deepSpeech_test.py 文件源码

def evaluate():
    """ Evaluate deepSpeech modelfor a number of steps."""

    with tf.Graph().as_default() as graph:

        # Get feats and labels for deepSpeech.
        feats, labels, seq_lens = deepSpeech.inputs(ARGS.eval_data,
                                                    data_dir=ARGS.data_dir,
                                                    batch_size=ARGS.batch_size,
                                                    use_fp16=ARGS.use_fp16,
                                                    shuffle=True)

        # Build ops that computes the logits predictions from the
        # inference model.
        ARGS.keep_prob = 1.0  # Disable dropout during testing.
        logits = deepSpeech.inference(feats, seq_lens, ARGS)

        # Calculate predictions.
        output_log_prob = tf.nn.log_softmax(logits)
        decoder = tf.nn.ctc_greedy_decoder
        strided_seq_lens = tf.div(seq_lens, ARGS.temporal_stride)
        predictions = decoder(output_log_prob, strided_seq_lens)

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

        # Build the summary operation based on the TF collection of Summaries.
        summary_op = tf.merge_all_summaries()
        summary_writer = tf.train.SummaryWriter(ARGS.eval_dir, graph)

        while True:
            eval_once(saver, summary_writer, predictions, summary_op, labels)

            if ARGS.run_once:
                break
            time.sleep(ARGS.eval_interval_secs)