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前言
创新互联公司专注于从化网站建设服务及定制,我们拥有丰富的企业做网站经验。 热诚为您提供从化营销型网站建设,从化网站制作、从化网页设计、从化网站官网定制、成都微信小程序服务,打造从化网络公司原创品牌,更为您提供从化网站排名全网营销落地服务。在tensorflow的官方文档中得卷积神经网络一章,有一个使用cifar-10图片数据集的实验,搭建卷积神经网络倒不难,但是那个cifar10_input文件着实让我费了一番心思。配合着官方文档也算看的七七八八,但是中间还是有一些不太明白,不明白的mark一下,这次记下一些已经明白的。
研究
cifar10_input.py文件的read操作,主要的就是下面的代码:
if not eval_data: filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i) for i in xrange(1, 6)] num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN else: filenames = [os.path.join(data_dir, 'test_batch.bin')] num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_EVAL ... filename_queue = tf.train.string_input_producer(filenames) ... label_bytes = 1 # 2 for CIFAR-100 result.height = 32 result.width = 32 result.depth = 3 image_bytes = result.height * result.width * result.depth # Every record consists of a label followed by the image, with a # fixed number of bytes for each. record_bytes = label_bytes + image_bytes # Read a record, getting filenames from the filename_queue. No # header or footer in the CIFAR-10 format, so we leave header_bytes # and footer_bytes at their default of 0. reader = tf.FixedLengthRecordReader(record_bytes=record_bytes) result.key, value = reader.read(filename_queue) ... if shuffle: images, label_batch = tf.train.shuffle_batch( [image, label], batch_size=batch_size, num_threads=num_preprocess_threads, capacity=min_queue_examples + 3 * batch_size, min_after_dequeue=min_queue_examples) else: images, label_batch = tf.train.batch( [image, label], batch_size=batch_size, num_threads=num_preprocess_threads, capacity=min_queue_examples + 3 * batch_size)