sequential

I'm trying to animate in elements sequentially in full css3 animations. Seems the very straight forward answer is using animation delay. However I wanted this in loop, any ideas how to make the animation loop infinitely? I found this fiddle on a similar question. Basically that's the same logic but I just wanted it looped. This was the similar [question] ( https://stackoverflow.com/a/8294491/340888 ) Was using this: @-webkit-keyframes FadeIn { 0% { opacity:0; -webkit-transform:scale(.1);} 85% {opacity:1; -webkit-transform:scale(1.05);} 100% {-webkit-transform:scale(1); } } .myClass img { float

1.stream.forEach() 与 collection.forEach() 虽然都是迭代方法，但执行结果完全不同。 如： List<String> strl=Arrays.asList("aaa","bb","c","wwww","hh"); Stream.of(strl).forEach(System.out::println);------------------------------------------------------>①System.out.println("^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^");strl.stream().forEach(System.out::println);-------------------------------------------------------->②System.out.println("^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^");strl.forEach(System.out::println);----------------------------------------------------------------->③ 输出结果如下： [aaa, bb, c, hh, wwww] ^^^^^^^^^^^^^^^^^^^

I am new to keras. I was attempting an ML problem. About the data: It has 5 input features, 4 output classes and about 26000 records. I had first attempted it using MLPClassifier() as follows: clf = MLPClassifier(verbose=True, tol=1e-6, batch_size=300, hidden_layer_sizes=(200,100,100,100), max_iter=500, learning_rate_init= 0.095, solver='sgd', learning_rate='adaptive', alpha = 0.002) clf.fit(train, y_train) After testing, I usually got a LB score around 99.90. To gain more flexibility over the model, I decided to implement the same model in Keras to start with and then make changes in it in an

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: As I see it, the obvious code, when using Java 8 Stream s, whether they be "object" streams or primitive streams (that is, IntStream and friends) would be to just use: someStreamableResource.stream().whatever() But then, quite a few "streamable resources" also have .parallelStream() . What isn't clear when reading the javadoc is whether .stream() streams are always sequential, and whether .parallelStream() streams are always parallel... And then there is Spliterator , and in particular its .characteristics() , one of them being that it can

导入同样导入之前的包或者模块 生成数据集 通过pytorch读取数据 定义模型 初始化模型 定义损失函数 定义优化算法 训练模型 小结 本节利用pytorch中的模块，生成一个更加简洁的代码来实现同样的功能 导入同样导入之前的包或者模块 %matplotlib inline import torch from IPython import display from matplotlib import pyplot as plt import numpy as np import random 生成数据集 num_inputs =2 ## 特征数量 num_examples=1000 # 样本量 true_w=[2,-3.4] # 真实的权重系数 true_b=4.2 # 真实的偏置量 features = torch.randn(num_examples,num_inputs,dtype=torch.float32) # 生成随机的特征 labels = true_w[0]*features[:,0]+true_w[1]*features[:,1]+true_b # 生成随机的标签 labels += torch.tensor(np.random.normal(0,0.01,size=labels.size()),dtype=torch.float32) #在标签上加上随机噪声项

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: When creating a Sequential model in Keras, I understand you provide the input shape in the first layer. Does this input shape then make an implicit input layer? For example, the model below explicitly specifies 2 Dense layers, but is this actually a model with 3 layers consisting of one input layer implied by the input shape, one hidden dense layer with 32 neurons, and then one output layer with 10 possible outputs? model = Sequential([ Dense(32, input_shape=(784,)), Activation('relu'), Dense(10), Activation('softmax'), ]) 回答1: Well, it

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I am trying to write a multithreaded program in Python to accelerate the copying of (under 1000) .csv files. The multithreaded code runs even slower than the sequential approach. I timed the code with profile.py . I am sure I must be doing something wrong but I'm not sure what. The Environment: Quad core CPU. 2 hard drives, one containing source files. The other is the destination. 1000 csv files ranging in size from several KB to 10 MB. The Approach: I put all the file paths in a Queue, and create 4-8 worker threads pull file paths from the

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I have an example of a neural network with two layers. The first layer takes two arguments and has one output. The second should take one argument as result of the first layer and one additional argument. It should looks like this: x1 x2 x3 \ / / y1 / \ / y2 So, I'd created a model with two layers and tried to merge them but it returns an error: The first layer in a Sequential model must get an "input_shape" or "batch_input_shape" argument. on the line result.add(merged) . Model: first = Sequential() first.add(Dense(1, input_shape=(2,),

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I' am getting this error when use imagecreatefromjpeg function. Warning: imagecreatefromjpeg(): gd-jpeg, libjpeg: recoverable error: Invalid SOS parameters for sequential JPEG in C:\wamp\www\test\index.php on line 7 I researched, with this commands pass errors fine, works good. ini_set("gd.jpeg_ignore_warning", 1); error_reporting(E_ALL & ~E_NOTICE); But I am wondering what is wrong this? here my image exif data Array ( [FileName] => 1.jpg [FileDateTime] => 1455186386 [FileSize] => 1364340 [FileType] => 2 [MimeType] => image/jpeg