问题描述
例如，在创建操作，通过该操作馈送批处理数据并运行该操作之后，tf.train.batch是否会自动将另一批数据馈送给会话?
For instance, after I have created my operations, fed the batch data through the operation and run the operation, does tf.train.batch automatically feed in another batch of data to the session?
我之所以这样问，是因为tf.train.batch具有属性allow_smaller_final_batch，这使得最终批次的装载尺寸小于指示的批次尺寸成为可能.这是否意味着即使没有循环，也可以自动喂入下一批?从教程代码中我很困惑.当我加载一个批次时，实际上得到的是形状为[batch_size，高度，宽度，num_channels]的单个批次大小，但是文档表示Creates batches of tensors in tensors.而且，当我阅读 tf-slim演练教程，其中有一个名为load_batch的函数，仅返回3个张量:images, images_raw, labels.如文档中所述，数据的批次"在哪里?
I ask this because tf.train.batch has an attribute of allow_smaller_final_batch which makes it possible for the final batch to be loaded as a size lesser than the indicated batch size. Does this mean even without a loop, the next batch could be automatically fed? From the tutorial codes I am rather confused. When I load a single batch, I get literally a single batch size of shape [batch_size, height, width, num_channels], but the documentation says it Creates batches of tensors in tensors. Also, when I read the tutorial code in the tf-slim walkthrough tutorial, where there is a function called load_batch, there are only 3 tensors returned: images, images_raw, labels. Where are 'batches' of data as explained in the documentation?
谢谢您的帮助.
推荐答案
不.什么也不会自动发生.您必须再次调用sess.run(...)才能加载新批次.
No. Nothing happens automatically. You must call sess.run(...) again to load a new batch.
不. tf.train.batch(..)将始终加载batch_size张量.例如，如果有100张图像和batch_size=30，则将有3 * 30批次，因为您可以在输入队列从头开始之前(如果是epoch=1则停止)调用三遍sess.run(batch).这意味着您会错过训练中的100-3*30=10样本.如果您不想错过它们，可以执行tf.train.batch(..., allow_smaller_final_batch=True)，这样在重新开始输入队列之前，您将拥有3个30样本批和1个10样本批.
No. tf.train.batch(..) will always load batch_size tensors. If you have for example 100 images and a batch_size=30 then you will have 3*30 batches as in you can call sess.run(batch) three times before the input queue will start from the beginning (or stop if epoch=1). This means that you miss out 100-3*30=10 samples from training. In case you do not want to miss them you can do tf.train.batch(..., allow_smaller_final_batch=True) so now you will have 3x 30-sample-batches and 1x 10-sample-batch before the input queue will restart.
让我也详细说明一个代码示例:
Let me also elaborate with a code sample:
queue = tf.train.string_input_producer(filenames,
num_epochs=1) # only iterate through all samples in dataset once
reader = tf.TFRecordReader() # or any reader you need
_, example = reader.read(queue)
image, label = your_conversion_fn(example)
# batch will now load up to 100 image-label-pairs on sess.run(...)
# most tf ops are tuned to work on batches
# this is faster and also gives better result on e.g. gradient calculation
batch = tf.train.batch([image, label], batch_size=100)
with tf.Session() as sess:
# "boilerplate" code
sess.run([
tf.local_variables_initializer(),
tf.global_variables_initializer(),
])
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
# in most cases coord.should_stop() will return True
# when there are no more samples to read
# if num_epochs=0 then it will run for ever
while not coord.should_stop():
# will start reading, working data from input queue
# and "fetch" the results of the computation graph
# into raw_images and raw_labels
raw_images, raw_labels = sess.run([images, labels])
finally:
coord.request_stop()
coord.join(threads)
这篇关于TensorFlow:当批次完成训练后，tf.train.batch是否会自动加载下一个批次?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！