[theano-users] ValueError: dimension mismatch in args to gemv (200,0)x(250)->(0)

Frédéric Bastien

2017-07-04 12:27:20 UTC

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

Post by å¨æ³½å½ª
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)
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.
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 donât know how to solve it .
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