luckmoon

# yum
There was a problem importing one of the Python modules
required to run yum. The error leading to this problem was:

   pycurl: libcurl link-time ssl backend (nss) is different from compile-time ssl backend (openssl)

Please install a package which provides this module, or
verify that the module is installed correctly.

luckmoon

• Download CMake from: https://cmake.org/download/

wget https://cmake.org/files/v3.11/cmake-3.11.0.tar.gz

• Compile from source and install

tar zxvf cmake-3.11.0.tar.gz && cd cmake-3.11.0

luckmoon

# (C) Kyle Kastner, June 2014
# License: BSD 3 clause

import scipy.stats as st
import numpy as np

class gmmhmm:
    #This class converted with modifications from https://code.google.com/p/hmm-speech-recognition/source/browse/Word.m
    def __init__(self, n_states):
        self.n_states = n_states

luckmoon

# You may use this CentOS 7 repository on Fedora Copr for Vim 8 builds.
#      https://copr.fedorainfracloud.org/coprs/mcepl/vim8/
#
#  Run these commands on CentOS 7.

# Add this repository:

sudo curl -L https://copr.fedorainfracloud.org/coprs/mcepl/vim8/repo/epel-7/mcepl-vim8-epel-7.repo -o /etc/yum.repos.d/mcepl-vim8-epel-7.repo

# Upgrade Vim to vim 8:

luckmoon

# coding:utf-8

from elasticsearch import Elasticsearch
import json

# Define config
host = "127.0.0.1"
port = 9200
timeout = 1000
index = "index"

luckmoon

#!/usr/bin/env python
# -*- coding:UTF-8 -*-

import torch
import torch.nn as nn
import torch.nn.init as init


def weight_init(m):
    '''

luckmoon

import torch
from torch import LongTensor
from torch.nn import Embedding, LSTM
from torch.autograd import Variable
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence

## We want to run LSTM on a batch of 3 character sequences ['long_str', 'tiny', 'medium']
#
#     Step 1: Construct Vocabulary
#     Step 2: Load indexed data (list of instances, where each instance is list of character indices)

luckmoon

看論文要注意的三點

(1)其分析的資料為何？(2) 其應用為何？ (3) 要達成該應用有什麼問題需要解決？

Ontoloty比賽

https://competitions.codalab.org/competitions/17119

需要看的論文

luckmoon

import tensorflow as tf


def create_bad_dataset(create_batches=True):
  dataset = tf.data.Dataset.from_tensor_slices([1., 2., 0., 4., 8., 16.])
  # Computing `tf.check_numerics(1. / 0.)` will raise an InvalidArgumentError.
  if create_batches:
    # Demonstrates that error handling works with map_and_batch
    dataset = dataset.apply(tf.contrib.data.map_and_batch(
                  map_func=lambda x: tf.check_numerics(1. / x, 'error'), batch_size=2))

luckmoon

import numpy as np
import tensorflow as flow
from tensorflow.python.saved_model import loader

# first, read the pretrained weights into a dictionary
variables = {}
g1 = tf.Graph()
with g1.as_default():
    restore_from = 'pretrained_model/1513006564'
    with tf.Session() as sess: