allwefantasy

-- set json = '''{}''';
-- load jsonStr.`json` as emptyData;

-- -- run emptyData as MnistLoaderExt.`` where
-- -- mnistDir="/Users/allwefantasy/Downloads/mnist"
-- -- as data;

-- run emptyData as ImageLoaderExt.`/Users/allwefantasy/Downloads/cifar/test`
-- where numOfImageTasks="4"
-- and code='''

allwefantasy

-- set json = '''{}''';
-- load jsonStr.`json` as emptyData;

-- -- run emptyData as MnistLoaderExt.`` where
-- -- mnistDir="/Users/allwefantasy/Downloads/mnist"
-- -- as data;

-- run emptyData as ImageLoaderExt.`/Users/allwefantasy/Downloads/cifar/test`
-- where numOfImageTasks="4"
-- and code='''

allwefantasy

import mxnet as mx
import numpy as np
import cPickle
import cv2

def extractImagesAndLabels(path, file):
    f = open(path+file, 'rb')
    dict = cPickle.load(f)
    images = dict['data']
    images = np.reshape(images, (10000, 3, 32, 32))

allwefantasy

load csv.`/Users/allwefantasy/CSDNWorkSpace/mlflow/examples/sklearn_elasticnet_wine/wine-quality.csv` 
where header="true" and inferSchema="true" 
as data;



train data as PythonAlg.`/tmp/abc` 
 
 where pythonScriptPath="/Users/allwefantasy/CSDNWorkSpace/mlflow/examples/sklearn_elasticnet_wine"
 and keepVersion="true"

allwefantasy

load csv.`/Users/allwefantasy/CSDNWorkSpace/mlflow/examples/sklearn_elasticnet_wine/wine-quality.csv` 
where header="true" and inferSchema="true" 
as data;



train data as PythonAlg.`/tmp/abc` 
 
 where pythonScriptPath="/Users/allwefantasy/CSDNWorkSpace/mlflow/examples/sklearn_elasticnet_wine"
 and keepVersion="true"

allwefantasy

QUnit.module("my-module",
{
  setup: function () {
    // Configure Sinon's FakeServer instance.  `autoRespond` ensures that any incoming requests
    // will automatically be replied to, otherwise you *must* invoke `this.server.respond` to
    // start processing.
    this.server = sinon.fakeServer.create();
    this.server.autoRespond = true;

    // Tells the FakeServer's XHR request factory that we don't want to respond to every

allwefantasy

set traning_dir = "/tmp/lwys_corpus";

-- 加载数据
load csv.`/Users/allwefantasy/Downloads/lwys_corpus` options header="true" and delimiter="\t" and quote="'"
as lwys_corpus;

select cut as features,cast(sid as int) as label from lwys_corpus
as orginal_text_corpus;

allwefantasy

import tensorflow as tf
import numpy as np
import os

BASIC_HOME = "/Users/allwefantasy/Downloads"
WORD_VECTOR_FILE = BASIC_HOME + '/laiwen/zhuhl_laiwen_word_embedding'
WORD_FILE = BASIC_HOME + '/laiwen/zhuhl_laiwen_keywords'
WORD_RESULT_VECTOR_FILE = BASIC_HOME + '/laiwen/WORD_RESULT_VECTOR_FILE6'
MODEL_SAVE_DIR = BASIC_HOME + '/laiwen/model'
MODEL_NAME = "autoencoder"

allwefantasy

import tensorflow as tf
import random
import numpy as np
import time

BASIC_HOME = "/Users/allwefantasy/Downloads"
WORD_VECTOR_FILE = BASIC_HOME + '/laiwen/zhuhl_laiwen_word_embedding'
WORD_FILE = BASIC_HOME + '/laiwen/zhuhl_laiwen_keywords2'
WORD_RESULT_VECTOR_FILE = BASIC_HOME + '/laiwen/WORD_RESULT_VECTOR_FILE4'
MODEL_SAVE_DIR = BASIC_HOME + '/laiwen/model/autoencoder'

allwefantasy

import tensorflow as tf
import random
import numpy as np
import time

WORD_VECTOR_FILE = '/tmp/laiwenvector/part-00000'
WORD_FILE = '/tmp/laiwenwords/part-00000'
WORD_RESULT_VECTOR_FILE = '/tmp/WORD_RESULT_VECTOR_FILE'
MODEL_SAVE_DIR = "/tmp/cnn/my_test_model"