Question

我对tf.train.string_input_producer的工作原理有所怀疑。因此，假设我将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号，随机播放文件名等。因此，就我而言，我有4个文件名（＆＃34; db1.tfrecords＆＃34;，＆＃34; db2.tfrecords＆＃34; ...）。我使用tf.train.batch来提供网络批量图片。另外，每个file_name / database包含一个人的一组图像。第二个数据库是第二个人，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，当试图在自动编码器的输出处查看重建图像时，我从第一个数据库中获得了第一个图像，然后我开始从第二个数据库查看图像，依此类推。

我的问题：我怎么知道我是否在同一个时代？如果我在理智的时代内，如何合并我所有文件名中的一批图像呢？

最后，我尝试通过评估Session中的局部变量来打印出纪元的价值，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.

非常感谢任何帮助!!

Answer 1

所以我想到的是，使用tf.train.shuffle_batch_join解决了我的问题，因为它开始改变来自不同数据集的图像。换句话说，每个批处理现在都包含来自所有数据集/ file_names的图像。这是一个例子：

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)

现在这将创建一些读者从FIFOQueue读取，并在每个读者后将有一个不同的解码器。最后，在解码图像之后，它们将被馈送到调用Queue之后创建的另一个tf.train.shuffle_batch_join以向网络提供一批图像。

使用张量流中的tf.train.string_input_producer确定Epoch编号

1 个答案: