sequential | 易学教程

jQuery sequential functions one after the other in a each loop

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问题 I know this topic has been discussed in a couple of posts but I can't seem to get my head around doing this for my particular use case. So I have a need to simulate the typing of values into inputs on a page one after the other. The user simply sits and watches - but essentially what I need is on load, the first input focuses > types in letter by letter a value, and then moves to the next input to do the same with it's value. Here is a fiddle of what I mean with my code so far: https:/

Keras Sequential Model-SGD- Neural Network-NLTK

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问题 creating a bot, Here i faced error after training. i trained using the Keras SEQUENTIAL Model, SGD Optimizer, NLTK lemmatizer =WordNetLemmatizer() words =pickle.load(open("words.pkl",'rb'))# reading binary mode classes= pickle.load(open("classes.pkl",'rb')) model =load_model('chatbot.model') print(classes) def clean_up_sentence(sentence): sentence_words =nltk.word_tokenize(sentence) sentence_words=[lemmatizer.lemmatize(word) for word in sentence_words] return sentence_words def bag_of_words

Execute and wait for multiple parallel and sequential Tasks by using a Arraylist of Tasks in JavaFX

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问题 I'm looking for a suitable way to display the processing time of parallel running Tasks on a separate stage. I want to execute different tasks combined in an ArrayList - one after the other. For this case I'm using a ThreadPool. After each executed list, I want to wait until all tasks are completed. Only when the tasks have reached the status „succeeded“, I want to do something in the MainThread. After that I want to execute another list of tasks and visualize them on a separate stage as well

Variable length input for a simple neural network (Keras)

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问题 Can a variable length, i.e. input_dim=None , be applied to a simple neural network? Specifically, the Keras Sequential model. I've been running into errors when trying to employ the same concept. I've already seen the documentation that seems to support this functionality: https://keras.io/getting-started/functional-api-guide/ But when I do the following... model = Sequential() model.add(Dense(num_feat, input_dim = None, kernel_initializer = 'normal', activation='relu')) model.add(Dense(num

Variable length input for a simple neural network (Keras)

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keras sequential() 模型

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本文是我在阅读 https://keras.io/getting-started/sequential-model-guide/ 时所做的一些笔记 sequential()主要有以下几个步骤。一、网络的构建第一种，直接在函数中传递层的实例 from keras.models import Sequential from keras.layers import Dense, Activation model = Sequential([ Dense(32, input_shape=(784,)), Activation('relu'), Dense(10), Activation('softmax'), ]) 第二种，利用.add()方法一层一层地累加 1.1 定义input shape model = Sequential() model.add(Dense(32, input_dim=784)) model.add(Activation('relu')) 二维的网络如Dense()可通过input_dim，三维为input_dim，input_length。为定义输入的batch size（对于含状态量的RNN适用）可向layer传递batch_size参数，如果同时传递了batch_size=32和input_shape=(6, 8)，将会认为每批输入都具有(32, 6,

VGG,NIN and GoogLeNet

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第一次使用kaggle的notebook，可以免费使用gpu还是挺爽的，不过就是不了解读取数据集的路径到底是怎么用的？感觉 /root/Datasets/里面的数据集不是主页面的Datasets 加强记忆:计算卷积，池化的H,W conv:W/H_new = (W/H_old + 2*padding - kernel_size) / stride +1 (padding是上下左右对称加的) pad: W/H_new = (W/H_old - kernel_size) / stride + 1 VGG 使用重复元素的网络 VGG的计算单位是一个Block，每一个Block都有数个:数个相同的填充为1、窗口形状为3 * 3的卷积层,接上一个步幅为2、窗口形状2 * 2的最大池化层。卷积层保持输入的高和宽不变，而池化层则对其减半。 def vgg_block ( num_convs , in_channels , out_channels ) : #卷积层个数，输入通道数，输出通道数 blk = [ ] for i in range ( num_convs ) : if i == 0 : blk . append ( nn . Conv2d ( in_channels , out_channels , kernel_size = 3 , padding = 1 ) ) else :

Pytorch自定义创建BP神经网络

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class BPNet(nn.Module): def __init__(self, in_dim, n_hidden_1, n_hidden_2,\ n_hidden_3, n_hidden_4, n_hidden_5, out_dim): super(BPNet, self).__init__() self.layer1 = nn.Sequential(nn.Linear(in_dim, n_hidden_1)) self.layer2 = nn.Sequential(nn.Linear(n_hidden_1, n_hidden_2), nn.BatchNorm1d(n_hidden_2)) self.layer3 = nn.Sequential(nn.Linear(n_hidden_2, n_hidden_3), nn.BatchNorm1d(n_hidden_3), nn.ReLU(True)) self.layer4 = nn.Sequential(nn.Linear(n_hidden_3, n_hidden_4), nn.BatchNorm1d(n_hidden_4), nn.ReLU(True), nn.Dropout(0.1)) self.layer5 = nn.Sequential(nn.Linear(n_hidden_4, n_hidden_5), nn

spark sql : How to achieve parallel processing of dataframe at group level but with in each group, we require sequential processing of rows

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问题 Apply grouping on the data frame. Let us say it resulted in 100 groups with 10 rows each. I have a function that has to be applied on each group. It can happen in parallel fashion and in any order (i.e., it is upto the spark discretion to choose any group in any order for execution). But with in group, I need the guarantee of sequential processing of the rows. Because after processing each row in a group, I use the output in the processing of any of the rows remaining in the group. We took

PyTorch【7】-nn.Sequential

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nn.Sequential 是一个有序的容器；它的输入是一个神经网络模块的有序序列，或者是由模块名和神经网络模块组成的有序字典；代表模型的输入（样本）要有序地经过这些模块，然后得到一个输出；源码解析源码地址 https://pytorch.org/docs/stable/_modules/torch/nn/modules/container.html#Sequential class Sequential(Module): r"""A sequential container. Modules will be added to it in the order they are passed in the constructor. Alternatively, an ordered dict of modules can also be passed in. To make it easier to understand, here is a small example:: # Example of using Sequential model = nn.Sequential( nn.Conv2d(1,20,5), nn.ReLU(), nn.Conv2d(20,64,5), nn.ReLU() ) # Example of using Sequential with

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