I want to use feedable
iterator design in tensorflow Dataset API, so I can switch to validation data after some training steps. But if I switched to validation data, it will end the whole session.
The following code demonstrate what I want to do:
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)
The training data has 32 elements, batched with 4, so got 8 batches we do validation every 4 steps, so I expect:
# 1 [training]
# 2 [training]
# 3 [training]
# 4 [training]
# 1 [validation]
# 2 [validation]
# 5 [training]
# 6 [training]
# 7 [training]
# 8 [training]
# 1 [validation]
# 2 [validation]
but it stops when the first validation is done:
# 1 [training]
# 2 [training]
# 3 [training]
# 4 [training]
# 1 [validation]
# 2 [validation]
So, how to use this feedable
iterator in tf.MonitoredTrainingSession
?