c09ca92e84f45c61f9213a32c7f01627fd467174,deepchem/models/tensorgraph/tensor_graph.py,TensorGraph,predict_proba_on_batch,#TensorGraph#Any#Any#,178

Before Change

    if not self.built:
      self.build()
    if sess is None:
      saver = tf.train.Saver()
      with tf.Session() as sess:
        saver.restore(sess, self.last_checkpoint)
        with self._get_tf("Graph").as_default():
          retval = predict()
    else:
      retval = predict()
    if self.mode == "classification":  // sample, task, class
      retval = np.transpose(retval, axes=[1, 0, 2])
    elif self.mode == "regression":  // sample, task
      retval = np.transpose(retval, axes=[1, 0])

After Change

    return self.predict_on_generator(generator)

  def predict_proba_on_batch(self, X, sess=None):
    dataset = NumpyDataset(X=X, y=None)
    generator = self.default_generator(dataset, predict=True, pad_batches=False)
    return self.predict_proba_on_generator(generator)

  def predict(self, dataset, transformers=[], batch_size=None):
    
    Uses self to make predictions on provided Dataset object.

Instances