How to use evaluation_loop with train_loop in tf-slim

Question

I\'m trying to implement a few different models and train them on CIFAR-10, and I want to use TF-slim to do this. It looks like TF-slim has two main loops that are useful during

Answer 1

Adding my 2-cent:

I currently have this model for the evaluation_loop hogging up an entire GPU, but it's rarely being used

Usually an evaluation model takes less GPU memory. You could prevent TF from hogging the whole GPU memory by setting the session config allow_growth to True. This way you can use the same GPU for both training and evaluation

Example @ Training

session_config = tf.ConfigProto()
session_config.gpu_options.allow_growth = True

slim.learning.train(train_tensor, 
                  logdir=train_log_dir,
                  local_init_op=tf.initialize_local_variables(),
                  save_summaries_secs=FLAGS.save_summaries_secs,
                  save_interval_secs=FLAGS.save_interval_secs,
                  session_config=session_config)

Example @ validation

session_config = tf.ConfigProto()
session_config.gpu_options.allow_growth = True

slim.evaluation.evaluation_loop(
      '',
      checkpoint_dir=train_log_dir,
      logdir=train_log_dir,
      num_evals=FLAGS.num_eval_batches,
      eval_op=names_to_updates.values(),
      summary_op=tf.merge_summary(summary_ops),
      eval_interval_secs=FLAGS.eval_interval_secs,
      session_config=session_config)