sequential

I have some side-effectful function, def f(): Future[Int] = { val n = Random.nextInt() println(s"Generated $n") Future(n) } and I want to execute it repeatedly until predicate returns true. def success(n: Int): Boolean = n % 2 == 0 My plan is to build Stream of results val s = Stream.fill(10)(f) and then use Future.find to get the first result that satisfies predicate. Future.find(s)(success) map println The problem is that Future.find runs all the futures in parallel and I want it to execute futures sequentially one after the other until predicate returns true. scala> Future.find(s)(success)

I have a sequential file which is the output of hadoop map-reduce job. In this file data is written in key value pairs ,and value itself is a map. I want to read the value as a MAP object so that i can process it further. Configuration config = new Configuration(); Path path = new Path("D:\\OSP\\sample_data\\data\\part-00000"); SequenceFile.Reader reader = new SequenceFile.Reader(FileSystem.get(config), path, config); WritableComparable key = (WritableComparable) reader.getKeyClass().newInstance(); Writable value = (Writable) reader.getValueClass().newInstance(); long position = reader

I'm trying to achieve the following replacement in python. Replace all html tags with {n} & create a hash of [tag, {n}] Original string -> " <h> This is a string. </H><P> This is another part. </P> " Replaced text -> "{0} This is a string. {1}{2} This is another part. {3}" Here's my code. I've started with replacement but I'm stuck at the replacement logic as I cannot figure out the best way to replace each occurrence in a consecutive manner i.e with {0}, {1} and so on: import re text = "<h> This is a string. </H><p> This is another part. </P>" num_mat = re.findall(r"(?:<(\/*)[a-zA-Z0-9]+>)"

I have the following jQuery: $("#div1").animate({ width: '160' }, 200).animate({ width: 'toggle' }, 300 ); $("#div2").animate({ width: 'toggle' }, 300).animate({ width: '150' }, 200); My issue is that both happen at the same time. I would like the div2 animation to start when the first one finishes. I've tried the method below, but it does the same thing: $("#div1").animate({ width: '160' }, 200).animate({ width: 'toggle' }, 300, ShowDiv() ); .... function ShowDiv(){ $("#div2").animate({ width: 'toggle' }, 300).animate({ width: '150' }, 200); } How can I make it wait for the first one to

【AI算法推荐】：tensorflow2.0建模教程系列产品 【阅读推荐】 在tensorflow2.0模型系列产品实例教程中，前四节人们用编码演译了： 系列产品1：怎样用tf2.0开展自定层互联网的布置（add.weight） 系列产品2：怎样用tf2.0开展自定实体模型的布置（Model） 系列产品3：怎样用tf2.0保持loss涵数和主要参数调优（loss gradient optimizer） 系列产品4：.怎样用tf2.0保持损失函数正则化，处理实体模型过拟合难题 前边几章节目录，人们重中之重学了怎样用tensorflow2.0进行自定层和互联网实体模型的布置。从这节起，将领着大伙儿把握tf2.0方便快捷的建模工具API——tensorflow.keras 。 Keras 是1个用以搭建和训炼深度神经网络实体模型的进阶 API。它可用以迅速布置原形、高級科学研究和生产制造。相对性于自定布置层和互联网，应用tf.keras 更为方便快捷，专业化，便于拓展。普遍的LSTM/RNN/Conv2D等神经元网络都包括在 keras.layer 中。 《一 根据keras的简单网络设计模型》 keras.sequential()层互联网的层叠，keras.Sequential实体模型 model = tf.keras.Sequential() model.add(layers

目录 Method 1 Method 2 Method 3 Method 4 Reference 网络结构： conv --> relu --> pool --> FC -- > relu --> FC 导入包 import torch import torch.nn.functional as F from collections import OrderedDict from torchsummary import summary Method 1 class Net1(torch.nn.Module): def __init__(self): super(Net1, self).__init__() self.conv1 = torch.nn.Conv2d(3, 32, 3, 1, 1) self.dense1 = torch.nn.Linear(32 * 3 * 3, 128) self.dense2 = torch.nn.Linear(128, 10) def forward(self, x): # [2, 3, 6, 6] x = F.max_pool2d(F.relu(self.conv1(x)), 2) x = x.view(x.size(0), -1) x = F.relu(self.dense1(x)) x = self.dense2(x) return x

keras是基于tensorflow封装的的高级API，Keras的优点是可以快速的开发实验，它能够以 TensorFlow , CNTK , 或者 Theano 作为后端运行。 模型构建 最简单的模型是 Sequential 顺序模型 ，它由多个网络层线性堆叠。对于更复杂的结构，你应该使用 Keras 函数式 API ，它允许构建任意的神经网络图。 用Keras定义网络模型有两种方式， Sequential 顺序模型 Keras 函数式 API模型 1、Sequential 顺序模型 from keras.models import Sequential model = Sequential() 我们可以通过将网络层实例的 列表 传递给 Sequential 的构造器，来创建一个 Sequential模型，： from keras.models import Sequential from keras.layers import Dense, Activation model = Sequential([ Dense(32, input_shape=(784,)), Activation('relu'), Dense(10), Activation('softmax'), ]) 也可以通过 .add()的方法将各层添加到网络中 from keras.layers import