我正在尝试在我的RPi3上重新训练inceptionV3。我收到此直方图错误消息。
python /home/pi/Tensorflow/tensorflow/tensorflow/examples/image_retraining/retrain.py --bottleneck_dir=/home/pi/Documents/Machine Learning/Inception/tf_files/bottlenecks --how_many_training_steps 500 --model_dir=/home/pi/Documents/Machine Learning/Inception/tf_files/inception --output_graph=/home/pi/Documents/Machine Learning/Inception/tf_files/retrained_graph.pb --output_labels=/home/pi/Documents/Machine Learning/Inception/tf_files/retrained_labels.txt --image_dir /home/pi/Documents/Machine Learning/Inception/Retraining_Images
Looking for images in 'Granny Smith Apple'
Looking for images in 'Red Delicious'
100 bottleneck files created.
200 bottleneck files created.
2017-01-07 11:30:22.180768: Step 0: Train accuracy = 56.0%
2017-01-07 11:30:22.242166: Step 0: Cross entropy = nan
2017-01-07 11:30:22.850969: Step 0: Validation accuracy = 50.0%
Traceback (most recent call last):
File "/home/pi/Tensorflow/tensorflow/tensorflow/examples/image_retraining/retrain.py", line 938, in <module>
tf.app.run()
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 30, in run
sys.exit(main(sys.argv[:1] + flags_passthrough))
File "/home/pi/Tensorflow/tensorflow/tensorflow/examples/image_retraining/retrain.py", line 887, in main
ground_truth_input: train_ground_truth})
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 717, in run
run_metadata_ptr)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 915, in _run
feed_dict_string, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 965, in _do_run
target_list, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 985, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors.InvalidArgumentError: Nan in summary histogram for: HistogramSummary
[[Node: HistogramSummary = HistogramSummary[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](HistogramSummary/tag, final_result)]]
Caused by op u'HistogramSummary', defined at:
File "/home/pi/Tensorflow/tensorflow/tensorflow/examples/image_retraining/retrain.py", line 938, in <module>
tf.app.run()
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 30, in run
sys.exit(main(sys.argv[:1] + flags_passthrough))
File "/home/pi/Tensorflow/tensorflow/tensorflow/examples/image_retraining/retrain.py", line 846, in main
bottleneck_tensor)
File "/home/pi/Tensorflow/tensorflow/tensorflow/examples/image_retraining/retrain.py", line 764, in add_final_training_ops
tf.histogram_summary(final_tensor_name + '/activations', final_tensor)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/logging_ops.py", line 100, in histogram_summary
tag=tag, values=values, name=scope)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_logging_ops.py", line 100, in _histogram_summary
name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 749, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2380, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1298, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): Nan in summary histogram for: HistogramSummary
[[Node: HistogramSummary = HistogramSummary[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](HistogramSummary/tag, final_result)]]
阅读本文后，我尝试在retrain.py中更改merged = tf.merge_all_summaries()但它没有用。
此外，我第一次尝试重新训练时，在遇到错误之前，我在步骤 0 中得到了不同的结果：
2017-01-07 11:13:36.548913: Step 0: Train accuracy = 89.0%
2017-01-07 11:13:36.555770: Step 0: Cross entropy = 0.590778
2017-01-07 11:13:37.052190: Step 0: Validation accuracy = 76.0%
听起来知道
NaN 值的来源可能会有所帮助。为此，请查看tensorflow debugger（tfdbg）：https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/how_tos/debugger/index.md
在您的 retrain.py 中，您可以进行如下更改
from tensorflow.python import debug as tf_debug
# ...
# In def main(_)
if debug:
sess = tf_debug.LocalCLIDebugWrapperSession(sess)
sess.add_tensor_filter("has_inf_or_nan", tf_debug.has_inf_or_nan)
# ...
然后，当训练和评估发生sess.run()时，您将进入调试器的命令行界面。在tfdbg>提示符下，您可以输入命令让代码运行，直到 TensorFlow 图中出现任何 NaN 或无穷大：
tfdbg> run -f has_inf_or_nan
当张量过滤器has_inf_or_nan被击中时，接口将为您提供包含 Infs 或 Nans 的张量列表，按时间顺序排序。顶部的那个应该是"罪魁祸首"，即首先生成错误数值的那个。假设它的名字是 node_1 ，您可以使用以下 tfdbg 命令查看其输入和节点属性：
tfdbg> li -r node_1
tfdbg> ni -a node_1
如果您使用的是 tf.contrib.learn，则需要使用以下方法：
debug_hook = tf_debug.LocalCLIDebugHook()
debug_hook.add_tensor_filter("has_inf_or_nan", tf_debug.has_inf_or_nan)
hooks = [debug_hook]
...
classifier.fit(..., monitors=hooks)