我想在张量流数据集API中使用feedable
迭代器设计,因此我可以在一些训练步骤之后切换到验证数据。但如果我切换到验证数据,它将结束整个会话。
以下代码演示了我想要做的事情:
import tensorflow as tf
graph = tf.Graph()
with graph.as_default():
training_ds = tf.data.Dataset.range(32).batch(4)
validation_ds = tf.data.Dataset.range(8).batch(4)
handle = tf.placeholder(tf.string, shape=[])
iterator = tf.data.Iterator.from_string_handle(
handle, training_ds.output_types, training_ds.output_shapes)
next_element = iterator.get_next()
training_iterator = training_ds.make_initializable_iterator()
validation_iterator = validation_ds.make_initializable_iterator()
with graph.as_default():
with tf.train.MonitoredTrainingSession() as sess:
training_handle = sess.run(training_iterator.string_handle())
validation_handle = sess.run(validation_iterator.string_handle())
sess.run(training_iterator.initializer)
count_training = 0
while not sess.should_stop():
x = sess.run(next_element, feed_dict={handle: training_handle})
count_training += 1
print('{} [training] {}'.format(count_training, x.shape))
# print(x)
# we do periodic validation
if count_training % 4 == 0:
sess.run(validation_iterator.initializer)
count_validation = 0
while not sess.should_stop():
y = sess.run(next_element, feed_dict={handle: validation_handle})
count_validation += 1
print(' {} [validation] {}'.format(count_validation, y.shape))
# print(y)
训练数据有32个元素,批量为4,因此有8个批次 我们每4个步骤进行一次验证,所以我希望:
# 1 [training]
# 2 [training]
# 3 [training]
# 4 [training]
# 1 [validation]
# 2 [validation]
# 5 [training]
# 6 [training]
# 7 [training]
# 8 [training]
# 1 [validation]
# 2 [validation]
但在第一次验证完成后它会停止:
# 1 [training]
# 2 [training]
# 3 [training]
# 4 [training]
# 1 [validation]
# 2 [validation]
那么,如何在feedable
中使用这个tf.MonitoredTrainingSession
迭代器?
答案 0 :(得分:4)
我建议在验证数据集的末尾捕获tf.errors.OutOfRangeError
(您还可以使用repeat
数据集检查官方API中的the processing multiple epochs section以获取另一个解决方案:
while not sess.should_stop():
x = sess.run(next_element, feed_dict={handle: training_handle})
count_training += 1
print('{} [training] {}'.format(count_training, x.shape))
# we do periodic validation
if count_training % 4 == 0:
sess.run(validation_iterator.initializer)
count_validation = 0
while True:
try:
y = sess.run(next_element, feed_dict={handle: validation_handle})
count_validation += 1
print(' {} [validation] {}'.format(count_validation, y.shape))
except tf.errors.OutOfRangeError:
break
这段代码打印出来:
1 [training] (4,)
2 [training] (4,)
3 [training] (4,)
4 [training] (4,)
1 [validation] (4,)
2 [validation] (4,)
5 [training] (4,)
6 [training] (4,)
7 [training] (4,)
8 [training] (4,)
1 [validation] (4,)
2 [validation] (4,)