Hi Arash,

Have you installed PySINGA via the following approaches (
http://singa.apache.org/en/docs/installation.html#install-pysinga)?

# in build/ folder
cmake -DUSE_CUDA=ON -DUSE_PYTHON=ON ..
make
cd python
pip install .

or using the python wheel

On Thu, Sep 22, 2016 at 2:02 PM, Arash Shafiei <arash.shaf...@gmail.com>
wrote:

> Dear Wang Wei,
>
> We are still working on the activity recognition project and have some
> difficulties installing SINGA.
>
> The python2.7 is installed. Trying to do RNN training using "python2.7
> train.py linux_input.txt", we get the following error:
>
> ImportError: No module name singa.
>
> It seems that python cannot find the singa module. We tried to set the
> path of singa in PYTHONPATH but it didn't help.
>
> Python is installed in:
>
> /usr/include/python2.7
> /usr/lib/python2.7
> /usr/bin/python2.7
>
> Singa is installed in:
> /usr/local/include/singa
> /usr/local/bin/singa
>
> Thanks.
> Arash
>
>
> On Fri, Sep 16, 2016 at 3:24 PM, Wang Wei <wang...@comp.nus.edu.sg> wrote:
>
>> Sure.
>> You can use the API of RNN (http://singa.apache.org/en/do
>> cs/layer.html#singa.layer.RNN) to construct a supervised model and train
>> it with labels.
>> E.g., a simple model would be input->rnn->dense->softmax->cross-entropy
>> loss.
>> The loss is computed to reduce the prediction error against the ground
>> truth labels.
>>
>> You may need to read some papers on activity recognition and find proper
>> datasets, e.g. http://archive.ics.uci.edu/ml/
>> datasets/Heterogeneity+Activity+Recognition.
>> I think one problem is that you need to do some feature engineering for
>> the raw data, whose feature has few dimensions.
>> We have tried to use the original 3-dimendional feature with a MLP model,
>> the performance is not good (about 50% accuracy). But the 500-dimensional
>> feature in the above dataset has about 90% accuracy.
>>
>> Best,
>> Wei
>>
>>
>

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