klaus82

from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime, timedelta
from algorithm import test

default_args = {
    "owner": "airflow",
    "depends_on_past": False,
    "start_date": datetime(2015, 6, 1),
    "email": ["airflow@airflow.com"],

klaus82

from keras.callbacks import ModelCheckpoint

# checkpoint
filepath="weights.best.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint]

batch_size = 512
history = model.fit(partial_X_train, 
                    partial_Y_train, 

klaus82

#####!/usr/bin/env /bin/bash
#! /bin/bash
INDEX_NAME="tweet-classification"
curl -XPUT "http://localhost:9200/$INDEX_NAME/" -d '{
  "settings": {
    "number_of_shards": 2,
    "number_of_replicas": 1
  },
  "mappings": {
    "classified-tweet": {

klaus82

from keras.models import load_model

def load_nn(nn_hdf5_filename):
    model = load_model(nn_hdf5_filename)
    return model

def main():
    #load neural network model
    model = load_nn('weights.best.hdf5')
      

klaus82

# base image
FROM python:3.6

# updating repository
RUN apt-get update

# copying requirements.txt file
COPY requirements.txt requirements.txt

# pip install

klaus82

# ## Sentiment analysis

# In[12]:

import nltk


# In[8]:

nltk.download("punkt")

klaus82

# coding: utf-8

# In[1]:
import pandas as pd
import numpy as np


# In[2]:
#Read the dataset in csv
tweets = pd.read_csv('./twitter-airline-sentiment/Tweets.csv', sep=',')

klaus82

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import pandas as pd
import csv

num_clusters = 4
num_steps = 100
num_vectors = 1000

klaus82

public class Singleton {
    private static final Singleton instance = new Singleton();
    public static Singleton getInstance() { return instance; }
    private Singleton() {}
}