sebmarkbage

import { Component } from "React";

export var Enhance = ComposedComponent => class extends Component {
  constructor() {
    this.state = { data: null };
  }
  componentDidMount() {
    this.setState({ data: 'Hello' });
  }
  render() {

MattMcFarland

Table of Contents:

• Why FLUX?
• About this Guide
• Library Dependencies
• Directory Structure
• store.js
• constants.js
• dispatcher.js
• actions.js
• app.js

piotr-teterwak

from __future__ import absolute_import
from __future__ import print_function
import numpy as np
np.random.seed(1337)  # for reproducibility

import random
from keras.datasets import mnist
from keras.models import Sequential, Graph
from keras.layers.core import Dense, Dropout, Lambda
from keras.optimizers import SGD, RMSprop

jesstelford

Regular Event Loop

This shows the execution order given JavaScript's Call Stack, Event Loop, and any asynchronous APIs provided in the JS execution environment (in this example; Web APIs in a Browser environment)

---

Given the code

mbollmann

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:

cbaziotis

from keras import backend as K, initializers, regularizers, constraints
from keras.engine.topology import Layer


def dot_product(x, kernel):
    """
    Wrapper for dot product operation, in order to be compatible with both
    Theano and Tensorflow
    Args:

ameasure

# -*- coding: utf-8 -*-
'''Trains an LSTM on the IMDB sentiment classification task with soft attention.

Experiments with max_features=10000, max_len=80
    1) MLP-dropout-tanh attention: 83.59 at epoch 4
    2) MLP-dropout-relu attention: 83.26 at epoch 3
    3) MLP-tanh attention: 82.91 at epoch 4
    4) GlobalMaxPooling1D attention: 82.44 at epoch 7
'''
from __future__ import print_function

slashvar

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

namakemono

from keras.layers import *
from keras.activations import softmax
from keras.models import Model

"""

References
----------
[1]. Parikh, Ankur P., et al. "A decomposable attention model for natural language inference." arXiv preprint arXiv:1606.01933 (2016).

nilshartmann

Libraries and Tools for React

If you're developing an application based on React it can be helpful if you don't need to develop all the basic UI components yourself. Here you can find a list with various components, component libraries and complete design systems developed with and for React.

As the list got longer and longer I thought it would be better to have a "real" site for it.

👉 Please find the (new) list here: https://react-libs.nilshartmann.net/