今天執行theano程式的時候，遇到了下面的問題：

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/link.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.
 

我開始的theano的版本為：

Theano                             1.0.2+2.gc449c8699

然後我把版本降低到0.9之後，就執行正常了：

conda install theano=0.9

程式正常跑起來的日誌：

 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