• These changes use the dart2_constant package to use dart 1 and dart 2 compatible constant names and widen the dart sdk range to allow for both dart versions. Other languages are untouched. <h3>...
• <div><p>Add a Dart 2 announcement banner to very page. This PR is the Dart 2 counterpart to the 1.x #764. <p><img width="849" alt="screen shot 2018-04-09 at 12 44 55" src=...
• - [x] Drop <a href="https://www.dartlang.org/guides/language/analysis-options#enabling-dart-2-semantics-sdk-200-dev680">Enabling Dart 2 semantics (SDK <2.0.0-dev.68.0)</a>? - [x] Fix a number of ...
• <p><code>Run the dart2_fix tool.</code> – include a short sub-bullet about what this does? Link to the pub page - https://pub.dartlang.org/packages/dart2_fix</p><p>该提问来源于开源项目：dart-...
• dart dart2 区别翻译自: https://hackernoon.com/10-good-reasons-why-you-should-learn-dart-4b257708a332dart dart2 区别
dart dart2 区别翻译自: https://hackernoon.com/10-good-reasons-why-you-should-learn-dart-4b257708a332dart dart2 区别

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• Dart2基础概念，Dart2注释，单行注释，多行注释，文档注释  Flutter1.0已经发布，而作为Flutter的编写语言Dart2相比较Dart1也有了非常大的改变，在Flutter发布预览版的时候试着使用Flutter编写了一些demo，但是因为...
Dart2
Flutter1.0已经发布，而作为Flutter的编写语言Dart2相比较Dart1也有了非常大的改进，在Flutter发布预览版的时候试着使用Flutter编写了一些demo，但是因为写惯了java，使用Dart2的时候还是有一些别扭。目前貌似还没有Dart2的书籍出版（有的话可以告诉我），所以准备根据 Dart2官方文档 从零开始学习。
Dart2重要概念

你可以放到一个变量中的所有内容都是对象，每一个对象都是一个类的实例。数字，函数以及 null 都是对象。所有的对象都继承自 Object 类。
尽管Dart是强类型的，但是类型注释是可选的，因为Dart可以推导出类型。如果要明确说明不需要任何类型，请使用特殊类型dynamic。
Dart支持泛型类型，如List(整数列表)，List(任何类型的对象列表).
Dart支持顶级函数（例如main()）,以及绑定到类或者对象的函数（分别是静态和实例方法）。你还可以在函数内创建函数（嵌套函数或本地函数）。
类似的Dart支持顶级变量以及绑定到类或者对象的变量（分别是静态或者实例变量）。实例变量有时也被称为字段或者属性。
不同于Java，Dart没有public，protected和private关键字。如果一个标识符以下划线（—）开始，那么它对其库是私有的。
标识符以字符或者下划线开头，后跟字符和数字的任意组合。
Dart有两种表达式（具有运行时值）和语句（不具有运行时值）。例如：条件表达式condition ? expr1 : expr2 的值为expr1或expr2，将其与if-else语句相比较，该语句没有任何值。语句通常包含一个或者多个表达式，但是一个表达式不能直接包含一个语句。
Dart工具可以报告两种问题：警告（warning）和错误（errors）.警告（warning）仅仅暗示你的代码可能不会运行，但是它们不会阻止你的程序的运行。错误（Errors）可以使编译时或运行时的。一个编译时错误会阻止代码的运行；而一个运行时错误导致代码在执行时引发异常。

注释

单行注释,使用//开头

// TODO: refactor into an AbstractLlamaGreetingFactory?


多行注释

void main() {
/*
* This is a lot of work. Consider raising chickens.

Llama larry = Llama();
larry.feed();
larry.exercise();
larry.clean();
*/
}


文档注释

第一种方式以 /// 开头

/// Feeds your llama [Food].
///
/// The typical llama eats one bale of hay per week.
void feed(Food food) {
// ...
}


第二种方式以 /** 开头

/// Feeds your llama [Food].
///
/// The typical llama eats one bale of hay per week.
void feed(Food food) {
/**
}

注：在文档注释中可以使用括号[],()来链接到 类，方法，字段，全局变量，函数以及参数
变量
如下可以创建一个变量并且初始化它：
var name = 'Bob';

变量存储的是引用。一个名为 name 的变量包含对字符串对象的引用，其值为“Bob”。
name变量的类型被推断为字符串，但是你可以通过明确指定其类型来改变它的类型。如果一个对象不限于单一类型，可以指定为 Object或dynamic类型。
dynamic name = 'Bob';

另一种选择是显式声明可以推断出的类型：
String name = 'Bob';

默认值
未被初始化的变量有一个初始化的值 null.即使是数字类型的变量最初也为null，因为数字像Dart中其他任何类型一样都是对象。
int lineCount;
assert(lineCount == null);

Final 和 const
如果你从不打算更改变量，请使用final 或 const，而不是var或者类型。一个 final 变量仅仅可以被设置一次；一个 const 变量是一个编译时常量。const 变量是隐式 final 变量。final修饰的顶级变量或者类变量在第一次使用时被初始化。
例如：
final name = 'Bob'; // Without a type annotation
final String nickname = 'Bobby';

你不能改变一个final 变量的值：
name = 'Alice'; // Error: a final variable can only be set once.

对于一个要成为编译时常量的变量请使用 const 修饰。如果一个const变量是类级别的，将他标记为 static const。在定义变量的地方，将值设置为编译时常量，例如数字或字符串字面量，const变量或者对常数进行算术运算的结果。
const bar = 1000000; // Unit of pressure (dynes/cm2)
const double atm = 1.01325 * bar; // Standard atmosphere

const 关键字并不仅仅用来声明常量变量。你也可以使用它来创建常量值，以及声明创建常量值的构造函数。任何变量都可以具有常量值。
var foo = const [];
final bar = const [];
const baz = []; // Equivalent to const []

你可以从const声明的初始化表达式中省略const，例如上面的 baz 变量。
你可以改变一个非 final，const变量的值，即使它曾经有一个 const 值
foo = [1, 2, 3]; // Was const []

你不能改变一个 const 变量的值：
baz = [42]; // Error: Constant variables can't be assigned a value.



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• <div><p>Dart2Native is released. Please update your benchmark and use Dart2Native in place of Dart VM</p><p>该提问来源于开源项目：costajob/app-servers</p></div>
• ## Dart2教程

千次阅读 2018-10-11 10:36:13
本教程是基于官网的Tour教程，...Dart2简介 Dart2安装 第一个Dart2程序 数据类型和变量 List、Set和Map Symbol和Rune 运算符 条件判断和循环 函数 异常处理 类和枚举 泛型 库和可见性 异步 未完待续......
本教程是基于官网的Tour教程，以及其他的一些官方文档，其实也就是一个个人学习笔记。

教程中的部分代码来自于官方文档，另一部分是个人写的。

Dart2简介
Dart2安装
第一个Dart2程序
数据类型和变量
List、Set和Map
Symbol和Rune
运算符
条件判断和循环
函数
异常处理
类和枚举
泛型
库和可见性
异步
未完待续...
展开全文
• Packt Learning Dart 2nd Edition，学习Dart语言，以Web网页游戏为教程，介绍了Dart语言的强大功能，最新版：第二版
• dart dart2 区别 内部AI (Inside AI) This is a paper that came out in the midst of 2018, addresses the problem of scalability of searching a network architecture. These papers address the problem of ...
dart dart2 区别 内部AI (Inside AI)
This is a paper that came out in the midst of 2018, addresses the problem of scalability of searching a network architecture. These papers address the problem of Neural Architecture Search or NAS in short. 该论文于2018年中期发布，解决了搜索网络架构的可扩展性问题。 简而言之，这些论文解决了神经体系结构搜索或NAS的问题。
As the name suggests, the idea behind this field is to explore how can we automatically search deep learning model architectures. Currently, most of the data science problems are solved by manually designing the model architecture which gives “state of the art” results on any given dataset. The problem with this approach is that, though these architectures perform really good on the standard datasets, they don’t perform as expected on the organisation specific datasets. 顾名思义，该领域的思想是探索如何自动搜索深度学习模型架构。 当前，大多数数据科学问题是通过手动设计模型体系结构解决的，该模型体系结构可在任何给定的数据集上提供“ 最先进的 ”结果。 这种方法的问题在于，尽管这些体系结构在标准数据集上确实表现出色，但它们在组织特定数据集上的表现却不如预期。
Unet-Architecture | Top Right: Original Image published in [LeCun et al., 1998] | Bottom left: VGG16 Architecture | Bottom Right: ResNet architectureUnet建筑 | 右上：[LeCun等，1998]发表的原始图像| 左下：VGG16架构| 右下：ResNet架构 This article is for those who are stepping into NAS research or reading this wonderful paper. I worked on this field as an internship project at the Indian Space Research Organisation (ISRO). In this blog, I will try to explain this paper in an intuitive manner as I experienced a lot of difficulties when I implemented it for semantic segmentation. 本文适用于那些正在从事NAS研究或阅读本文的人。 我在印度航天研究组织(ISRO)的实习项目中从事该领域的工作。 在此博客中，由于将其用于语义分割时遇到了很多困难，因此我将尝试以一种直观的方式来解释本文。
神经架构搜索(NAS)简介 (Introduction to Neural Architecture Search (NAS))
The problem of neural architecture search is posed as follows. 神经体系结构搜索的问题如下。
Given a set of search space operation O, we need to find the combination of these operations that maximizes or minimizes the objective function. 给定一组搜索空间操作O，我们需要找到这些操作的组合，以使目标函数最大化或最小化。
Dog Image cc-by: Von.grzanka | Image by Author | Animation showing how different operations affect output. 狗图片抄送：Von.grzanka | 图片作者 显示不同操作如何影响输出的动画。 In simple words, we need to find the architecture of the model to minimize the loss. 简而言之，我们需要找到模型的架构以最小化损失。
天真的解决方案 (Naïve Solution)
A naive solution to NAS is trail and error. We will randomly select a subset of operation and evaluate its performance on a parameter like validation loss and select the model configuration with the best performance. NAS的幼稚解决方案是走错了路。 我们将随机选择一个操作子集，并根据诸如验证损失之类的参数评估其性能，然后选择性能最佳的模型配置。
NAS的发展简史 (Brief History of progress in NAS)
We will not get into depth, but here are some influential papers that paved the way for NAS research. 我们不会深入探讨，但是这里有一些有影响力的论文为NAS研究铺平了道路。
Neural Architecture Search with Reinforcement Learning 神经架构搜索与强化学习 Efficient Neural Architecture Search via Parameter Sharing 通过参数共享进行有效的神经体系结构搜索 Progressive Neural Architecture Search 渐进式神经架构搜索 DARTS: Differentiable Architecture Search DARTS：差异化架构搜索 And then recently there is HNAS: Hierarchical Neural Architecture Search on Mobile Devices, which extended the idea for DARTS to next level. 然后是最近的HNAS：移动设备上的分层神经体系结构搜索 ，将DARTS的思想扩展到了另一个层次。
The trend in the research has been to decrease the computation time from 2000 GPU days of reinforcement learning or 3150 GPU days of evolution to 2–3 GPU days for DARTS. 该研究的趋势是将计算时间从2000年GPU增强学习天数或3150 GPU演化天数减少到DARTS的2–3 GPU天数。
NAS的方法 (Approach for NAS)
The idea of searching for high performing model architecture is not trivial and involves two steps. 搜索高性能模型体系结构的想法并不简单，涉及两个步骤。
Searching the cell architecture on a small dataset (e.g. CIFAR10 or CIFAR100) 在小型数据集(例如CIFAR10或CIFAR100)上搜索单元架构 Making model from searched cell architecture and training it on a big dataset (e.g. ImageNet) 从搜索到的单元架构中构建模型并在大型数据集(例如ImageNet)上对其进行训练  搜索单元架构 (Searching Cell Architectures)
Image by Author | Structure of a Simple Cell and mixed operations. The cell above shown has 3 states in a stacked fashion. 图片作者 简单单元的结构和混合操作。 上面显示的单元以堆叠方式具有3个状态。 What is a cell in a model anyways? Well, a cell can be considered a special block where layers are stacked just like any other model. These cells apply many convolution operations to get feature maps which can be passed over to other cells. A model is made by stacking these cells in a series to make a complete model. All these papers follow a pattern, where two types of cell structure are searched, namely Normal Cell and Reduction Cell. 无论如何，模型中的单元是什么？ 好吧，像其他任何模型一样，单元可以被认为是一个特殊的块，其中层被堆叠。 这些单元应用许多卷积运算以获得可以传递给其他单元的特征图。 通过将这些单元格堆叠成一个完整的模型来制作模型。 所有这些论文都遵循一种模式，即搜索两种类型的细胞结构，即正常细胞和还原细胞 。
Image by Author | Normal Cell 图片作者 正常细胞 Normal Cell: Normal Cell can be thought of a normal block which computes the feature map of an image. Convolutions and poolings in this block have a stride of 1. 普通单元 ：普通单元可以认为是计算图像特征图的普通块。 此块中的卷积和池化的步幅为1 。
Image by Author | Reduction Cell 图片作者 还原池 Reduction Cell: Reduction Cell can be thought of normal block which reduced the feature map dimensions. Convolutions and poolings in this block have a stride of 2. The purpose of the reduction cell is to downsample the feature maps. 约简单元：约简单元可以看作是正常的块，从而减小了特征图的尺寸。 此块中的卷积和合并步幅为2 。 约简单元的目的是对特征图进行下采样。
Since all these papers tackle the classification problem, a global average pooling layer is used at last along with optional fully connected layers. 由于所有这些论文都解决了分类问题，因此最后使用了全局平均池层以及可选的完全连接层。
Image by Author | Normal and Reduction cells stacked to form the final model after the search phase. 图片作者 在搜索阶段之后，将法线和归约单元堆叠以形成最终模型。  有关DARTS的详细信息。 怎么样呢？ (Details about DARTS. How it is better?)
Darts is a very influential paper in neural architecture search. Earlier methods used reinforcement learning and required a large number of computational resources. It took 2000 GPU days of reinforcement learning or 3150 GPU days of evolution. This computation time is not at all feasible by most of the organizations. Dart是神经体系结构搜索中非常有影响力的论文。 早期的方法使用强化学习，并且需要大量的计算资源。 花费了2000 GPU天的强化学习或3150 GPU天的演变。 对于大多数组织来说，这种计算时间根本不可行。
“In this work, we approach the problem from a different angle and propose a method for efficient architecture search called DARTS (Differentiable Architecture Search). Instead of searching over a discrete set of candidate architectures, we relax the search space to be continuous, so that the architecture can be optimized with respect to its validation set performance by gradient descent. “ 在这项工作中，我们从另一个角度解决了这个问题，并提出了一种称为 DARTS (可区分架构搜索)的 有效架构搜索方法 。 代替在离散的候选架构集上进行搜索，我们放宽了搜索空间以使其成为连续的，以便可以通过梯度下降来针对其验证集性能对架构进行优化。
The data efficiency of gradient-based optimization, as opposed to inefficient black-box search, allows DARTS to achieve competitive performance with the state of the art using orders of magnitude fess computation resources. 与效率低下的黑匣子搜索相反，基于梯度的优化的数据效率使DARTS使用数量级的fess计算资源，可以与现有技术保持竞争优势。
We introduce a novel algorithm for differentiable network architecture search based on bilevel optimization, which is applicable to both convolutional and recurrent architectures.” — source: DARTS Paper 我们介绍了一种基于双层优化的可区分网络体系结构搜索的新颖算法，该算法适用于卷积和循环体系结构。” —来源：DARTS Paper
DARTS reduced the search time to 2–3 GPU days which is phenomenal. DARTS将搜索时间减少到2–3 GPU天 ，这是非常惊人的。
DARTS如何做到这一点？ (How does DARTS do this?)
Searching over a discrete set of candidate operations is computationally heavy. 在候选操作的离散集合上进行搜索的计算量很大。 The problem with searching over a discrete set of candidate operations is that model has to be trained on specific configuration before moving onto the next configuration. This obviously is time-consuming. Authors found a way of relaxing the discrete set of candidate operations. 搜索一组离散的候选操作的问题在于，在移至下一个配置之前，必须在特定配置上训练模型。 这显然很耗时。 作者发现了一种放松候选操作离散集的方法。
“To make the search space continuous, we relax the categorical choice of a particular operation to a softmax over all possible operations:” — DARTS paper “为使搜索空间连续，我们将特定操作的分类选择放宽到所有可能操作上的softmax：” — DARTS论文 Equation from DARTS paper DARTS的公式 Well what this means is that assume we have few operations in our candidate operations namely 那么这意味着假设我们在候选操作中几乎没有操作
O = {conv_3x3, max_pool_3x3, dilated_conv_5x5}. O = {conv_3x3，max_pool_3x3，dilated_conv_5x5}。
The output of the operation is called mixed operation and is defined by multiplying the output from these operations to their probabilities. 该操作的输出称为混合操作，通过将这些操作的输出乘以其概率来定义。
Image by Author | Image showing how Mixed Operation is computed. 图片作者 该图显示了如何计算混合运算。 “Each intermediate node is computed based on all of its predecessors.” — DARTS paper “每个中间节点都是基于其所有前任进行计算的。” — DARTS纸 Equation from DARTS paper DARTS的公式 Image by Author | Typical NAS Cell | Note how each node has output from all the previous nodes as its input. 图片作者 典型的NAS单元| 注意每个节点如何将所有先前节点的输出作为其输入。 This brings us to the structure of the cell of DARTS. This is the core structure of the model I want you to give a good focus here. 这使我们进入了DARTS单元的结构。 这是模型的核心结构，我希望您在此重点关注。
The cell contains one or more nodes. These nodes are also known as states. 该单元包含一个或多个节点。 这些节点也称为状态。
nput to a cell is outputs of the last two-cell, just like ResNets. In this cell there are nodes. Let us assume we make a cell with 3 states/nodes. So the first node will have two inputs i.e outputs from the last two cells. 像ResNets一样，输入到一个单元的是最后两个单元的输出。 在这个单元中有节点。 假设我们制作一个具有3个状态/节点的单元。 因此，第一个节点将有两个输入，即最后两个单元的输出。
The second state will have inputs from the first state, and outputs from the last two cells so total 3 inputs. 第二个状态的输入来自第一个状态 ， 最后两个单元的输出则总计3个输入。
The third state will have inputs from the second state, the first state and outputs from the last two cells. 第三状态将具有来自第二状态，第一状态的输入和来自最后两个单元的输出。
At the end of the search, a discrete architecture can be obtained by replacing each mixed operation o(i,j) with most likely operation i.e. 在搜索结束时，可以通过将每个混合操作o(i，j)替换为最可能的操作(即
Equation from DARTS paper. DARTS论文中的公式。 DARTS PaperDARTS Paper This sound complex, but let’s break it down. After searching phase is over, we can find the architecture of a cell by getting top k (generally k=2) connections from the cell. This way discrete search space is converted to continuous search space on which gradient descent algorithm will work fine. 这听起来很复杂，但让我们对其进行分解。 搜索阶段结束后，我们可以通过从单元中获取前k个(通常为k = 2)连接来找到单元的体系结构。 这样，离散搜索空间将转换为连续搜索空间，在该连续搜索空间上梯度下降算法将可以正常工作。
2. Bilevel Optimization 2. 双层优化
“After relaxation, our goal is to jointly learn the architecture α and the weights w within all the mixed operations (e.g. weights of the convolution filters).” — DARTS Paper “放松之后，我们的目标是共同学习所有混合操作中的体系结构α和权重w(例如卷积滤波器的权重)。” — DARTS纸 We have discussed how can we obtain the searched architecture. But how does this model search the optimal operation is still a question unanswered. The training part is still left. 我们已经讨论了如何获得搜索的体系结构。 但是，该模型如何搜索最佳操作仍未解决。 训练部分仍然保留。
The optimization problem can be posed as to finding alphas so that validation loss is minimized given that we have weights that are already optimized on the training set. 可以针对查找alpha提出优化问题，以便在我们具有已在训练集上进行了优化的权重的情况下，将 验证损失降至最低 。
bilevel optimization.双层优化。  近似建筑梯度-房间里的大象 (Approximate Architecture Gradient — The elephant in the room)
“Evaluating the architecture gradient exactly can be prohibitive due to the expensive inner optimization. We, therefore, propose a simple approximation scheme as follows:” DARTS Paper 由于昂贵的内部优化，准确评估架构梯度可能会令人望而却步。 因此，我们提出一种简单的近似方案，如下所示： Image by Author | Equation showing gradients of alphas 图片作者 显示Alpha梯度的方程式 1st (Cat) Photo by Loan on Unsplash, Second (Dog) Photo by Victor Grabarczyk on Unsplash, Third (Dog) Photo by Alvan Nee on Unsplash | Image by Author | Notice how changing alphas (orange lines) change the training loss (top graph) and retraining till convergence has to be done on weight. Optimizing over alphas need optimized weights first. 第一(猫)照片由Unsplash上的Loan 拍摄 ，第二(狗)照片由Victor Grabarczyk在Unsplash上照片 ，第三(狗)照片由Alvan Nee在Unsplash上拍摄 | 图片作者 请注意，不断变化的alpha(橙色线)如何改变训练损失(上图)和重新训练，直到必须权重完成收敛为止。 对Alpha进行优化需要首先优化权重。 There is a computational problem in this equation. To get optimal convolution weights we need to train the network by minimizing training loss by updating convolution weights. This means every time alpha is updated minimization of training step is required. This will make network training infeasible. 该方程式存在计算问题。 为了获得最佳的卷积权重，我们需要通过更新卷积权重来最小化训练损失来训练网络。 这意味着每次更新alpha时都需要最小化训练步骤。 这将使网络培训不可行。
“The idea is to approximate w*(α) by adapting w using only a single training step, without solving the inner optimization completely by training until convergence.” “我们的想法是仅使用一个训练步骤就可以通过调整w来近似w *(α)，而无需通过训练直至收敛来完全解决内部优化问题。” Equation from DARTS paper DARTS的公式 In equation 5 getting optimal weight w* for each configuration of alpha leads to two loops of optimization, so authors suggested to approximate w* in such a way that there is no need to optimize w* till convergence. The idea is to use just one training step instead of whole inner optimization loop. 在等式5中，对于每种alpha配置，获得最佳权重w *会导致两个优化循环，因此作者建议以一种这样的方式来近似w *，即在收敛之前无需优化w *。 想法是只使用一个训练步骤，而不是整个内部优化循环。
Image from DARTS Paper 图片来自DARTS Paper Click here to understand the math behind these equations. 单击此处了解这些方程背后的数学公式。
Looking at equation 7, we have a second-order partial derivative which is computationally expensive to compute. To solve this, the finite difference method is used. 看方程7，我们有一个二阶偏导数，计算起来计算起来很昂贵。 为了解决这个问题，使用了有限差分法。
Look, at equation 8 there is no second-order partial derivative! 看，在等式8中没有二阶偏导数！
For results, you can refer to paper here. 有关结果，请参阅此处的论文 。
替代优化策略 (Alternative Optimization Strategies)
Authors also tried to optimize alphas and weights jointly on training+validation data, but results deteriorated. The authors explained that this could be due to overfitting of alphas on the data. 作者还尝试根据训练和验证数据共同优化Alpha和权重，但结果却恶化了。 作者解释说，这可能是由于数据中Alpha的过度拟合。
结论 (Conclusion)
DARTS was a very influential paper that drastically reduced the time of searching a high performing architecture from thousands of GPU hours to just 2–3 GPU days and still achieving state-of-the-art results. DARTS是一篇很有影响力的论文，它极大地减少了搜索高性能架构的时间，从数千个GPU小时减少到只有2-3个GPU天，并且仍然获得了最先进的结果。
资源资源 (Resources)
U-Net: Convolutional Networks for Biomedical Image Segmentation U-Net：用于生物医学图像分割的卷积网络 DARTS: Differentiable Architecture Search DARTS：差异化架构搜索 Neural Architecture Search with Reinforcement Learning 神经架构搜索与强化学习 翻译自: https://towardsdatascience.com/intuitive-explanation-of-differentiable-architecture-search-darts-692bdadcc69cdart dart2 区别
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