Fred,
I run the convnet another time using nanguardmode and I had not an illegal
memory access.
The training cost start as 0.69336 and it doesn't change.
This is the output:
Mapped name None to device cuda: GeForce 840M
Using cuDNN version 5005 on context None
/home/luca/data/Theano-master/
theano/tensor/signal/downsample.py:6: UserWarning: downsample module has
been moved to the theano.tensor.signal.pool module.
"downsample module has been moved to the theano.tensor.signal.pool
module.")
Using gpu device 0: GeForce 840M (CNMeM is disabled, cuDNN 5005)
Disabling C code for Elemwise{mul,no_inplace} due to unsupported float16
Disabling C code for Elemwise{Cast{float32}} due to unsupported float16
Disabling C code for Elemwise{Cast{float16}} due to unsupported float16
Disabling C code for Elemwise{Cast{float16}} due to unsupported float16
Disabling C code for Alloc due to unsupported float16
Disabling C code for DiagonalSubtensor{inplace} due to unsupported float16
Disabling C code for Pool{ds=(2, 2), ignore_border=False, st=(2, 2),
padding=(0, 0), mode='max'} due to unsupported float16
Disabling C code for Pool{ds=(1, 2), ignore_border=False, st=(1, 2),
padding=(0, 0), mode='max'} due to unsupported float16
Disabling C code for MaxPoolGrad{ds=(1, 2), ignore_border=False, st=(1, 2),
padding=(0, 0), mode='max'} due to unsupported float16
Disabling C code for MaxPoolGrad{ds=(2, 2), ignore_border=False, st=(2, 2),
padding=(0, 0), mode='max'} due to unsupported float16
Disabling C code for IncDiagonalSubtensor due to unsupported float16
Disabling C code for DiagonalSubtensor{inplace} due to unsupported float16
Disabling C code for Pool{ds=(2, 2), ignore_border=False, st=(2, 2),
padding=(0, 0), mode='max'} due to unsupported float16
Disabling C code for Pool{ds=(1, 2), ignore_border=False, st=(1, 2),
padding=(0, 0), mode='max'} due to unsupported float16
Disabling C code for MaxAndArgmax due to unsupported float16
start time:
26/07/2016
11:16:52
images for training: 288
images for validation: 50
epochs: 300
... training neural network 25
training @ iter = 0
training @ iter = 200
training cost 0.69336
epoch 1, training batch 288/288,validation error 50.000 %
training @ iter = 400
training cost 0.69336
epoch 2, training batch 288/288,validation error 50.000 %
training @ iter = 600
training @ iter = 800
training cost 0.69336
epoch 3, training batch 288/288,validation error 50.000 %
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