inputs[k]
          feed_dict[self._meta_placeholders[k]] = inputs[k]
        self._session.run(self._add_gradients, feed_dict=feed_dict)
      self._session.run(self._meta_train_op)

      // Do checkpointing.

After Change

          summed_gradients = meta_gradients
        else:
          summed_gradients = [
              s + g for s, g in zip(summed_gradients, meta_gradients)
          ]
      self._tf_optimizer.apply_gradients(zip(summed_gradients, variables))

      // Do checkpointing.

      if i == steps - 1 or time.time() >= checkpoint_time + checkpoint_interval:

Instances