I just updated to gpuarray backend, and suddenly I started seeing float64
warnings. I printed the graph (float64 shown in bold red below), then set
it up so that float64 triggers pdb. However, I still can't figure out which
variable it is. By using UP in pdb I eventually got to my training function:
C:\ProgramData\Miniconda2\lib\site-packages\theano\gof\type.py:405:
UserWarning: You are creating a TensorVariable with float64 dtype. You
requested an action via the Theano flag
warn_float64={ignore,warn,raise,pdb}.
return utils.add_tag_trace(self.make_variable(name))
C:\ProgramData\Miniconda2\lib\site-packages\theano\gof\graph.py:447:
UserWarning: You are creating a TensorVariable with float64 dtype. You
requested an action via the Theano flag
warn_float64={ignore,warn,raise,pdb}.
cp = self.__class__(self.type, None, None, self.name)
HostFromGpu(gpuarray) [id A] <TensorType(float32, scalar)> 'mean' 84
|GpuElemwise{Composite{(i0 / Cast{float32}((Composite{Switch(LT(i0, i1),
i2, i0)}(Composite{Switch(GE(i0, i1), i2, i0)}(Composite{Switch(LT(i0, i1),
i2, i0)}(Composite{Switch(LT(i0, i1), (i2 + i3), i4)}(i1, i2, i3, i4, i5),
i6, i7), i8, i9), i10, i11) - Switch(LT(Composite{Switch(LT(i0, i1), i2,
i0)}(Composite{Switch(GE(i0, i1), i2, i0)}(Composite{Switch(LT(i0, i1), i2,
i0)}(Composite{Switch(LT(i0, i1), (i2 + i3), i4)}(i12, i13, i14, i15, i16),
i17, i18), i19, i20), i21, i22), Composite{Switch(LT(i0, i1), i2,
i0)}(Composite{Switch(GE(i0, i1), i2, i0)}(Composite{Switch(LT(i0, i1), i2,
i0)}(Composite{Switch(LT(i0, i1), (i2 + i3), i4)}(i1, i2, i3, i4, i5), i6,
i7), i8, i9), i10, i11)), Composite{Switch(LT(i0, i1), i2,
i0)}(Composite{Switch(GE(i0, i1), i2, i0)}(Composite{Switch(LT(i0, i1), i2,
i0)}(Composite{Switch(LT(i0, i1), (i2 + i3), i4)}(i12, i13, i14, i15, i16),
i17, i18), i19, i20), i21, i22), Composite{Switch(LT(i0, i1), i2,
i0)}(Composite{Switch(GE(i0, i1), i2, i0)}(Composite{Switch(LT(i0, i1), i2,
i0)}(Composite{Switch(LT(i0, i1), (i2 + i3), i4)}(i1, i2, i3, i4, i5), i6,
i7), i8, i9), i10, i11)))))}}[(0, 0)]<gpuarray> [id B]
<GpuArrayType<None>(float32, ())> '' 83
|GpuCAReduceCuda{add} [id C] <GpuArrayType<None>(float32, ())> '' 82
| |GpuElemwise{Composite{EQ(Cast{int32}(i0), i1)}}[]<gpuarray> [id D]
<GpuArrayType<None>(bool, (False,))> '' 81
| |GpuSubtensor{int64:int64:} [id E] <GpuArrayType<None>(float32,
(False,))> '' 27
| | |<GpuArrayType<None>(float32, (False,))> [id F]
<GpuArrayType<None>(float32, (False,))>
| | |ScalarFromTensor [id G] <int64> '' 13
| | | |Elemwise{mul,no_inplace} [id H] <TensorType(int64, scalar)> '' 1
| | | |TensorConstant{128} [id I] <TensorType(int64, scalar)>
| | | |<TensorType(int64, scalar)> [id J] <TensorType(int64, scalar)>
| | |ScalarFromTensor [id K] <int64> '' 15
| | |Elemwise{Composite{(i0 * (i1 + i2))}} [id L] <TensorType(int64,
scalar)> '' 2
| | |TensorConstant{128} [id I] <TensorType(int64, scalar)>
| | |TensorConstant{1} [id M] <TensorType(int64, scalar)>
| | |<TensorType(int64, scalar)> [id J] <TensorType(int64, scalar)>
| |GpuFromHost<None> [id N] <GpuArrayType<None>(int64, (False,))> '' 80
| |Argmax [id O] <TensorType(int64, vector)> 'argmax' 79
| |HostFromGpu(gpuarray) [id P] <TensorType(float32, matrix)> '' 78
| | |GpuElemwise{Add}[(0, 0)]<gpuarray> [id Q] <GpuArrayType<None>(float32,
(False, False))> '' 77
| | |GpuGemm{inplace=True} [id R] <GpuArrayType<None>(float32, (False,
False))> '' 76
| | | |GpuAllocEmpty{dtype='float32', context_name=None} [id S]
<GpuArrayType<None>(float32, (False, False))> '' 75
| | | | |Subtensor{int64} [id T] <TensorType(int64, scalar)> '' 74
| | | | | |Shape [id U] <TensorType(int64, vector)> '' 73
| | | | | | |GpuReshape{2} [id V] <GpuArrayType<None>(float32, (False,
False))> '' 72
| | | | | | |GpuElemwise{Composite{(i0 * ((i1 * (i2 + i3)) + (i4 * Abs((i2
+ i3)))))}}[(0, 2)]<gpuarray> [id W] <GpuArrayType<None>(float32, (False,
False))> '' 71
| | | | | | | |GpuArrayConstant{[[ 0.5]]} [id X]
<GpuArrayType<None>(float32, (True, True))>
| | | | | | | |GpuArrayConstant{[[ 0.1]]} [id Y]
<GpuArrayType<None>(float32, (True, True))>
| | | | | | | |GpuGemm{inplace=True} [id Z] <GpuArrayType<None>(float32,
(False, False))> '' 70
| | | | | | | | |GpuAllocEmpty{dtype='float32', context_name=None} [id BA]
<GpuArrayType<None>(float32, (False, False))> '' 69
| | | | | | | | | |Subtensor{int64} [id BB] <TensorType(int64, scalar)> ''
68
| | | | | | | | | | |Shape [id BC] <TensorType(int64, vector)> '' 67
| | | | | | | | | | | |GpuReshape{2} [id BD] <GpuArrayType<None>(float32,
(False, False))> '' 66
| | | | | | | | | | | |GpuElemwise{Composite{(i0 * ((i1 * (i2 + i3)) + (i4
* Abs((i2 + i3)))))}}[(0, 2)]<gpuarray> [id BE]
<GpuArrayType<None>(float32, (False, False, False, False))> '' 65
| | | | | | | | | | | | |GpuArrayConstant{[[[[ 0.5]]]]} [id BF]
<GpuArrayType<None>(float32, (True, True, True, True))>
| | | | | | | | | | | | |GpuArrayConstant{[[[[ 0.095]]]]} [id BG]
<GpuArrayType<None>(float32, (True, True, True, True))>
| | | | | | | | | | | | |GpuDnnConv{algo='time_on_shape_change',
inplace=True} [id BH] <GpuArrayType<None>(float32, (False, False, False,
False))> '' 64
| | | | | | | | | | | | | |GpuContiguous [id BI]
<GpuArrayType<None>(float32, (False, False, False, False))> '' 51
| | | | | | | | | | | | | | |GpuReshape{4} [id BJ]
<GpuArrayType<None>(float32, (False, False, False, False))> '' 50
| | | | | | | | | | | | | | |GpuElemwise{Composite{(i0 * ((i1 * (i2 + i3))
+ (i4 * Abs((i2 + i3)))))}}[(0, 2)]<gpuarray> [id BK]
<GpuArrayType<None>(float32, (False, False, False, False))> '' 49
| | | | | | | | | | | | | | | |GpuArrayConstant{[[[[ 0.5]]]]} [id BF]
<GpuArrayType<None>(float32, (True, True, True, True))>
| | | | | | | | | | | | | | | |GpuArrayConstant{[[[[ 0.1]]]]} [id BL]
<GpuArrayType<None>(float32, (True, True, True, True))>
| | | | | | | | | | | | | | | |GpuDnnConv{algo='time_on_shape_change',
inplace=True} [id BM] <GpuArrayType<None>(float32, (False, False, False,
False))> '' 48
| | | | | | | | | | | | | | | | |GpuContiguous [id BN]
<GpuArrayType<None>(float32, (False, False, False, False))> '' 35
| | | | | | | | | | | | | | | | | |GpuReshape{4} [id BO]
<GpuArrayType<None>(float32, (False, False, False, False))> '' 32
| | | | | | | | | | | | | | | | | |GpuSubtensor{int64:int64:} [id BP]
<GpuArrayType<None>(float32, (False, False))> '' 26
| | | | | | | | | | | | | | | | | | |<GpuArrayType<None>(float32, (False,
False))> [id BQ] <GpuArrayType<None>(float32, (False, False))>
| | | | | | | | | | | | | | | | | | |ScalarFromTensor [id G] <int64> '' 13
| | | | | | | | | | | | | | | | | | |ScalarFromTensor [id K] <int64> '' 15
| | | | | | | | | | | | | | | | | |TensorConstant{[128 3 32 32]} [id BR]
<TensorType(int32, vector)>
| | | | | | | | | | | | | | | | |GpuContiguous [id BS]
<GpuArrayType<None>(float32, (False, False, False, False))> '' 10
| | | | | | | | | | | | | | | | | |<GpuArrayType<None>(float32, (False,
False, False, False))> [id BT] <GpuArrayType<None>(float32, (False, False,
False, False))>
| | | | | | | | | | | | | | | | |GpuAllocEmpty{dtype='float32',
context_name=None} [id BU] <GpuArrayType<None>(float32, (False, False,
False, False))> '' 47
| | | | | | | | | | | | | | | | | |Assert{msg='The convolution would
produce an invalid shape (dim[0] < 0).'} [id BV] <TensorType(int64,
scalar)> '' 44
| | | | | | | | | | | | | | | | | | |Shape_i{0} [id BW] <TensorType(int64,
scalar)> '' 38
| | | | | | | | | | | | | | | | | | | |GpuContiguous [id BN]
<GpuArrayType<None>(float32, (False, False, False, False))> '' 35
| | | | | | | | | | | | | | | | | | |Elemwise{ge,no_inplace} [id BX]
<TensorType(bool, scalar)> '' 41
| | | | | | | | | | | | | | | | | | |Shape_i{0} [id BW] <TensorType(int64,
scalar)> '' 38
| | | | | | | | | | | | | | | | | | |TensorConstant{0} [id BY]
<TensorType(int8, scalar)>
| | | | | | | | | | | | | | | | | |Assert{msg='The convolution would
produce an invalid shape (dim[1] < 0).'} [id BZ] <TensorType(int64,
scalar)> '' 34
| | | | | | | | | | | | | | | | | | |Shape_i{0} [id CA] <TensorType(int64,
scalar)> '' 25
| | | | | | | | | | | | | | | | | | | |GpuContiguous [id BS]
<GpuArrayType<None>(float32, (False, False, False, False))> '' 10
| | | | | | | | | | | | | | | | | | |Elemwise{ge,no_inplace} [id CB]
<TensorType(bool, scalar)> '' 31
| | | | | | | | | | | | | | | | | | |Shape_i{0} [id CA] <TensorType(int64,
scalar)> '' 25
| | | | | | | | | | | | | | | | | | |TensorConstant{0} [id CC]
<TensorType(int8, scalar)>
| | | | | | | | | | | | | | | | | |Assert{msg='The convolution would
produce an invalid shape (dim[2] <= 0).'} [id CD] <TensorType(int64,
scalar)> '' 46
| | | | | | | | | | | | | | | | | | |Elemwise{Composite{(((i0 - (((i1 - i2)
* i3) + i4)) // i5) + i6)}}[(0, 0)] [id CE] <TensorType(int64, scalar)> ''
40
| | | | | | | | | | | | | | | | | | | |Shape_i{2} [id CF]
<TensorType(int64, scalar)> '' 37
| | | | | | | | | | | | | | | | | | | | |GpuContiguous [id BN]
<GpuArrayType<None>(float32, (False, False, False, False))> '' 35
| | | | | | | | | | | | | | | | | | | |Shape_i{2} [id CG]
<TensorType(int64, scalar)> '' 24
| | | | | | | | | | | | | | | | | | | | |GpuContiguous [id BS]
<GpuArrayType<None>(float32, (False, False, False, False))> '' 10
| | | | | | | | | | | | | | | | | | | |TensorConstant{1} [id CH]
<TensorType(int8, scalar)>
| | | | | | | | | | | | | | | | | | | |TensorConstant{1} [id CI]
<TensorType(int32, scalar)>
| | | | | | | | | | | | | | | | | | | |TensorConstant{1} [id CH]
<TensorType(int8, scalar)>
| | | | | | | | | | | | | | | | | | | |TensorConstant{2} [id CJ]
<TensorType(int8, scalar)>
| | | | | | | | | | | | | | | | | | | |TensorConstant{1} [id CH]
<TensorType(int8, scalar)>
| | | | | | | | | | | | | | | | | | |Elemwise{gt,no_inplace} [id CK]
<TensorType(bool, scalar)> '' 43
| | | | | | | | | | | | | | | | | | |Elemwise{Composite{(((i0 - (((i1 - i2)
* i3) + i4)) // i5) + i6)}}[(0, 0)] [id CE] <TensorType(int64, scalar)> ''
40
| | | | | | | | | | | | | | | | | | |TensorConstant{0} [id CL]
<TensorType(int8, scalar)>
| | | | | | | | | | | | | | | | | |Assert{msg='The convolution would
produce an invalid shape (dim[3] <= 0).'} [id CM] <TensorType(int64,
scalar)> '' 45
| | | | | | | | | | | | | | | | | |Elemwise{Composite{(((i0 - (((i1 - i2) *
i3) + i4)) // i5) + i6)}}[(0, 0)] [id CN] <TensorType(int64, scalar)> '' 39
| | | | | | | | | | | | | | | | | | |Shape_i{3} [id CO] <TensorType(int64,
scalar)> '' 36
| | | | | | | | | | | | | | | | | | | |GpuContiguous [id BN]
<GpuArrayType<None>(float32, (False, False, False, False))> '' 35
| | | | | | | | | | | | | | | | | | |Shape_i{3} [id CP] <TensorType(int64,
scalar)> '' 23
| | | | | | | | | | | | | | | | | | | |GpuContiguous [id BS]
<GpuArrayType<None>(float32, (False, False, False, False))> '' 10
| | | | | | | | | | | | | | | | | | |TensorConstant{1} [id CH]
<TensorType(int8, scalar)>
| | | | | | | | | | | | | | | | | | |TensorConstant{1} [id CI]
<TensorType(int32, scalar)>
| | | | | | | | | | | | | | | | | | |TensorConstant{1} [id CH]
<TensorType(int8, scalar)>
| | | | | | | | | | | | | | | | | | |TensorConstant{2} [id CJ]
<TensorType(int8, scalar)>
| | | | | | | | | | | | | | | | | | |TensorConstant{1} [id CH]
<TensorType(int8, scalar)>
| | | | | | | | | | | | | | | | | |Elemwise{gt,no_inplace} [id CQ]
<TensorType(bool, scalar)> '' 42
| | | | | | | | | | | | | | | | | |Elemwise{Composite{(((i0 - (((i1 - i2) *
i3) + i4)) // i5) + i6)}}[(0, 0)] [id CN] <TensorType(int64, scalar)> '' 39
| | | | | | | | | | | | | | | | | |TensorConstant{0} [id CR]
<TensorType(int8, scalar)>
| | | | | | | | | | | | | | | | |GpuDnnConvDesc{border_mode='valid',
subsample=(2, 2), conv_mode='conv', precision='float32'} [id CS]
<CDataType{cudnnConvolutionDescriptor_t}> '' 30
| | | | | | | | | | | | | | | | | |Shape [id CT] <TensorType(int64,
vector)> '' 22
| | | | | | | | | | | | | | | | | |GpuContiguous [id BS]
<GpuArrayType<None>(float32, (False, False, False, False))> '' 10
| | | | | | | | | | | | | | | | |Constant{1.0} [id CU] <float32>
| | | | | | | | | | | | | | | | |Constant{0.0} [id CV] <float32>
| | | | | | | | | | | | | | | |InplaceGpuDimShuffle{x,0,x,x} [id CW]
<GpuArrayType<None>(float32, (True, False, True, True))> '' 9
| | | | | | | | | | | | | | | | |<GpuArrayType<None>(float32, (False,))>
[id CX] <GpuArrayType<None>(float32, (False,))>
| | | | | | | | | | | | | | | |GpuArrayConstant{[[[[ 0.1]]]]} [id BL]
<GpuArrayType<None>(float32, (True, True, True, True))>
| | | | | | | | | | | | | | |TensorConstant{[128 64 14 14]} [id CY]
<TensorType(int32, vector)>
| | | | | | | | | | | | | |GpuContiguous [id CZ]
<GpuArrayType<None>(float32, (False, False, False, False))> '' 8
| | | | | | | | | | | | | | |<GpuArrayType<None>(float32, (False, False,
False, False))> [id DA] <GpuArrayType<None>(float32, (False, False, False,
False))>
| | | | | | | | | | | | | |GpuAllocEmpty{dtype='float32',
context_name=None} [id DB] <GpuArrayType<None>(float32, (False, False,
False, False))> '' 63
| | | | | | | | | | | | | | |Assert{msg='The convolution would produce an
invalid shape (dim[0] < 0).'} [id DC] <TensorType(int64, scalar)> '' 60
| | | | | | | | | | | | | | | |Shape_i{0} [id DD] <TensorType(int64,
scalar)> '' 54
| | | | | | | | | | | | | | | | |GpuContiguous [id BI]
<GpuArrayType<None>(float32, (False, False, False, False))> '' 51
| | | | | | | | | | | | | | | |Elemwise{ge,no_inplace} [id DE]
<TensorType(bool, scalar)> '' 57
| | | | | | | | | | | | | | | |Shape_i{0} [id DD] <TensorType(int64,
scalar)> '' 54
| | | | | | | | | | | | | | | |TensorConstant{0} [id DF] <TensorType(int8,
scalar)>
| | | | | | | | | | | | | | |Assert{msg='The convolution would produce an
invalid shape (dim[1] < 0).'} [id DG] <TensorType(int64, scalar)> '' 33
| | | | | | | | | | | | | | | |Shape_i{0} [id DH] <TensorType(int64,
scalar)> '' 21
| | | | | | | | | | | | | | | | |GpuContiguous [id CZ]
<GpuArrayType<None>(float32, (False, False, False, False))> '' 8
| | | | | | | | | | | | | | | |Elemwise{ge,no_inplace} [id DI]
<TensorType(bool, scalar)> '' 29
| | | | | | | | | | | | | | | |Shape_i{0} [id DH] <TensorType(int64,
scalar)> '' 21
| | | | | | | | | | | | | | | |TensorConstant{0} [id DJ] <TensorType(int8,
scalar)>
| | | | | | | | | | | | | | |Assert{msg='The convolution would produce an
invalid shape (dim[2] <= 0).'} [id DK] <TensorType(int64, scalar)> '' 62
| | | | | | | | | | | | | | | |Elemwise{Composite{(((i0 - (((i1 - i2) * i3)
+ i4)) // i5) + i6)}}[(0, 0)] [id DL] <TensorType(int64, scalar)> '' 56
| | | | | | | | | | | | | | | | |Shape_i{2} [id DM] <TensorType(int64,
scalar)> '' 53
| | | | | | | | | | | | | | | | | |GpuContiguous [id BI]
<GpuArrayType<None>(float32, (False, False, False, False))> '' 51
| | | | | | | | | | | | | | | | |Shape_i{2} [id DN] <TensorType(int64,
scalar)> '' 20
| | | | | | | | | | | | | | | | | |GpuContiguous [id CZ]
<GpuArrayType<None>(float32, (False, False, False, False))> '' 8
| | | | | | | | | | | | | | | | |TensorConstant{1} [id CH]
<TensorType(int8, scalar)>
| | | | | | | | | | | | | | | | |TensorConstant{1} [id CI]
<TensorType(int32, scalar)>
| | | | | | | | | | | | | | | | |TensorConstant{1} [id CH]
<TensorType(int8, scalar)>
| | | | | | | | | | | | | | | | |TensorConstant{2} [id CJ]
<TensorType(int8, scalar)>
| | | | | | | | | | | | | | | | |TensorConstant{1} [id CH]
<TensorType(int8, scalar)>
| | | | | | | | | | | | | | | |Elemwise{gt,no_inplace} [id DO]
<TensorType(bool, scalar)> '' 59
| | | | | | | | | | | | | | | |Elemwise{Composite{(((i0 - (((i1 - i2) * i3)
+ i4)) // i5) + i6)}}[(0, 0)] [id DL] <TensorType(int64, scalar)> '' 56
| | | | | | | | | | | | | | | |TensorConstant{0} [id DP] <TensorType(int8,
scalar)>
| | | | | | | | | | | | | | |Assert{msg='The convolution would produce an
invalid shape (dim[3] <= 0).'} [id DQ] <TensorType(int64, scalar)> '' 61
| | | | | | | | | | | | | | |Elemwise{Composite{(((i0 - (((i1 - i2) * i3) +
i4)) // i5) + i6)}}[(0, 0)] [id DR] <TensorType(int64, scalar)> '' 55
| | | | | | | | | | | | | | | |Shape_i{3} [id DS] <TensorType(int64,
scalar)> '' 52
| | | | | | | | | | | | | | | | |GpuContiguous [id BI]
<GpuArrayType<None>(float32, (False, False, False, False))> '' 51
| | | | | | | | | | | | | | | |Shape_i{3} [id DT] <TensorType(int64,
scalar)> '' 19
| | | | | | | | | | | | | | | | |GpuContiguous [id CZ]
<GpuArrayType<None>(float32, (False, False, False, False))> '' 8
| | | | | | | | | | | | | | | |TensorConstant{1} [id CH] <TensorType(int8,
scalar)>
| | | | | | | | | | | | | | | |TensorConstant{1} [id CI] <TensorType(int32,
scalar)>
| | | | | | | | | | | | | | | |TensorConstant{1} [id CH] <TensorType(int8,
scalar)>
| | | | | | | | | | | | | | | |TensorConstant{2} [id CJ] <TensorType(int8,
scalar)>
| | | | | | | | | | | | | | | |TensorConstant{1} [id CH] <TensorType(int8,
scalar)>
| | | | | | | | | | | | | | |Elemwise{gt,no_inplace} [id DU]
<TensorType(bool, scalar)> '' 58
| | | | | | | | | | | | | | |Elemwise{Composite{(((i0 - (((i1 - i2) * i3) +
i4)) // i5) + i6)}}[(0, 0)] [id DR] <TensorType(int64, scalar)> '' 55
| | | | | | | | | | | | | | |TensorConstant{0} [id DV] <TensorType(int8,
scalar)>
| | | | | | | | | | | | | |GpuDnnConvDesc{border_mode='valid',
subsample=(2, 2), conv_mode='conv', precision='float32'} [id DW]
<CDataType{cudnnConvolutionDescriptor_t}> '' 28
| | | | | | | | | | | | | | |Shape [id DX] <TensorType(int64, vector)> '' 18
| | | | | | | | | | | | | | |GpuContiguous [id CZ]
<GpuArrayType<None>(float32, (False, False, False, False))> '' 8
| | | | | | | | | | | | | |Constant{1.0} [id CU] <float32>
| | | | | | | | | | | | | |Constant{0.0} [id CV] <float32>
| | | | | | | | | | | | |InplaceGpuDimShuffle{x,0,x,x} [id DY]
<GpuArrayType<None>(float32, (True, False, True, True))> '' 7
| | | | | | | | | | | | | |<GpuArrayType<None>(float32, (False,))> [id DZ]
<GpuArrayType<None>(float32, (False,))>
| | | | | | | | | | | | |GpuArrayConstant{[[[[ 0.095]]]]} [id BG]
<GpuArrayType<None>(float32, (True, True, True, True))>
| | | | | | | | | | | |TensorConstant{[ 128 3200]} [id EA]
<TensorType(int32, vector)>
| | | | | | | | | | |Constant{0} [id EB] <int64>
| | | | | | | | | |Subtensor{int64} [id EC] <TensorType(int64, scalar)> ''
17
| | | | | | | | | |Shape [id ED] <TensorType(int64, vector)> '' 6
| | | | | | | | | | |w [id EE] <GpuArrayType<None>(float32, (False, False))>
| | | | | | | | | |Constant{1} [id EF] <int64>
*| | | | | | | | |TensorConstant{0.25} [id EG] <TensorType(float64,
scalar)>*
| | | | | | | | |GpuReshape{2} [id BD] <GpuArrayType<None>(float32, (False,
False))> '' 66
| | | | | | | | |w [id EE] <GpuArrayType<None>(float32, (False, False))>
*| | | | | | | | |TensorConstant{0.0} [id EH] <TensorType(float64, scalar)>*
| | | | | | | |InplaceGpuDimShuffle{x,0} [id EI]
<GpuArrayType<None>(float32, (True, False))> '' 5
| | | | | | | | |b [id EJ] <GpuArrayType<None>(float32, (False,))>
| | | | | | | |GpuArrayConstant{[[ 0.1]]} [id Y]
<GpuArrayType<None>(float32, (True, True))>
| | | | | | |TensorConstant{[128 390]} [id EK] <TensorType(int32, vector)>
| | | | | |Constant{0} [id EB] <int64>
| | | | |Subtensor{int64} [id EL] <TensorType(int64, scalar)> '' 16
| | | | |Shape [id EM] <TensorType(int64, vector)> '' 4
| | | | | |w [id EN] <GpuArrayType<None>(float32, (False, False))>
| | | | |Constant{1} [id EF] <int64>
*| | | |TensorConstant{0.25} [id EG] <TensorType(float64, scalar)>*
| | | |GpuReshape{2} [id V] <GpuArrayType<None>(float32, (False, False))>
'' 72
| | | |w [id EN] <GpuArrayType<None>(float32, (False, False))>
| | | |TensorConstant{0.0} [id EH] <TensorType(float64, scalar)>
| | |InplaceGpuDimShuffle{x,0} [id EO] <GpuArrayType<None>(float32, (True,
False))> '' 3
| | |b [id EP] <GpuArrayType<None>(float32, (False,))>
| |TensorConstant{(1L,) of 1} [id EQ] <TensorType(int32, (True,))>
|GpuFromHost<None> [id ER] <GpuArrayType<None>(int64, ())> '' 14
| |Elemwise{Composite{(i0 * (i1 + i2))}} [id L] <TensorType(int64, scalar)>
'' 2
|GpuArrayConstant{0} [id ES] <GpuArrayType<None>(int8, ())>
|GpuFromHost<None> [id ER] <GpuArrayType<None>(int64, ())> '' 14
|GpuFromHost<None> [id ET] <GpuArrayType<None>(int64, ())> '' 11
| |Shape_i{0} [id EU] <TensorType(int64, scalar)> '' 0
| |<GpuArrayType<None>(float32, (False,))> [id F]
<GpuArrayType<None>(float32, (False,))>
|GpuFromHost<None> [id ER] <GpuArrayType<None>(int64, ())> '' 14
|GpuArrayConstant{0} [id ES] <GpuArrayType<None>(int8, ())>
|GpuArrayConstant{-1} [id EV] <GpuArrayType<None>(int8, ())>
|GpuFromHost<None> [id ET] <GpuArrayType<None>(int64, ())> '' 11
|GpuFromHost<None> [id ET] <GpuArrayType<None>(int64, ())> '' 11
|GpuArrayConstant{0} [id ES] <GpuArrayType<None>(int8, ())>
|GpuArrayConstant{0} [id ES] <GpuArrayType<None>(int8, ())>
|GpuFromHost<None> [id EW] <GpuArrayType<None>(int64, ())> '' 12
| |Elemwise{mul,no_inplace} [id H] <TensorType(int64, scalar)> '' 1
|GpuArrayConstant{0} [id ES] <GpuArrayType<None>(int8, ())>
|GpuFromHost<None> [id EW] <GpuArrayType<None>(int64, ())> '' 12
|GpuFromHost<None> [id ET] <GpuArrayType<None>(int64, ())> '' 11
|GpuFromHost<None> [id EW] <GpuArrayType<None>(int64, ())> '' 12
|GpuArrayConstant{0} [id ES] <GpuArrayType<None>(int8, ())>
|GpuArrayConstant{0} [id ES] <GpuArrayType<None>(int8, ())>
|GpuFromHost<None> [id ET] <GpuArrayType<None>(int64, ())> '' 11
|GpuFromHost<None> [id ET] <GpuArrayType<None>(int64, ())> '' 11
|GpuArrayConstant{0} [id ES] <GpuArrayType<None>(int8, ())>
|GpuArrayConstant{0} [id ES] <GpuArrayType<None>(int8, ())>
--Return--
None
> c:\programdata\miniconda2\lib\site-packages\theano\tensor\var.py(824)
__init__()
822 elif config.warn_float64 == 'pdb':
823 import pdb
--> 824 pdb.set_trace()
825 TensorType.Variable = TensorVariable
826
ipdb>
ipdb> p variable_name
*** NameError: NameError("name 'variable_name' is not defined",)
ipdb> p dir
<built-in function dir>
ipdb> l
819 warnings.warn(msg, stacklevel=1 + nb_rm)
820 elif config.warn_float64 == "raise":
821 raise Exception(msg)
822 elif config.warn_float64 == 'pdb':
823 import pdb
--> 824 pdb.set_trace()
825 TensorType.Variable = TensorVariable
826
827
828 class TensorConstantSignature(tuple):
829 """
ipdb> up
> c:\programdata\miniconda2\lib\site-packages\theano\tensor\type.py(353)
make_variable()
351
352 """
--> 353 return self.Variable(self, name=name)
354
355 def __str__(self):
ipdb> up
> c:\programdata\miniconda2\lib\site-packages\theano\gof\type.py(405)
__call__()
403
404 """
--> 405 return utils.add_tag_trace(self.make_variable(name))
406
407 def values_eq(self, a, b):
ipdb> up
> c:\programdata\miniconda2\lib\site-packages\theano\gpuarray\basic_ops.py
(564)make_node()
562 return Apply(self, [x],
563 [tensor.TensorType(dtype=x.dtype,
--> 564 broadcastable=x.broadcastable)()])
565
566 def perform(self, node, inp, out):
ipdb> up
> c:\programdata\miniconda2\lib\site-packages\theano\gof\op.py(615)__call__
()
613 """
614 return_list = kwargs.pop('return_list', False)
--> 615 node = self.make_node(*inputs, **kwargs)
616
617 if config.compute_test_value != 'off':
ipdb> up
> c:\programdata\miniconda2\lib\site-packages\theano\gpuarray\opt.py(175)
safe_to_cpu()
173 def safe_to_cpu(x):
174 if isinstance(x.type, GpuArrayType):
--> 175 return host_from_gpu(x)
176 else:
177 return x
ipdb> up
> c:\programdata\miniconda2\lib\site-packages\theano\gpuarray\opt.py(235)
local_opt()
233 if isinstance(new_op, theano.Op):
234 return [safe_to_cpu(o) for o in
--> 235 new_op(*node.inputs, return_list=True)]
236 elif isinstance(new_op, (tuple, list)):
237 return [safe_to_cpu(o) for o in new_op]
ipdb> up
> c:\programdata\miniconda2\lib\site-packages\theano\gof\opt.py(1982)
process_node()
1980 lopt = lopt or self.local_opt
1981 try:
-> 1982 replacements = lopt.transform(node)
1983 except Exception as e:
1984 if self.failure_callback is not None:
ipdb> up
> c:\programdata\miniconda2\lib\site-packages\theano\gof\opt.py(2470)apply()
2468 nb = change_tracker.nb_imported
2469 t_opt = time.time()
-> 2470 lopt_change = self.process_node(fgraph, node, lopt)
2471 time_opts[lopt] += time.time() - t_opt
2472 if not lopt_change:
ipdb> up
> c:\programdata\miniconda2\lib\site-packages\theano\gof\opt.py(87)optimize
()
85 orig = theano.tensor.basic.constant.enable
86 theano.tensor.basic.constant.enable = False
---> 87 ret = self.apply(fgraph, *args, **kwargs)
88 finally:
89 theano.tensor.basic.constant.enable = orig
ipdb> up
> c:\programdata\miniconda2\lib\site-packages\theano\gof\opt.py(235)apply()
233 nb_nodes_before = len(fgraph.apply_nodes)
234 t0 = time.time()
--> 235 sub_prof = optimizer.optimize(fgraph)
236 l.append(float(time.time() - t0))
237 sub_profs.append(sub_prof)
ipdb> up
> c:\programdata\miniconda2\lib\site-packages\theano\gof\opt.py(87)optimize
()
85 orig = theano.tensor.basic.constant.enable
86 theano.tensor.basic.constant.enable = False
---> 87 ret = self.apply(fgraph, *args, **kwargs)
88 finally:
89 theano.tensor.basic.constant.enable = orig
ipdb> up
> c:\programdata\miniconda2\lib\site-packages\theano\gof\opt.py(235)apply()
233 nb_nodes_before = len(fgraph.apply_nodes)
234 t0 = time.time()
--> 235 sub_prof = optimizer.optimize(fgraph)
236 l.append(float(time.time() - t0))
237 sub_profs.append(sub_prof)
ipdb> up
> c:\programdata\miniconda2\lib\site-packages\theano\gof\opt.py(87)optimize
()
85 orig = theano.tensor.basic.constant.enable
86 theano.tensor.basic.constant.enable = False
---> 87 ret = self.apply(fgraph, *args, **kwargs)
88 finally:
89 theano.tensor.basic.constant.enable = orig
ipdb> up
> c:\programdata\miniconda2\lib\site-packages\theano\gof\opt.py(98)__call__
()
96
97 """
---> 98 return self.optimize(fgraph)
99
100 def add_requirements(self, fgraph):
ipdb> up
>
c:\programdata\miniconda2\lib\site-packages\theano\compile\function_module.py
(1474)__init__()
1472 optimizer, inputs, outputs)
1473 else:
-> 1474 optimizer_profile = optimizer(fgraph)
1475
1476 end_optimizer = time.time()
ipdb> up
>
c:\programdata\miniconda2\lib\site-packages\theano\compile\function_module.py
(1794)orig_function()
1792 profile=profile,
1793 on_unused_input=on_unused_input,
-> 1794 output_keys=output_keys).create(
1795 defaults)
1796
ipdb> up
> c:\programdata\miniconda2\lib\site-packages\theano\compile\pfunc.py(486)
pfunc()
484 accept_inplace=accept_inplace, name=name,
485 profile=profile, on_unused_input=on_unused_input,
--> 486 output_keys=output_keys)
487
488
ipdb> up
> c:\programdata\miniconda2\lib\site-packages\theano\compile\function.py
(326)function()
324 on_unused_input=on_unused_input,
325 profile=profile,
--> 326 output_keys=output_keys)
327 # We need to add the flag check_aliased inputs if we have any mutable or
328 # borrowed used defined inputs
ipdb> up
> c:\users\michael\desktop\research\code\strided_fitnet.py(139)SGD()
137 givens={
138 self.x: training_x[i*self.mini_batch_size: (i+1)*self.mini_batch_size],
--> 139 self.y: training_y[i*self.mini_batch_size: (i+1)*self.
mini_batch_size]
140 })
141
ipdb>
How do I find it?
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