python之学习率大于 0.001 会导致错误

我尝试将 Udacity 深度学习类(class)(作业 3 - 正则化)和 Tensorflow mnist_with_summaries.py 教程中的代码整合在一起。我的代码似乎运行良好

https://github.com/llevar/udacity_deep_learning/blob/master/multi-layer-net.py

但有些奇怪的事情正在发生。这些作业都使用 0.5 的学习率，并在某些时候引入指数衰减。但是，只有当我将学习率设置为 0.001(有或没有衰减)时，我放在一起的代码才能正常运行。如果我将初始速率设置为 0.1 或更高，我会收到以下错误:

Traceback (most recent call last): 
  File "/Users/siakhnin/Documents/workspace/udacity_deep_learning/multi-layer-net.py", line 175, in <module> 
    summary, my_accuracy, _ = my_session.run([merged, accuracy, train_step], feed_dict=feed_dict) 
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 340, in run 
    run_metadata_ptr) 
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 564, in _run 
    feed_dict_string, options, run_metadata) 
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 637, in _do_run 
    target_list, options, run_metadata) 
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 659, in _do_call 
    e.code) 
tensorflow.python.framework.errors.InvalidArgumentError: Nan in summary histogram for: layer1/weights/summaries/HistogramSummary 
     [[Node: layer1/weights/summaries/HistogramSummary = HistogramSummary[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](layer1/weights/summaries/HistogramSummary/tag, layer1/weights/Variable/read)]] 
Caused by op u'layer1/weights/summaries/HistogramSummary', defined at: 
  File "/Users/siakhnin/Documents/workspace/udacity_deep_learning/multi-layer-net.py", line 106, in <module> 
    layer1, weights_1 = nn_layer(x, num_features, 1024, 'layer1') 
  File "/Users/siakhnin/Documents/workspace/udacity_deep_learning/multi-layer-net.py", line 79, in nn_layer 
    variable_summaries(weights, layer_name + '/weights') 
  File "/Users/siakhnin/Documents/workspace/udacity_deep_learning/multi-layer-net.py", line 65, in variable_summaries 
    tf.histogram_summary(name, var) 
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/logging_ops.py", line 113, in histogram_summary 
    tag=tag, values=values, name=scope) 
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/gen_logging_ops.py", line 55, in _histogram_summary 
    name=name) 
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/op_def_library.py", line 655, in apply_op 
    op_def=op_def) 
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2154, in create_op 
    original_op=self._default_original_op, op_def=op_def) 
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1154, in __init__ 
    self._traceback = _extract_stack() 

如果我将速率设置为 0.001，则代码运行完成，测试精度为 0.94。

在 Mac OS X 上使用 tensorflow 0.8 RC0。

请您参考如下方法:

看起来您的训练发散(这会导致您得到无穷大或 NaN)。对于为什么事物在某些条件下会发散而在其他情况下不会发散，没有简单的解释，但通常较高的学习率会使其更容易发散。

编辑，4 月 17 日 您在 Histogram 摘要中得到一个 NaN，这很可能意味着您的权重或激活中有一个 NaN。 NaN 是由不正确的数值计算引起的，即取 0 的对数并将结果乘以 0。直方图中也有可能存在一些错误，要排除这种情况，请关闭汇总，看看是否你仍然能够训练到很好的准确性。

要关闭摘要，请替换此行 合并 = tf.merge_all_summaries()

有了这个

merged = tf.constant(1) 

并注释掉这一行

test_writer.add_summary(summary)