58a8f6ea64e30ebfc369153fe2e2591f603f369f,tf_agents/agents/ddpg/examples/train_eval_rnn_dm.py,,train_eval,#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,65

Before Change

    for train_metric in train_metrics:
      train_metric.tf_summaries(step_metrics=train_metrics[:2])
    summary_op = tf.contrib.summary.all_summary_ops()

    with eval_summary_writer.as_default(), \
         tf.compat.v2.summary.record_if(True):
      for eval_metric in eval_metrics:
        eval_metric.tf_summaries()

    init_agent_op = tf_agent.initialize()

    with tf.compat.v1.Session() as sess:
      // Initialize the graph.
      train_checkpointer.initialize_or_restore(sess)
      rb_checkpointer.initialize_or_restore(sess)
      sess.run(iterator.initializer)
      // TODO(sguada) Remove once Periodically can be saved.
      common_utils.initialize_uninitialized_variables(sess)

      sess.run(init_agent_op)
      tf.contrib.summary.initialize(session=sess)
      sess.run(initial_collect_op)

      global_step_val = sess.run(global_step)
      metric_utils.compute_summaries(
          eval_metrics,
          eval_py_env,
          eval_py_policy,
          num_episodes=num_eval_episodes,
          global_step=global_step_val,
          callback=eval_metrics_callback,
          log=True,
      )

      collect_call = sess.make_callable(collect_op)
      train_step_call = sess.make_callable([train_op, summary_op, global_step])

      timed_at_step = sess.run(global_step)
      time_acc = 0

After Change

        for _ in range(train_steps_per_iteration):
          loss_info_value = train_step_call()
        time_acc += time.time() - start_time
        global_step_val = global_step_call()

        if global_step_val % log_interval == 0:
          logging.info("step = %d, loss = %f", global_step_val,
                       loss_info_value.loss)

Instances