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

Before Change

      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():
        feed_dict = self.construct_feed_dict(X)
        data = self._get_shared_session(train=False).run(
            self.eval_graph.output, feed_dict=feed_dict)
        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

          // 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.

Instances