You have a problem in your code. To help you identify it, u you can n follow the HINTS hidded in the long output, use the Theano flag
optimizer=fast_compile Le lun. 3 juil. 2017 10:02, 周泽彪 <[email protected]> a écrit : > Building model > Loading data > File > "/opt/zebiao.zhou/anaconda2/lib/python2.7/site-packages/theano/gof/link.py", > line 325, in raise_with_op > reraise(exc_type, exc_value, exc_trace) > reraise(exc_type, exc_value, exc_trace) > File > "/opt/zebiao.zhou/anaconda2/lib/python2.7/site-packages/theano/gof/link.py", > line 325, in raise_with_op > reraise(exc_type, exc_value, exc_trace) > Building model > Loading data > File > "/opt/zebiao.zhou/anaconda2/lib/python2.7/site-packages/theano/gof/link.py", > line 325, in raise_with_op > reraise(exc_type, exc_value, exc_trace) > reraise(exc_type, exc_value, exc_trace) > File "theano/scan_module/scan_perform.pyx", line 397, in > theano.scan_module.scan_perform.perform > (/opt/zebiao.zhou/.theano/compiledir_Linux-3.10-el7.x86_64-x86_64-with-centos-7.2.1511-Core-x86_64-2.7.13-64/scan_perform/mod.cpp:4490) > File > "/opt/zebiao.zhou/anaconda2/lib/python2.7/site-packages/theano/scan_module/scan_op.py", > line 989, in rval > r = p(n, [x[0] for x in i], o) > File > "/opt/zebiao.zhou/anaconda2/lib/python2.7/site-packages/theano/scan_module/scan_op.py", > line 978, in p > self, node) > File "theano/scan_module/scan_perform.pyx", line 405, in > theano.scan_module.scan_perform.perform > (/opt/zebiao.zhou/.theano/compiledir_Linux-3.10-el7.x86_64-x86_64-with-centos-7.2.1511-Core-x86_64-2.7.13-64/scan_perform/mod.cpp:4606) > File > "/opt/zebiao.zhou/anaconda2/lib/python2.7/site-packages/theano/gof/link.py", > line 325, in raise_with_op > reraise(exc_type, exc_value, exc_trace) > File "theano/scan_module/scan_perform.pyx", line 397, in > theano.scan_module.scan_perform.perform > (/opt/zebiao.zhou/.theano/compiledir_Linux-3.10-el7.x86_64-x86_64-with-centos-7.2.1511-Core-x86_64-2.7.13-64/scan_perform/mod.cpp:4490) > ValueError: dimension mismatch in args to gemv (200,0)x(250)->(0) > Apply node that caused the error: > GpuGemv{no_inplace}(GpuSubtensor{int32:int32:}.0, TensorConstant{1.0}, > GpuSubtensor{:int32:, int32:int32:}.0, GpuReshape{1}.0, TensorConstant{1.0}) > Toposort index: 27 > Inputs types: [CudaNdarrayType(float32, vector), TensorType(float32, > scalar), CudaNdarrayType(float32, matrix), CudaNdarrayType(float32, > vector), TensorType(float32, scalar)] > Inputs shapes: [(0,), (), (200, 0), (250,), ()] > Inputs strides: [(1,), (), (500, 1), (1,), ()] > Inputs values: [CudaNdarray([]), array(1.0, dtype=float32), > CudaNdarray([]), 'not shown', array(1.0, dtype=float32)] > Outputs clients: [[GpuElemwise{Composite{(scalar_sigmoid(i0) * i1)}}[(0, > 0)](GpuGemv{no_inplace}.0, GpuReshape{1}.0)]] > > HINT: Re-running with most Theano optimization disabled could give you a > back-trace of when this node was created. This can be done with by setting > the Theano flag 'optimizer=fast_compile'. If that does not work, Theano > optimizations can be disabled with 'optimizer= None'. > HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and > storage map footprint of this apply node. > Apply node that caused the error: > for{gpu,scan_fn}(Elemwise{Composite{minimum(minimum(i0, i1), i2)}}.0, > Elemwise{add,no_inplace}.0, Elemwise{add,no_inplace}.0, > Subtensor{:int64:}.0, Subtensor{int64:int64:int8}.0, > Subtensor{int64:int64:int8}.0, Elemwise{Composite{min imum(minimum(i0, > i1), i2)}}.0, ugW_copy[cuda], cW_copy[cuda], rgW_copy[cuda], > rgb_copy[cuda], cb_copy[cuda], ugb_copy[cuda], <CudaNdarrayType(float32, > 3D)>) > Toposort index: 24 > Inputs types: [TensorType(int64, scalar), TensorType(int32, vector), > TensorType(int32, vector), TensorType(int64, vector), TensorType(int32, > vector), TensorType(int32, vector), TensorType(int64, scalar), > CudaNdarrayType(float32, matrix), CudaNdarrayType(float32, mat rix), > CudaNdarrayType(float32, matrix), CudaNdarrayType(float32, vector), > CudaNdarrayType(float32, vector), CudaNdarrayType(float32, vector), > CudaNdarrayType(float32, 3D)] > Inputs shapes: [(), (256,), (256,), (256,), (256,), (256,), (), (250, > 700), (50, 500), (200, 500), (500,), (200,), (700,), (256, 6, 50)] > Inputs strides: [(), (4,), (4,), (8,), (24,), (4,), (), (700, 1), (500, > 1), (500, 1), (1,), (1,), (1,), (300, 50, 1)] > Inputs values: [array(256), 'not shown', 'not shown', 'not shown', 'not > shown', 'not shown', array(256), 'not shown', 'not shown', 'not shown', > 'not shown', 'not shown', 'not shown', 'not shown'] > Outputs clients: [[GpuDot22(for{gpu,scan_fn}.0, lstmW_copy[cuda]), > GpuGemv{inplace}(GpuCAReduce{add}{0,1}.0, TensorConstant{1.0}, > for{gpu,scan_fn}.0, U_copy[cuda], TensorConstant{1.0}), > GpuElemwise{Composite{(i0 * tanh((i1 + i2)))}}[(0, 1)](for{gpu,scan_fn}.0, > GpuDo t22.0, <CudaNdarrayType(float32, row)>)]] > > > > > who can help me~~i don‘t know how to solve it . > > -- > > --- > You received this message because you are subscribed to the Google Groups > "theano-users" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to [email protected]. > For more options, visit https://groups.google.com/d/optout. > -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
