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

Before Change

        batch_outputs = np.asarray(data[:n_tasks], dtype=float)
        // reshape to batch_size x n_tasks x ...
        if batch_outputs.ndim == 3:
          batch_outputs = batch_outputs.transpose((1, 0, 2))
        elif batch_outputs.ndim == 2:
          batch_outputs = batch_outputs.transpose((1, 0))
        else:
          raise ValueError(
              "Unrecognized rank combination for output: %s " %
              (batch_outputs.shape,))

      // Note that softmax is already applied in construct_grpah
      outputs = batch_outputs

    return np.copy(outputs)

class TensorflowRegressor(TensorflowGraphModel):
  Regression model.

After Change

      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