nudles

Updated at 3:46PM, June 4

We are going to implement this model using SINGA. https://github.com/jojonki/QA-LSTM Please ignore the text below currently.

Task

Create a question answering model for customer support.

The dataset

nudles

class Layer:
   def __init__(self,):
       self.has_initialized = False
        
   def get_params(self):
       """return the params of this layer and sublayers as a dict;  
          param name is: layername.param_name. e.g., 
          
          self.W = Tensor(), self.b=Tensor()
          name of W and b is  like conv1.W and conv1.b  

simon-mo

STT_FUNC         STB_GLOBAL STO_ENTRY      cudnn_maxwell_gcgemm_32x32_cc
STT_FUNC         STB_GLOBAL STO_ENTRY      cudnn_maxwell_gcgemm_32x32_cc_batched
STT_FUNC         STB_GLOBAL STO_ENTRY      cudnn_maxwell_gcgemm_32x32_cn
STT_FUNC         STB_GLOBAL STO_ENTRY      cudnn_maxwell_gcgemm_32x32_cn_batched
STT_FUNC         STB_GLOBAL STO_ENTRY      cudnn_maxwell_gcgemm_32x32_ct
STT_FUNC         STB_GLOBAL STO_ENTRY      cudnn_maxwell_gcgemm_32x32_ct_batched
STT_FUNC         STB_GLOBAL STO_ENTRY      cudnn_maxwell_gcgemm_32x32_lower_cn
STT_FUNC         STB_GLOBAL STO_ENTRY      cudnn_maxwell_gcgemm_32x32_lower_cn_batched
STT_FUNC         STB_GLOBAL STO_ENTRY      cudnn_maxwell_gcgemm_32x32_lower_nc
STT_FUNC         STB_GLOBAL STO_ENTRY      cudnn_maxwell_gcgemm_32x32_lower_nc_batched

dcslin

import os
import urllib.request
import gzip
import numpy as np
import codecs

from singa import device
from singa import tensor
from singa import opt
from singa import autograd

dbiesecke

# Use offical jupyter image and added iperl
# docker run --rm -it -p 8000:8888 -v "$(pwd):/notebooks" nated/jupyter
FROM jupyter/notebook

MAINTAINER Daniel Biesecke <dbiesecke@gmail.com>

RUN  apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -yq --no-install-recommends \
                libzmq3-dev libmagic-dev cpanminus 

RUN git clone https://github.com/EntropyOrg/p5-Devel-IPerl.git /iperl