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.
Create a question answering model for customer support.
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.
Create a question answering model for customer support.
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 |
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 |
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 |
# 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 |