Mxnet-R-Reference-Manual.Pdf

Total Page：16

File Type：pdf, Size：1020Kb

Package ‘mxnet’ June 24, 2020 Type Package Title MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Sys- tems Version 2.0.0 Date 2017-06-27 Author Tianqi Chen, Qiang Kou, Tong He, Anirudh Acharya <https://github.com/anirudhacharya> Maintainer Qiang Kou <[email protected]> Repository apache/incubator-mxnet Description MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix the flavours of deep learning programs together to maximize the efficiency and your productivity. License Apache License (== 2.0) URL https://github.com/apache/incubator-mxnet/tree/master/R-package BugReports https://github.com/apache/incubator-mxnet/issues Imports methods, Rcpp (>= 0.12.1), DiagrammeR (>= 0.9.0), visNetwork (>= 1.0.3), data.table, jsonlite, magrittr, stringr Suggests testthat, mlbench, knitr, rmarkdown, imager, covr Depends R (>= 3.4.4) LinkingTo Rcpp VignetteBuilder knitr 1 2 R topics documented: RoxygenNote 7.1.0 Encoding UTF-8 R topics documented: arguments . 15 as.array.MXNDArray . 15 as.matrix.MXNDArray . 16 children . 16 ctx.............................................. 16 dim.MXNDArray . 17 graph.viz . 17 im2rec . 18 internals . 19 is.mx.context . 19 is.mx.dataiter . 20 is.mx.ndarray . 20 is.mx.symbol . 21 is.serialized . 21 length.MXNDArray . 21 mx.apply . 22 mx.callback.early.stop . 22 mx.callback.log.speedometer . 23 mx.callback.log.train.metric . 23 mx.callback.save.checkpoint . 24 mx.cpu . 24 mx.ctx.default . 24 mx.exec.backward . 25 mx.exec.forward . 25 mx.exec.update.arg.arrays . 26 mx.exec.update.aux.arrays . 26 mx.exec.update.grad.arrays . 27 mx.gpu . 27 mx.infer.rnn . 28 mx.infer.rnn.one . 28 mx.infer.rnn.one.unroll . 29 mx.init.create . 29 mx.init.internal.default . 30 mx.init.normal . 30 mx.init.uniform . 30 mx.init.Xavier . 31 mx.io.arrayiter . 31 mx.io.bucket.iter . 32 mx.io.CSVIter . 32 mx.io.extract . 34 mx.io.ImageDetRecordIter . 34 mx.io.ImageRecordInt8Iter . 38 R topics documented: 3 mx.io.ImageRecordIter . 41 mx.io.ImageRecordIter_v1 . 44 mx.io.ImageRecordUInt8Iter . 48 mx.io.ImageRecordUInt8Iter_v1 . 51 mx.io.LibSVMIter . 54 mx.io.MNISTIter . 56 mx.io.RandomSampler . 57 mx.io.SequentialSampler . 57 mx.io.ThreadedDataLoader . 58 mx.kv.create . 59 mx.lr_scheduler.FactorScheduler . 59 mx.lr_scheduler.MultiFactorScheduler . 60 mx.metric.accuracy . 60 mx.metric.custom . 61 mx.metric.logistic_acc . 61 mx.metric.logloss . 61 mx.metric.mae . 62 mx.metric.mse . 62 mx.metric.Perplexity . 62 mx.metric.rmse . 63 mx.metric.rmsle . 63 mx.metric.top_k_accuracy . 63 mx.model.buckets . 64 mx.model.FeedForward.create . 64 mx.model.init.params . 66 mx.model.load . 67 mx.model.save . 67 mx.nd.abs . 68 mx.nd.Activation . 68 mx.nd.adam.update . 69 mx.nd.add.n . 70 mx.nd.all.finite . 70 mx.nd.amp.cast . 71 mx.nd.amp.multicast . 71 mx.nd.arccos . 72 mx.nd.arccosh . 72 mx.nd.arcsin . 73 mx.nd.arcsinh . 73 mx.nd.arctan . 74 mx.nd.arctanh . 74 mx.nd.argmax . 75 mx.nd.argmax.channel . 75 mx.nd.argmin . 76 mx.nd.argsort . 77 mx.nd.array . 77 mx.nd.batch.dot . 78 mx.nd.batch.take . ..

518

Copy Link

Recommended publications

Neural Networks (AI) (WBAI028-05) Lecture Notes
Herbert Jaeger Neural Networks (AI) (WBAI028-05) Lecture Notes V 1.5, May 16, 2021 (revision of Section 8) BSc program in Artificial Intelligence Rijksuniversiteit Groningen, Bernoulli Institute Contents 1 A very fast rehearsal of machine learning basics 7 1.1 Training data. .............................. 8 1.2 Training objectives. ........................... 9 1.3 The overfitting problem. ........................ 11 1.4 How to tune model flexibility ..................... 17 1.5 How to estimate the risk of a model .................. 21 2 Feedforward networks in machine learning 24 2.1 The Perceptron ............................. 24 2.2 Multi-layer perceptrons ......................... 29 2.3 A glimpse at deep learning ....................... 52 3 A short visit in the wonderland of dynamical systems 56 3.1 What is a “dynamical system”? .................... 58 3.2 The zoo of standard finite-state discrete-time dynamical systems .. 64 3.3 Attractors, Bifurcation, Chaos ..................... 78 3.4 So far, so good ... ............................ 96 4 Recurrent neural networks in deep learning 98 4.1 Supervised training of RNNs in temporal tasks ............ 99 4.2 Backpropagation through time ..................... 107 4.3 LSTM networks ............................. 112 5 Hopfield networks 118 5.1 An energy-based associative memory ................. 121 5.2 HN: formal model ............................ 124 5.3 Geometry of the HN state space .................... 126 5.4 Training a HN .............................. 127 5.5 Limitations ..............................
[Show full text]
Predrnn: Recurrent Neural Networks for Predictive Learning Using Spatiotemporal Lstms
PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs Yunbo Wang Mingsheng Long∗ School of Software School of Software Tsinghua University Tsinghua University [email protected] [email protected] Jianmin Wang Zhifeng Gao Philip S. Yu School of Software School of Software School of Software Tsinghua University Tsinghua University Tsinghua University [email protected] [email protected] [email protected] Abstract The predictive learning of spatiotemporal sequences aims to generate future images by learning from the historical frames, where spatial appearances and temporal vari- ations are two crucial structures. This paper models these structures by presenting a predictive recurrent neural network (PredRNN). This architecture is enlightened by the idea that spatiotemporal predictive learning should memorize both spatial ap- pearances and temporal variations in a uniﬁed memory pool. Concretely, memory states are no longer constrained inside each LSTM unit. Instead, they are allowed to zigzag in two directions: across stacked RNN layers vertically and through all RNN states horizontally. The core of this network is a new Spatiotemporal LSTM (ST-LSTM) unit that extracts and memorizes spatial and temporal representations simultaneously. PredRNN achieves the state-of-the-art prediction performance on three video prediction datasets and is a more general framework, that can be easily extended to other predictive learning tasks by integrating with other architectures. 1 Introduction
[Show full text]
Jssjournal of Statistical Software MMMMMM YYYY, Volume VV, Issue II
JSSJournal of Statistical Software MMMMMM YYYY, Volume VV, Issue II. http://www.jstatsoft.org/ OSTSC: Over Sampling for Time Series Classification in R Matthew Dixon Diego Klabjan Lan Wei Stuart School of Business, Department of Industrial Department of Computer Science, Illinois Institute of Technology Engineering and Illinois Institute of Technology Management Sciences, Northwestern University Abstract The OSTSC package is a powerful oversampling approach for classifying univariant, but multinomial time series data in R. This article provides a brief overview of the over- sampling methodology implemented by the package. A tutorial of the OSTSC package is provided. We begin by providing three test cases for the user to quickly validate the functionality in the package. To demonstrate the performance impact of OSTSC,we then provide two medium size imbalanced time series datasets. Each example applies a TensorFlow implementation of a Long Short-Term Memory (LSTM) classifier - a type of a Recurrent Neural Network (RNN) classifier - to imbalanced time series. The classifier performance is compared with and without oversampling. Finally, larger versions of these two datasets are evaluated to demonstrate the scalability of the package. The examples demonstrate that the OSTSC package improves the performance of RNN classifiers applied to highly imbalanced time series data. In particular, OSTSC is observed to increase the AUC of LSTM from 0.543 to 0.784 on a high frequency trading dataset consisting of 30,000 time series observations. arXiv:1711.09545v1 [stat.CO] 27 Nov 2017 Keywords: Time Series Oversampling, Classification, Outlier Detection, R. 1. Introduction A significant number of learning problems involve the accurate classification of rare events or outliers from time series data.
[Show full text]
Verification of RNN-Based Neural Agent-Environment Systems
The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) Verification of RNN-Based Neural Agent-Environment Systems Michael E. Akintunde, Andreea Kevorchian, Alessio Lomuscio, Edoardo Pirovano Department of Computing, Imperial College London, UK Abstract and realised via a previously trained recurrent neural net- work (Hausknecht and Stone 2015). As we discuss below, We introduce agent-environment systems where the agent we are not aware of other methods in the literature address- is stateful and executing a ReLU recurrent neural network. ing the formal verification of systems based on recurrent We define and study their verification problem by providing networks. The present contribution focuses on reachability equivalences of recurrent and feed-forward neural networks on bounded execution traces. We give a sound and complete analysis, that is we intend to give formal guarantees as to procedure for their verification against properties specified whether a state, or a set of states are reached in the system. in a simplified version of LTL on bounded executions. We Typically this is an unwanted state, or a bug, that we intend present an implementation and discuss the experimental re- to ensure is not reached in any possible execution. Similarly, sults obtained. to provide safety guarantees, this analysis can be used to show the system does not enter an unsafe region of the state space. 1 Introduction The rest of the paper is organised as follows. After the A key obstacle in the deployment of autonomous systems is discussion of related work below, in Section 2, we introduce the inherent difficulty of their verification. This is because the basic concepts related to neural networks that are used the behaviour of autonomous systems is hard to predict; in throughout the paper.
[Show full text]
Anurag Soni's Blog
ANURAG SONI 447 Sharon Rd Pittsburgh, PA, USA |[email protected] | 765-775-0116 | GitHub | Blog | LinkedIn PROFILE • MS Business Analytics student seeking position in data science and decision-making business units • 5 years of experience as Analyst/Product Mgr. for a technology consulting firm with Investment Banking clients • Skills in figuring out impactful stories with data EDUCATION Purdue University, Krannert School of Management West Lafayette, IN Master of Science in Business Analytics and Information Management May 2018 Indian Institute of Technology Guwahati Guwahati, INDIA Bachelor of Technology, Chemical Engineering May 2011 HIGHLIGHTS • Tools: PL/SQL, Python, R, Excel(VBA), SAS, MATLAB, Tableau, Gurobi • Environment: TensorFlow, Keras, Hadoop, Hive, Docker, Google Cloud, UNIX • Process: Data Mining, SDLC, Process Simulation, Optimization, Data Modeling • Skills: Client-Facing, Strong Communication, Change Management, Team Management, Mentoring EXPERIENCE Purdue BIZ Analytics LAB West Lafayette, IN Associate Analyst(Co-Op) July 2017 – Mar 2018 • Demand Forecasting of supermarket sales: Built a production ready machine-learning solution using - Python deep learning libraries with results expecting to save significant inventory costs • Delivery Rebate Forecast for retailers: Built a forecasting model that supports reporting of weekly expected rebate numbers in an annual tiered delivery contract. • Decision Support System for Music Producer: Implemented dashboard application that uses Spotify datasets to partially cluster songs by their popularity and revenue capability • Production implementation of ML solution: Created an automated machine learning pipeline of an object- detection solution built using TensorFlow on Google Cloud using Docker and Kubernetes technology • Hate Speech Recognition using NLP: Implemented a text mining model that identifies hate speech characteristics in the text.
[Show full text]
Deep Learning Architectures for Sequence Processing
Speech and Language Processing. Daniel Jurafsky & James H. Martin. Copyright © 2021. All rights reserved. Draft of September 21, 2021. CHAPTER Deep Learning Architectures 9 for Sequence Processing Time will explain. Jane Austen, Persuasion Language is an inherently temporal phenomenon. Spoken language is a sequence of acoustic events over time, and we comprehend and produce both spoken and written language as a continuous input stream. The temporal nature of language is reflected in the metaphors we use; we talk of the flow of conversations, news feeds, and twitter streams, all of which emphasize that language is a sequence that unfolds in time. This temporal nature is reflected in some of the algorithms we use to process lan- guage. For example, the Viterbi algorithm applied to HMM part-of-speech tagging, proceeds through the input a word at a time, carrying forward information gleaned along the way. Yet other machine learning approaches, like those we’ve studied for sentiment analysis or other text classification tasks don’t have this temporal nature – they assume simultaneous access to all aspects of their input. The feedforward networks of Chapter 7 also assumed simultaneous access, al- though they also had a simple model for time. Recall that we applied feedforward networks to language modeling by having them look only at a fixed-size window of words, and then sliding this window over the input, making independent predictions along the way. Fig. 9.1, reproduced from Chapter 7, shows a neural language model with window size 3 predicting what word follows the input for all the. Subsequent words are predicted by sliding the window forward a word at a time.
[Show full text]
Deep Learning for Source Code Modeling and Generation: Models, Applications and Challenges
Deep Learning for Source Code Modeling and Generation: Models, Applications and Challenges TRIET H. M. LE, The University of Adelaide HAO CHEN, The University of Adelaide MUHAMMAD ALI BABAR, The University of Adelaide Deep Learning (DL) techniques for Natural Language Processing have been evolving remarkably fast. Recently, the DL advances in language modeling, machine translation and paragraph understanding are so prominent that the potential of DL in Software Engineering cannot be overlooked, especially in the field of program learning. To facilitate further research and applications of DL in this field, we provide a comprehensive review to categorize and investigate existing DL methods for source code modeling and generation. To address the limitations of the traditional source code models, we formulate common program learning tasks under an encoder-decoder framework. After that, we introduce recent DL mechanisms suitable to solve such problems. Then, we present the state-of-the-art practices and discuss their challenges with some recommendations for practitioners and researchers as well. CCS Concepts: • General and reference → Surveys and overviews; • Computing methodologies → Neural networks; Natural language processing; • Software and its engineering → Software notations and tools; Source code generation; Additional Key Words and Phrases: Deep learning, Big Code, Source code modeling, Source code generation 1 INTRODUCTION Deep Learning (DL) has recently emerged as an important branch of Machine Learning (ML) because of its incredible performance in Computer Vision and Natural Language Processing (NLP) [75]. In the field of NLP, it has been shown that DL models can greatly improve the performance of many classic NLP tasks such as semantic role labeling [96], named entity recognition [209], machine translation [256], and question answering [174].
[Show full text]
Julia: Next Generation Language Scipy 2019, Universidad De Los Andes
Julia: Next Generation Language SciPy 2019, Universidad de los Andes Let us understand the necessity: Scientific AI Machine Learning + Domain Power modeling AI & Robotics Team Kiwibot Do I have to learn another language? Current situation: I'm here talking to experts in the scientific python community At the moment we have the two language problem in Scientific AI When I was TA in tools for computing: ● NumPy for numerical linear algebra. ● R for statistical analysis. ● C for the fast stuﬀ ● Bash to tie all up A.k.a "Ousterholt's Dichotomy" "System languages" "Scripting languages" ● Static ● Dynamic ● Compiled ● Interpreted ● User types ● Standard types AI & Robotics Team Kiwibot The Two Language Problem ? Because of this dichotomy, a two-tier compromise is standard: ● For convenience, use a a scripting language (Matlab, R, Python) ● But do all the hard stuﬀ in a system language (C, C++, Fortran) Pragmatic for many applications, but has drawbacks ● Aren't the hard parts exactly where you need an easier language? ● Forces vectorization everywhere, even when awkward or wasterful ● Creates a social barrier - a wall between users and developers @ucb_notanartist Enter The Julian Unification ● Dynamic ● Compiled ● User Types and standard types ● Standalone or glue AI & Robotics Team Kiwibot Julia is Fast!! Let us understand the necessity: Scientific AI Machine Learning + Domain Power modeling AI + Robotics Team Kiwibot Mechanistics vs Non-Mechanistics Models Let's say we want to define a nonlinear model for the population of rabbits. There are three basic ways to do this: Rabbits tomorrow = Model(Rabbits today) ● Analytical solutions require that someone explicitly define a nonlinear model.
[Show full text]
Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting
water Article Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting Halit Apaydin 1 , Hajar Feizi 2 , Mohammad Taghi Sattari 1,2,* , Muslume Sevba Colak 1 , Shahaboddin Shamshirband 3,4,* and Kwok-Wing Chau 5 1 Department of Agricultural Engineering, Faculty of Agriculture, Ankara University, Ankara 06110, Turkey; [email protected] (H.A.); [email protected] (M.S.C.) 2 Department of Water Engineering, Agriculture Faculty, University of Tabriz, Tabriz 51666, Iran; [email protected] 3 Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam 4 Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam 5 Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China; [email protected] * Correspondence: [email protected] or [email protected] (M.T.S.); [email protected] (S.S.) Received: 1 April 2020; Accepted: 21 May 2020; Published: 24 May 2020 Abstract: Due to the stochastic nature and complexity of flow, as well as the existence of hydrological uncertainties, predicting streamflow in dam reservoirs, especially in semi-arid and arid areas, is essential for the optimal and timely use of surface water resources. In this research, daily streamflow to the Ermenek hydroelectric dam reservoir located in Turkey is simulated using deep recurrent neural network (RNN) architectures, including bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU), long short-term memory (LSTM), and simple recurrent neural networks (simple RNN). For this purpose, daily observational flow data are used during the period 2012–2018, and all models are coded in Python software programming language.
[Show full text]
Neural Network Architectures and Activation Functions: a Gaussian Process Approach
Fakultät für Informatik Neural Network Architectures and Activation Functions: A Gaussian Process Approach Sebastian Urban Vollständiger Abdruck der von der Fakultät für Informatik der Technischen Universität München zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften (Dr. rer. nat.) genehmigten Dissertation. Vorsitzender: Prof. Dr. rer. nat. Stephan Günnemann Prüfende der Dissertation: 1. Prof. Dr. Patrick van der Smagt 2. Prof. Dr. rer. nat. Daniel Cremers 3. Prof. Dr. Bernd Bischl, Ludwig-Maximilians-Universität München Die Dissertation wurde am 22.11.2017 bei der Technischen Universtät München eingereicht und durch die Fakultät für Informatik am 14.05.2018 angenommen. Neural Network Architectures and Activation Functions: A Gaussian Process Approach Sebastian Urban Technical University Munich 2017 ii Abstract The success of applying neural networks crucially depends on the network architecture being appropriate for the task. Determining the right architecture is a computationally intensive process, requiring many trials with different candidate architectures. We show that the neural activation function, if allowed to individually change for each neuron, can implicitly control many aspects of the network architecture, such as effective number of layers, effective number of neurons in a layer, skip connections and whether a neuron is additive or multiplicative. Motivated by this observation we propose stochastic, non-parametric activation functions that are fully learnable and individual to each neuron. Complexity and the risk of overﬁtting are controlled by placing a Gaussian process prior over these functions. The result is the Gaussian process neuron, a probabilistic unit that can be used as the basic building block for probabilistic graphical models that resemble the structure of neural networks.
[Show full text]
HGC: a Fast Hierarchical Graph-Based Clustering Method
Package ‘HGC’ September 27, 2021 Type Package Title A fast hierarchical graph-based clustering method Version 1.1.3 Description HGC (short for Hierarchical Graph-based Clustering) is an R package for conducting hierarchical clustering on large-scale single-cell RNA-seq (scRNA-seq) data. The key idea is to construct a dendrogram of cells on their shared nearest neighbor (SNN) graph. HGC provides functions for building graphs and for conducting hierarchical clustering on the graph. The users with old R version could visit https://github.com/XuegongLab/HGC/tree/HGC4oldRVersion to get HGC package built for R 3.6. License GPL-3 Encoding UTF-8 SystemRequirements C++11 Depends R (>= 4.1.0) Imports Rcpp (>= 1.0.0), RcppEigen(>= 0.3.2.0), Matrix, RANN, ape, dendextend, ggplot2, mclust, patchwork, dplyr, grDevices, methods, stats LinkingTo Rcpp, RcppEigen Suggests BiocStyle, rmarkdown, knitr, testthat (>= 3.0.0) VignetteBuilder knitr biocViews SingleCell, Software, Clustering, RNASeq, GraphAndNetwork, DNASeq Conﬁg/testthat/edition 3 NeedsCompilation yes git_url https://git.bioconductor.org/packages/HGC git_branch master git_last_commit 61622e7 git_last_commit_date 2021-07-06 Date/Publication 2021-09-27 1 2 CKNN.Construction Author Zou Ziheng [aut], Hua Kui [aut], XGlab [cre, cph] Maintainer XGlab <[email protected]> R topics documented: CKNN.Construction . .2 FindClusteringTree . .3 HGC.dendrogram . .4 HGC.parameter . .5 HGC.PlotARIs . .6 HGC.PlotDendrogram . .7 HGC.PlotParameter . .8 KNN.Construction . .9 MST.Construction . 10 PMST.Construction . 10 Pollen . 11 RNN.Construction . 12 SNN.Construction . 12 Index 14 CKNN.Construction Building Unweighted Continuous K Nearest Neighbor Graph Description This function builds a Continuous K Nearest Neighbor (CKNN) graph in the input feature space using Euclidean distance metric.
[Show full text]
Julia: a Modern Language for Modern ML
Julia: A modern language for modern ML Dr. Viral Shah and Dr. Simon Byrne www.juliacomputing.com What we do: Modernize Technical Computing Today’s technical computing landscape: • Develop new learning algorithms • Run them in parallel on large datasets • Leverage accelerators like GPUs, Xeon Phis • Embed into intelligent products “Business as usual” will simply not do! General Micro-benchmarks: Julia performs almost as fast as C • 10X faster than Python • 100X faster than R & MATLAB Performance benchmark relative to C. A value of 1 means as fast as C. Lower values are better. A real application: Gillespie simulations in systems biology 745x faster than R • Gillespie simulations are used in the field of drug discovery. • Also used for simulations of epidemiological models to study disease propagation • Julia package (Gillespie.jl) is the state of the art in Gillespie simulations • https://github.com/openjournals/joss- papers/blob/master/joss.00042/10.21105.joss.00042.pdf Implementation Time per simulation (ms) R (GillespieSSA) 894.25 R (handcoded) 1087.94 Rcpp (handcoded) 1.31 Julia (Gillespie.jl) 3.99 Julia (Gillespie.jl, passing object) 1.78 Julia (handcoded) 1.2 Those who convert ideas to products fastest will win Computer Quants develop Scientists prepare algorithms The last 25 years for production (Python, R, SAS, DEPLOY (C++, C#, Java) Matlab) Quants and Computer Compress the Scientists DEPLOY innovation cycle collaborate on one platform - JULIA with Julia Julia offers competitive advantages to its users Julia is poised to become one of the Thank you for Julia. Yo u ' v e k i n d l ed leading tools deployed by developers serious excitement.
[Show full text]