tensorflow学习——批量读取数据

import tensorflow as tfimport numpy as npdef generate_data():    num = 25    label = np.asarray(range(0, num))    images = np.random.random([num, 5, 5, 3])    print('label size :{}, image size {}'.format(label.shape, images.shape))    return label, imagesdef get_batch_data():    label, images = generate_data()    images = tf.cast(images, tf.float32)    label = tf.cast(label, tf.int32)    input_queue = tf.train.slice_input_producer([images, label], shuffle=False)    image_batch, label_batch = tf.train.batch(input_queue, batch_size=10, num_threads=1, capacity=64)    return image_batch, label_batchimage_batch, label_batch = get_batch_data()with tf.Session() as sess:    coord = tf.train.Coordinator()    threads = tf.train.start_queue_runners(sess, coord)    i = 0    try:        while not coord.should_stop():            image_batch_v, label_batch_v = sess.run([image_batch, label_batch])            i += 1            for j in range(10):                print(image_batch_v.shape, label_batch_v[j])    except tf.errors.OutOfRangeError:        print("done")    finally:        coord.request_stop()    coord.join(threads)