HINT: Use the Theano flag exception_verbosity=high for a debugprint and storage map footprint of t

今天运行theano程序的时候，遇到了下面的问题：

GRU4Rec git:(master) ✗ python run_rsc15.py
Using cuDNN version 6021 on context None
Mapped name None to device cuda: GeForce GTX 1080 Ti (0000:03:00.0)
start training
epoch: 0
Traceback (most recent call last):
  File \"/home/eric/anaconda3/lib/python3.6/site-packages/theano/compile/function_module.py\", line 903, in __call__
    self.fn() if output_subset is None else\\
IndexError: Index out of bounds.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File \"run_rsc15.py\", line 35, in <module>
    gru.fit(data)
  File \"/home/eric/Documents/Experiments/GRU4Rec/gru4rec.py\", line 575, in fit
    cost = train_function(in_idx, y, len(iters), reset)
  File \"/home/eric/anaconda3/lib/python3.6/site-packages/theano/compile/function_module.py\", line 917, in __call__
    storage_map=getattr(self.fn, \'storage_map\', None))
  File \"/home/eric/anaconda3/lib/python3.6/site-packages/theano/gof/ .py\", line 325, in raise_with_op
    reraise(exc_type, exc_value, exc_trace)
  File \"/home/eric/anaconda3/lib/python3.6/site-packages/six.py\", line 692, in reraise
    raise value.with_traceback(tb)
  File \"/home/eric/anaconda3/lib/python3.6/site-packages/theano/compile/function_module.py\", line 903, in __call__
    self.fn() if output_subset is None else\\
IndexError: Index out of bounds.
Apply node that caused the error: GpuAdvancedSubtensor1(<GpuArrayType<None>(float32, matrix)>, GpuContiguous.0)
Toposort index: 29
Inputs types: [GpuArrayType<None>(float32, matrix), GpuArrayType<None>(int64, vector)]
Inputs shapes: [(68892, 100), (2080,)]
Inputs strides: [(400, 4), (8,)]
Inputs values: [\'not shown\', \'not shown\']
Outputs clients: [[GpuElemwise{Composite{((i0 * i1) - (i2 * (i3 / sqrt((i4 + i5 + i6)))))}}[(0, 1)]<gpuarray>(GpuArrayConstant{[[0.3]]}, GpuAdvancedSubtensor1.0, GpuArrayConstant{[[0.05]]}, GpuDot22.0, GpuArrayConstant{[[1.e-06]]}, GpuAdvancedSubtensor1.0, GpuElemwise{sqr,no_inplace}.0)]]

Backtrace when the node is created(use Theano flag traceback.limit=N to make it longer):
  File \"run_rsc15.py\", line 35, in <module>
    gru.fit(data)
  File \"/home/eric/Documents/Experiments/GRU4Rec/gru4rec.py\", line 532, in fit
    updates = self.RMSprop(cost, params, full_params, sparams, sidxs)
  File \"/home/eric/Documents/Experiments/GRU4Rec/gru4rec.py\", line 408, in RMSprop
    vs = velocity[sample_idx]
  File \"/home/eric/anaconda3/lib/python3.6/site-packages/theano/gpuarray/type.py\", line 675, in __getitem__
    return _operators.__getitem__(self, *args)
  File \"run_rsc15.py\", line 35, in <module>
    gru.fit(data)
  File \"/home/eric/Documents/Experiments/GRU4Rec/gru4rec.py\", line 532, in fit
    updates = self.RMSprop(cost, params, full_params, sparams, sidxs)
  File \"/home/eric/Documents/Experiments/GRU4Rec/gru4rec.py\", line 408, in RMSprop
    vs = velocity[sample_idx]
  File \"/home/eric/anaconda3/lib/python3.6/site-packages/theano/gpuarray/type.py\", line 675, in __getitem__
    return _operators.__getitem__(self, *args)

HINT: Use the Theano flag \'exception_verbosity=high\' for a debugprint and storage map footprint of this apply node.

我开始的theano的版本为：

Theano                             1.0.2+2.gc449c8699

然后我把版本降低到0.9之后，就运行正常了：

conda install theano=0.9

程序正常跑起来的日志：

 GRU4Rec git:(master) ✗ python run_rsc15.py
/home/eric/anaconda3/lib/python3.6/site-packages/theano/gpuarray/dnn.py:135: UserWarning: Your cuDNN version is more recent than Theano. If you encounter problems, try updating Theano or downgrading cuDNN to version 5.1.
  warnings.warn(\"Your cuDNN version is more recent than \"
Using cuDNN version 6021 on context None
Mapped name None to device cuda: GeForce GTX 1080 Ti (0000:03:00.0)
start training
epoch: 0
Epoch0	loss: 0.515304
epoch: 1
Epoch1	loss: 0.356552
epoch: 2
Epoch2	loss: 0.273438
epoch: 3
Epoch3	loss: 0.232888
epoch: 4
Epoch4	loss: 0.211045
epoch: 5
Epoch5	loss: 0.198076
epoch: 6
Epoch6	loss: 0.189473
epoch: 7
Epoch7	loss: 0.183521
epoch: 8
Epoch8	loss: 0.179129
epoch: 9
Epoch9	loss: 0.175793
epoch: 10
Epoch10	loss: 0.173115
epoch: 11
Epoch11	loss: 0.170898
epoch: 12
Epoch12	loss: 0.169162