ed7782f0f59f1eef6453f47633fe9e15bda00f84,deepchem/models/tensorflow_models/__init__.py,TensorflowGraphModel,fit,#TensorflowGraphModel#Any#Any#Any#Any#Any#,298

Before Change

        saver = tf.train.Saver(max_to_keep=max_checkpoints_to_keep)
        // Save an initial checkpoint.
        saver.save(sess, self._save_path, global_step=0)
        for epoch in range(nb_epoch):
          avg_loss, n_batches = 0., 0
          for ind, (X_b, y_b, w_b, ids_b) in enumerate(
              // Turns out there are valid cases where we don"t want pad-batches
              // on by default.
              //dataset.iterbatches(batch_size, pad_batches=True)):
              dataset.iterbatches(
                  self.batch_size, pad_batches=self.pad_batches)):
            if ind % log_every_N_batches == 0:
              log("On batch %d" % ind, self.verbose)
            // Run training op.
            feed_dict = self.construct_feed_dict(X_b, y_b, w_b, ids_b)
            fetches = self.train_graph.output + [
                train_op, self.train_graph.loss
            ]
            fetched_values = sess.run(fetches, feed_dict=feed_dict)
            output = fetched_values[:len(self.train_graph.output)]
            loss = fetched_values[-1]
            avg_loss += loss
            y_pred = np.squeeze(np.array(output))
            y_b = y_b.flatten()
            n_batches += 1
          if epoch % checkpoint_interval == checkpoint_interval - 1:
            saver.save(sess, self._save_path, global_step=epoch)
          avg_loss = float(avg_loss) / n_batches
          log("Ending epoch %d: Average loss %g" % (epoch, avg_loss),
              self.verbose)
        // Always save a final checkpoint when complete.
        saver.save(sess, self._save_path, global_step=epoch + 1)
    //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// TIMING
    time2 = time.time()
    print("TIMING: model fitting took %0.3f s" % (time2 - time1), self.verbose)

After Change

        // Main training loop.
        try:
          epoch = 0
          index = 0
          index_in_epoch = 0
          avg_loss = 0.0
          while True:
            if index_in_epoch % log_every_N_batches == 0:
              log("On batch %d" % index_in_epoch, self.verbose)
            // Run training op.
            fetches = self.train_graph.output + [
                train_op, self.train_graph.loss
            ]
            fetched_values = sess.run(fetches)
            loss = fetched_values[-1]
            avg_loss += loss
            index += 1
            index_in_epoch += 1
            if len(epoch_end_indices) > 0 and index >= epoch_end_indices[0]:
              // We have reached the end of an epoch.
              if epoch % checkpoint_interval == checkpoint_interval - 1:
                saver.save(sess, self._save_path, global_step=epoch)
              avg_loss = float(avg_loss) / index_in_epoch
              log("Ending epoch %d: Average loss %g" % (epoch, avg_loss),
                  self.verbose)
              index_in_epoch = 0
              avg_loss = 0.0
              del epoch_end_indices[0]
        except tf.errors.OutOfRangeError:
          // We have reached the end of the data.
          pass
        // Always save a final checkpoint when complete.

Instances