我对tf.train.string_input_producer的工作方式有些怀疑。所以假设我filename_list作为输入参数提供给string_input_producer.然后，根据文档 https://www.tensorflow.org/programmers_guide/reading_data，这将创建一个FIFOQueue，我可以在其中设置纪元编号，洗牌文件名等。因此，就我而言，我有 4 个文件名("db1.tfrecords"、"db2.tfrecords"...(。我用tf.train.batch来馈送网络一批图像。此外，每个file_name/数据库都包含一个人的一组图像。第二个数据库供第二个人使用，依此类推。到目前为止，我有以下代码：
tfrecords_filename_seq = [(common + "P16_db.tfrecords"), (common + "P17_db.tfrecords"), (common + "P19_db.tfrecords"),
(common + "P21_db.tfrecords")]
filename_queue = tf.train.string_input_producer(tfrecords_filename_seq, num_epochs=num_epoch, shuffle=False, name='queue')
reader = tf.TFRecordReader()
key, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(
serialized_example,
# Defaults are not specified since both keys are required.
features={
'height': tf.FixedLenFeature([], tf.int64),
'width': tf.FixedLenFeature([], tf.int64),
'image_raw': tf.FixedLenFeature([], tf.string),
'annotation_raw': tf.FixedLenFeature([], tf.string)
})
image = tf.decode_raw(features['image_raw'], tf.uint8)
height = tf.cast(features['height'], tf.int32)
width = tf.cast(features['width'], tf.int32)
image = tf.reshape(image, [height, width, 3])
annotation = tf.cast(features['annotation_raw'], tf.string)
min_after_dequeue = 100
num_threads = 4
capacity = min_after_dequeue + num_threads * batch_size
label_batch, images_batch = tf.train.batch([annotation, image],
shapes=[[], [112, 112, 3]],
batch_size=batch_size,
capacity=capacity,
num_threads=num_threads)
最后，当尝试在自动编码器的输出端查看重建的图像时，我从第一个数据库中获取了第一个图像，然后我开始查看来自第二个数据库的图像，依此类推。
我的问题：我怎么知道我是否在同一时代？如果我处于理智时代，我如何合并我拥有的所有file_names中的一批图像？
最后，我尝试通过计算Session内的局部变量来打印出纪元的值，如下所示：
epoch_var = tf.local_variables()[0]
然后：
with tf.Session() as sess:
print(sess.run(epoch_var.eval())) # Here I got 9 as output. don't know y.
任何帮助都非常感谢！！
所以我发现使用tf.train.shuffle_batch_join解决了我的问题，因为它开始从不同的数据集中随机播放图像。换句话说，每个批次现在都包含来自所有数据集/file_names的图像。下面是一个示例：
def read_my_file_format(filename_queue):
reader = tf.TFRecordReader()
key, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(
serialized_example,
# Defaults are not specified since both keys are required.
features={
'height': tf.FixedLenFeature([], tf.int64),
'width': tf.FixedLenFeature([], tf.int64),
'image_raw': tf.FixedLenFeature([], tf.string),
'annotation_raw': tf.FixedLenFeature([], tf.string)
})
# This is how we create one example, that is, extract one example from the database.
image = tf.decode_raw(features['image_raw'], tf.uint8)
# The height and the weights are used to
height = tf.cast(features['height'], tf.int32)
width = tf.cast(features['width'], tf.int32)
# The image is reshaped since when stored as a binary format, it is flattened. Therefore, we need the
# height and the weight to restore the original image back.
image = tf.reshape(image, [height, width, 3])
annotation = tf.cast(features['annotation_raw'], tf.string)
return annotation, image
def input_pipeline(filenames, batch_size, num_threads, num_epochs=None):
filename_queue = tf.train.string_input_producer(filenames, num_epochs=num_epoch, shuffle=False,
name='queue')
# Therefore, Note that here we have created num_threads readers to read from the filename_queue.
example_list = [read_my_file_format(filename_queue=filename_queue) for _ in range(num_threads)]
min_after_dequeue = 100
capacity = min_after_dequeue + num_threads * batch_size
label_batch, images_batch = tf.train.shuffle_batch_join(example_list,
shapes=[[], [112, 112, 3]],
batch_size=batch_size,
capacity=capacity,
min_after_dequeue=min_after_dequeue)
return label_batch, images_batch, example_list
label_batch, images_batch, input_ann_img =
input_pipeline(tfrecords_filename_seq, batch_size, num_threads, num_epochs=num_epoch)
现在这将创建许多阅读器从FIFOQueue读取，并且每个阅读器之后将具有不同的解码器。最后，在解码图像后，它们将馈送到另一个Queue中，该在调用tf.train.shuffle_batch_join后创建，以向网络提供一批图像。