eval.py 文件源码

def evaluate():
    """Eval CNN for a number of steps."""
    with tf.Graph().as_default() as g, tf.device("/cpu:0"):
        # Get sequences and labels
        sequences, labels = model.inputs_eval()

        # Build a Graph that computes the logits predictions from the
        # inference model.
        logits = model.inference(sequences)

        # Calculate predictions.
        top_k_op = tf.nn.in_top_k(logits, labels, 1)

        # # Restore the moving average version of the learned variables for eval.
        # variable_averages = tf.train.ExponentialMovingAverage(
        #     model.MOVING_AVERAGE_DECAY)
        # variables_to_restore = variable_averages.variables_to_restore()
        # saver = tf.train.Saver(variables_to_restore)
        saver = tf.train.Saver(tf.all_variables())

        # Build the summary operation based on the TF collection of Summaries.
        summary_op = tf.merge_all_summaries()

        summary_writer = tf.train.SummaryWriter(EVAL_DIR, g)

        while True:
            eval_once(saver, summary_writer, top_k_op, summary_op)
            if FLAGS.run_once:
                print("eval only once, stope eval")
                break
            print("sleep for {} seconds".format(FLAGS.eval_interval_secs))
            time.sleep(FLAGS.eval_interval_secs)