compressed sizes of our regular model and its 50% sparse version. We used Tensorflow's save_model() API and zipped the model files using gzip. In addition to the usual models, the figure also shows compressed compute the centroids? The objective function to optimize k-means clustering problems is the within-cluster-sum-of-squares (WCSS) metric. . Here, we are trying to find a set which has centroids , such that Update step: Compute the new centroids by computing the mean of all the points assigned to each cluster. 4. Run steps (2) & (3) until convergence. Notice that this algorithm’s runtime is not deterministic

降低已有tensorflow程序迁移成本：� （1）单机模式 不需要修改代码 （2）分布式模式（最多修改三行代码） cluster = !.train.ClusterSpec(json.loads(os.environ[“TF_CLUSTER_DEF”])) job_name = os.environ[“TF_ROLE”] task_index = int(os.environ["TF_INDEX"]) 设备亲和性影响较大 TensorFlow on Yarn技术细节揭秘 Yarn支持GPU调度ResourceManager端实现：� 扩展org.apache.hadoop.yarn.api.records.Resource抽象类及其实现，增加：� � public abstract int getGpuCores();� � public abstract void setGpuCores(int

Advantages (continued) • Which one do you think is better? PyTorch! • Easy Interface − easy to use API. The code execution in this framework is quite easy. Also need a fewer lines to code in comparison References: • For setting up jupyter notebook on princeton ionic cluster • https://oncomputingwell.princeton.edu/2018/05/jupyter-on-the-cluster/ • Best reference is PyTorch Documentation • https://pytorch

classification without ground truth labels A good clustering is one that achieves: High within-cluster similarity Low inter-cluster similarity Feng Li (SDU) K-Means December 28, 2021 3 / 46 Similarity can be Subjective partition the N observations into K sets (K ≤ N) {Ck}k=1,··· ,K such that the sets minimize the within-cluster sum of squares: arg min {Ck} K � i=1 � x∈Ci ∥x − µi∥2 where µi is the mean of points in set closest cluster center (based on the smallest Euclidean distance) Ck = {xi | ∥xi − µk∥2 ≤ ∥xi − µk′∥2, for ∀k′ ̸= k} (Ck is the set of examples assigned to cluster k with center µk) Update the cluster means

That is, you will compute 16 colors as the cluster centroids and replace each pixel in the image with its nearest cluster centroid color. Because computing cluster centroids on a 538x538 image would be time- computer, you will instead run K-means on the 128×128 image bird small.tiff. Once you have computed the cluster centroids on the small image, you will then use the 16 colors to replace the pixels in the large much in our example because the x and y dimensions have the same size). Your task is to compute 16 cluster centroids from this image, with each centroid being a vector of length three that holds a set of

ensemble.GradientBoostingRegressor 18 2.Scikit-learn主要用法 无监督学习算法 sklearn.cluster模块包含了一系列无监督聚类算法. from sklearn.cluster import KMeans 构建聚类实例 kmeans = KMeans(n_clusters=3, random_state=0) 拟合 kmeans explained_variance_) 20 2.Scikit-learn主要用法 无监督学习算法-聚类 DBSCAN 层次聚类 谱聚类 cluster.DBSCAN cluster.AgglomerativeClustering cluster.SpectralClustering 21 2.Scikit-learn主要用法 评价指标 sklearn.metrics模块包含了一系列用于评价模型的评分函数、损失函

行聚类， 效果不好。 利用随机过程做词 语聚类可以解决这一问题 11 具体的生成cluster的流程如图： V[i]: 为产品信息里每个词的词语向量(word vector)分数 C[i]: 为聚类(cluster)的vector分数 N: 为cluster的数目 Sim(I, j): 词语i 与cluster j的余弦相似度 Random: 生成一个0 – 1之间的随机数 基于词语聚类的矢量化模型

Springer,2006. [9] Rosenberg A, Hirschberg J. V-Measure: A conditional entropy-based external cluster evaluation[C]// Conference on Emnlp-conll. DBLP, 2007. [10] Campello R J G B, Moulavi D, Zimek

SciPy是一款方便、易于使用、专为科学和 工程设计的Python工具包，它包括了统计 、优化、整合以及线性代数模块、傅里叶 变换、信号和图像图例，常微分方差的求 解等 scipy.cluster 向量量化 scipy.constants 数学常量 scipy.fftpack 快速傅里叶变换 scipy.integrate 积分 scipy.interpolate 插值 scipy

SciPy是一款方便、易于使用、专为科学和 工程设计的Python工具包，它包括了统计 、优化、整合以及线性代数模块、傅里叶 变换、信号和图像图例，常微分方差的求 解等 scipy.cluster 向量量化 scipy.constants 数学常量 scipy.fftpack 快速傅里叶变换 scipy.integrate 积分 scipy.interpolate 插值 scipy