c96af58e6ab2f85626e51a331e8479da49329f0e,deepchem/models/tensorflow_models/__init__.py,TensorflowClassifier,predict_proba_on_batch,#TensorflowClassifier#Any#Any#,654

Before Change

    if pad_batch:
      X = pad_features(self.batch_size, X)
    if not self._restored_model:
      self.restore()
    with self.eval_graph.graph.as_default():
      // run eval data through the model
      n_tasks = self.n_tasks
      with self._get_shared_session(train=False).as_default():

After Change

          // weight decay
          if self.penalty != 0.0:
            penalty = model_ops.weight_decay(self.penalty_type, self.penalty)
            loss += penalty

      return loss 

  def fit(self, dataset, nb_epoch=10, max_checkpoints_to_keep=5, 
	  log_every_N_batches=50, **kwargs):
    Fit the model.

    Parameters
    ---------- 
    dataset: dc.data.Dataset
      Dataset object holding training data 
    nb_epoch: 10
      Number of training epochs.
    max_checkpoints_to_keep: int

Instances