如何解决tf.train.batch只生成一个batch的数据的问题

[CZlin.]

使用tensorflow训练网上下载好的flowers数据集的时候出现了问题，按照下面代码操作后用sess.run()获取batch数据并打印，只显示一个batch（10个）的数据，求问大神们如何解决

keys_to_features = {
    'image/encoded': tf.FixedLenFeature([], default_value='', dtype=tf.string, ),
    'image/format': tf.FixedLenFeature([], default_value='jpeg', dtype=tf.string),
    'image/class/label': tf.FixedLenFeature([], tf.int64, default_value=0),
    'image/height': tf.FixedLenFeature([], tf.int64, default_value=0),
    'image/width': tf.FixedLenFeature([], tf.int64, default_value=0)
}

items_to_handlers = {
    'image': slim.tfexample_decoder.Image(image_key='image/encoded', format_key='image/format', channels=3),
    'label': slim.tfexample_decoder.Tensor('image/class/label', shape=[]),
    'height': slim.tfexample_decoder.Tensor('image/height', shape=[]),
    'width': slim.tfexample_decoder.Tensor('image/width', shape=[])
}
decoder = slim.tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers)

labels_to_names = None
items_to_descriptions = {
    'image': 'An image with shape image_shape.',
    'label': 'A single integer between 0 and 9.',
    'height': 'float number',
    'width': 'float number'}

dataset = slim.dataset.Dataset(
    data_sources=tfrecord_path,
    reader=tf.TFRecordReader,
    decoder=decoder,
    num_samples=1000,
    items_to_descriptions=None,
    num_classes=num_classes,
)

provider = slim.dataset_data_provider.DatasetDataProvider(dataset=dataset,
                                                          num_readers=4,
                                                          shuffle=False, # 这个改成False以后每次生成的batch都一样了
                                                          common_queue_capacity=256,
                                                          common_queue_min=128,
                                                          seed=None)

[image, label, height, width] = provider.get(['image', 'label', 'height', 'width'])

resized_image = tf.squeeze(tf.image.resize_bilinear([image], size=[resize_height, resize_width]))

images, labels = tf.train.batch([resized_image, label], batch_size=bsize, allow_smaller_final_batch=True, num_threads=1, capacity=5*bsize)