a ticket建议使用可馈送迭代器在训练和验证数据集之间切换,而无需重新初始化迭代器。它主要需要喂食手柄以便在它们之间进行选择。
如何与tf.train.MonitoredTrainingSession一起使用?
以下方法失败并显示“RuntimeError:Graph已完成且无法修改”。错误。
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如何实现MonitoredTrainingSession的便利性以及同时迭代训练和验证数据集?
答案 0 :(得分:5)
我从Tensorflow GitHub问题得到答案 - https://github.com/tensorflow/tensorflow/issues/12859
解决方案是在创建iterator.string_handle()
之前调用MonitoredSession
。
import tensorflow as tf
from tensorflow.contrib.data import Dataset, Iterator
dataset_train = Dataset.range(10)
dataset_val = Dataset.range(90, 100)
iter_train_handle = dataset_train.make_one_shot_iterator().string_handle()
iter_val_handle = dataset_val.make_one_shot_iterator().string_handle()
handle = tf.placeholder(tf.string, shape=[])
iterator = Iterator.from_string_handle(
handle, dataset_train.output_types, dataset_train.output_shapes)
next_batch = iterator.get_next()
with tf.train.MonitoredTrainingSession() as sess:
handle_train, handle_val = sess.run([iter_train_handle, iter_val_handle])
for step in range(10):
print('train', sess.run(next_batch, feed_dict={handle: handle_train}))
if step % 3 == 0:
print('val', sess.run(next_batch, feed_dict={handle: handle_val}))
Output:
('train', 0)
('val', 90)
('train', 1)
('train', 2)
('val', 91)
('train', 3)
答案 1 :(得分:1)
import tensorflow as tf
dataset_train = tf.data.Dataset.range(10)
dataset_val = tf.data.Dataset.range(90, 100)
iter_train_handle = dataset_train.make_one_shot_iterator().string_handle()
iter_val_handle = dataset_val.make_one_shot_iterator().string_handle()
handle = tf.placeholder(tf.string, shape=[])
iterator = tf.data.Iterator.from_string_handle(
handle, dataset_train.output_types, dataset_train.output_shapes)
feature = iterator.get_next()
pred = feature * feature
tf.summary.scalar('pred', pred)
global_step = tf.train.create_global_step()
summary_hook = tf.train.SummarySaverHook(save_steps=5,
output_dir="summaries", summary_op=tf.summary.merge_all())
with tf.train.MonitoredTrainingSession(hooks=[summary_hook]) as sess:
handle_train, handle_val = sess.run([iter_train_handle, iter_val_handle])
for step in range(10):
feat = sess.run(feature, feed_dict={handle: handle_train})
pred_ = sess.run(pred, feed_dict={handle: handle_train})
print('train: ', feat)
print('pred: ', pred_)
if step % 3 == 0:
print('val', sess.run(feature, feed_dict={handle: handle_val}))
这将失败并显示错误:
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder' with dtype string
[[Node: Placeholder = Placeholder[dtype=DT_STRING, shape=[], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
[[Node: cond/Switch_1/_15 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device_incarnation=1, tensor_name="edge_18_cond/Switch_1", tensor_type=DT_INT64, _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]
原因是钩子会尝试在第一个session.run([iter_train_handle,iter_val_handle])上评估图形,它显然还没有在feed_dict中包含句柄。
解决方法是覆盖导致问题的钩子并更改before_run和after_run中的代码,以仅评估包含feed_dict中句柄的session.run调用(您可以访问当前session.run调用的feed_dict)通过before_run和after_run的run_context参数
或者您可以使用Tensorflow的最新版本(1.4之后),它将一个run_step_fn函数添加到MonitoredSession,它允许您指定以下step_fn,这将避免错误(以评估if语句TrainingIteration次数为代价) ...)
def step_fn(step_context):
if handle_train is None:
handle_train, handle_val = sess.run([iter_train_handle, iter_val_handle])
return step_context.run_with_hooks(fetches=..., feed_dict=...)
答案 2 :(得分:1)
有一个使用SessionRunHook在mot_session中使用占位符的演示。 这个演示是关于通过提供diff handle_string来切换数据集。
顺便说一下,我已经尝试了所有解决方案,但只有这样才有效。