开始再培训问题"Nan在总结柱状图中: HistogramSummary“

Question

我正试着在我的RPi3上重新训练inceptionV3。我收到了这个直方图错误消息。

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)]]

我试着在读完this后在retrain.py中更改this，但它不起作用。

此外，当我第一次尝试重新训练时，在遇到错误之前，我得到了不同的步骤0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%

Answer 1

听起来，知道NaN的值来自哪里可能会有所帮助。为此，请看一下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，当训练和评估的sess.run()发生时，您将进入调试器的命令行界面。在tfdbg>提示符下，可以输入命令让代码运行，直到TensorFlow图形中出现任何NaNs或无穷大：

tfdbg> run -f has_inf_or_nan

当点击张量过滤器has_inf_or_nan时，界面将给出一个包含Infs或Nans的张量列表，并按时间顺序排序。上面的应该是“罪魁祸首”，也就是最先产生错误数值的那个。假设其名称为node_1，您可以使用以下tfdbg命令来查看其输入和节点属性：

tfdbg> li -r node_1
tfdbg> ni -a node_1

Answer 2

如果您使用的是tf.contrib.learn，您将需要使用以下内容：

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)