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Open session Modal

MOM.modal.openSessionModal(options);

Options

title: 'Your session is about to expire',
message: 'You will be logged out in <strong>{time}</strong> seconds. Do you want to stay logged in?',

confirm_action: 'javascript:fnConfirmSessionExtend()',

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from autobahn.twisted.websocket import WebSocketServerProtocol, WebSocketServerFactory, listenWS
from twisted.python import log
from autobahn.util import newid, utcnow
import Cookie
from chatterbot import ChatBot
from chatterbot.trainers import ChatterBotCorpusTrainer
from chatterbot.conversation import Statement
from users import Users

import sys

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function toNestedArray (data, rootLevel = 0, parentNode) {
  let output = []
  for (var i = 0; i < data.length; i++) {
    var count = data[i].count
    var items = data[i].name.split('/')
    var hasParent = items.length > rootLevel
    if (hasParent) {
      let parent = rootLevel
      ? items.length === rootLevel
        ? null

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from __future__ import print_function

import json
import os
import numpy as np

from gensim.models import Word2Vec
from gensim.utils import simple_preprocess
from keras.engine import Input
from keras.layers import Embedding, merge

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from keras.layers.core import Layer  
from keras import initializers, regularizers, constraints  
from keras import backend as K

class Attention(Layer):
    def __init__(self,
                 kernel_regularizer=None, bias_regularizer=None,
                 kernel_constraint=None, bias_constraint=None,
                 use_bias=True, **kwargs):
        """

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from keras import backend as K
from keras.layers import Input, Dense, merge, Dropout, Lambda, LSTM, Masking
from keras.models import Model, Sequential
from keras.optimizers import SGD, RMSprop, Adam, Nadam
from sys import argv
import argparse
import csv
import json
import numpy as np
import pickle

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class AttentionLayer(Layer):
    '''
    Attention layer. 
    Usage:
        lstm_layer = LSTM(dim, return_sequences=True)
        attention = AttentionLayer()(lstm_layer)
        sentenceEmb = merge([lstm_layer, attention], mode=lambda x:x[1]*x[0], output_shape=lambda x:x[0])
        sentenceEmb = Lambda(lambda x:K.sum(x, axis=1), output_shape=lambda x:(x[0],x[2]))(sentenceEmb)
    '''
    def __init__(self, init='glorot_uniform', kernel_regularizer=None, bias_regularizer=None, kernel_constraint=None, bias_constraint=None,  **kwargs):

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class AttentionWithContext(Layer):
    """
        Attention operation, with a context/query vector, for temporal data.
        Supports Masking.
        Follows the work of Yang et al. [https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf]
        "Hierarchical Attention Networks for Document Classification"
        by using a context vector to assist the attention
        # Input shape
            3D tensor with shape: `(samples, steps, features)`.
        # Output shape

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{ test: require.resolve("react"), loader: "expose-loader?React" },
{ test: require.resolve("react-dom"), loader: "expose-loader?ReactDOM" },
{
  test: /redux\/es\/index.js/,
  use: [
    {
      loader: 'expose-loader',
      options: 'Redux'
    }
  ]

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class AttentionLSTM(LSTM):
    """LSTM with attention mechanism

    This is an LSTM incorporating an attention mechanism into its hidden states.
    Currently, the context vector calculated from the attended vector is fed
    into the model's internal states, closely following the model by Xu et al.
    (2016, Sec. 3.1.2), using a soft attention model following
    Bahdanau et al. (2014).

    The layer expects two inputs instead of the usual one: