Note to readers of the NCO User Guide in HTML format The NCO User Guide in PDF format also on SourceForge contains the complete NCO documentation. Introduction. netcdf4python is a Python interface to the netCDF C library. netCDF version 4 has many features not found in earlier versions of the library and is. NCO 4. 7. 0 alpha. User Guide. NCO 4. User Guide. This file documents NCO, a collection of utilities to. CDF files. Copyright 1. Charlie Zender. This is the first edition of the NCO User Guide,and is consistent with version 2 of texinfo. Permission is granted to copy, distribute andor modify this document. GNU Free Documentation License, Version 1. Free Software Foundation. Invariant Sections, no Front Cover Texts, and no Back Cover. Texts. The license is available online at. The original author of this software, Charlie Zender, wants to improve it. Charlie Zender lt surname at uci dot edu yes, my surname is zender3. Croul Hall. Department of Earth System Science. University of California, Irvine. Irvine, CA 9. 26. Table of Contents. NCO User Guide. Note to readers of the NCO User Guide in HTML format. The NCO User Guide in PDF format. Source. Forge. contains the complete NCO documentation. This HTML documentation is equivalent except it refers you to the. DVI, Post. Script, and PDF documentation for description. Also, images appear only in the. PDF document due to Source. Forge limitations. The net. CDF Operators, or NCO, are a suite of programs known as. The operators facilitate manipulation and analysis of data stored in the. CDF format, available from. Each NCO operator e. CDF input. files, performs an operation e. CDF file. Although most users of net. CDF data are involved in scientific research. NCO, are generic and are equally. The NCO User Guide illustrates NCO use with. The NCO homepage is http nco. This documentation is for NCO version 4. It was last updated 3. October 2. 01. 7. Corrections, additions, and rewrites of this documentation are. Enjoy,Charlie Zender. Foreword. NCO is the result of software needs that arose while I worked. NCAR, NASA, and ARM. Thinking they might prove useful as tools or templates to others. Many users most of whom I have never met have encouraged the. NCO. Thanks espcially to Jan Polcher, Keith Lindsay, Arlindo da Silva. John Sheldon, and William Weibel for stimulating suggestions and. Your encouragment motivated me to complete the NCO User Guide. So if you like NCO, send me a note I should mention that NCO is not connected to or. Unidata, ACD, ASP. CGD, or Nike. Charlie Zender. May 1. 99. 7Boulder, Colorado. Major feature improvements entitle me to write another Foreword. In the last five years a lot of work has been done to refine. NCO is now an open source project and appears to be much. The list of illustrious institutions that do not endorse NCO. UCI. Charlie Zender. October 2. 00. 0Irvine, California. The most remarkable advances in NCO capabilities in the last. Open Source community. Especially noteworthy are the contributions of Henry Butowsky and Rorik. Peterson. Charlie Zender. January 2. 00. 3Irvine, California. NCO was generously supported from 2. US. National Science Foundation NSF grant. This support allowed me to maintain and extend core NCO code. NCO in new directions. Gayathri Venkitachalam helped implement MPI. Harry Mangalam improved regression testing and benchmarking. Daniel Wang developed the server side capability, SWAMP. Henry Butowsky, a long time contributor, developed ncap. This support also led NCO to debut in professional journals. The personal and professional contacts made during this evolution have. Charlie Zender. March 2. Grenoble, France. The end of the NSFSEI grant in August, 2. NCO development. Fortunately we could justify supporting Henry Butowsky on other research. May, 2. 01. 0 while he developed the key ncap. And recently the NASAACCESS program commenced. CDF4 group functionality. Thus NCO will grow and evade bit rot for the foreseeable. I continue to receive with gratitude the thanks of NCO users. I attend. People introduce themselves, shake my hand and extol NCO. I grin in stupid embarassment. These exchanges lighten me like anti gravity. Sometimes I daydream how many hours NCO has turned from grunt. Its a cool feeling. Charlie Zender. April, 2. Irvine, California. The NASAACCESS 2. Cooperative Agreement NNX1. AF4. 8A NCO from 2. This allowed us to produce the first iteration of a Group oriented. Data Analysis and Distribution GODAD software ecosystem. Shifting more geoscience data analysis to GODAD is a. Then the NASAACCESS 2. Cooperative Agreement NNX1. AH5. 5A NCO from. This support permits us to implement support for Swath like Data. Most recently, the DOE has funded me to implement. NCO re gridding and parallelization in support of their. After many years of crafting NCO as an after hours hobby. I finally have the cushion necessary to give it some real attention. And Im looking forward to this next, and most intense yet, phase of. NCO development. Charlie Zender. June, 2. 01. 5Irvine, California. Summary. This manual describes NCO, which stands for net. CDF Operators. NCO is a suite of programs known as operators. Each operator is a standalone, command line program executed at the. The operators take net. CDF files including HDF5 files. CDF API as input, perform an. CDF file. The operators are primarily designed to aid manipulation and analysis of. The examples in this documentation are typical applications of the. This stems from their origin, though the operators are as general as. Introduction. 1. 1 Availability. The complete NCO source distribution is currently distributed. The compressed tarfile must be uncompressed and untarred before building. Uncompress the file with gunzip nco. Extract the source files from the resulting tarfile with tar xvf. GNUtar lets you perform both operations in one step. The documentation for NCO is called the. NCO User Guide. The User Guide is available in PDF, Postscript. HTML, DVI, Te. Xinfo, and Info formats. These formats are included in the source distribution in the files. All the documentation descends from a single source file. Hence the documentation in every format is very similar. However, some of the complex mathematical expressions needed to describe. DVI, Postscript, and. A complete list of papers and publications onabout NCO. NCO homepage. Most of these are freely available. The primary refereed publications are Ze. M0. 6 and Zen. 08. These contain copyright restrictions which limit their redistribution. NCO. If you want to quickly see what the latest improvements in NCO. NCO homepage at. http nco. The HTML version of the User Guide is also available. World Wide Web at URLhttp nco. To build and use NCO, you must have net. CDF installed. The net. CDF homepage is. http www. New NCO releases are announced on the net. CDF list. and on the nco announce mailing list. How to Use This Guide. Detailed instructions about. FAQ and. descriptions of Known Problems etc. There are twelve operators in the current version 4. The function of each is explained in Reference Manual. Many of the tasks that NCO can accomplish are described during. NCO Features see Shared features. More specific use examples for each operator can be seen by visiting the. Reference Manual. These can be found directly by prepending the operator name with the. Also, users can type the operator name on the shell command line to. NCO is a command line language. You may either use an operator after the prompt e. CMIP5 Example see CMIP5 Example. If you are new to NCO, the Quick Start see Quick Start. NCO on different kinds. More detailed real world examples are in the. CMIP5 Example. The Index is presents multiple keyword entries for. If these resources do not help enough, please. Help Requests and Bug Reports. Operating systems compatible with NCOIn its time on Earth, NCO has been successfully ported and. IBM AIX 4. x, 5. x. GNULinux 2. x, Linux. PPC, Linux. Alpha, Linux. ARM, Linux. Sparc. SGI IRIX 5. x and 6. NEC Super UX 1. 0. Sun Sun. OS 4. 1. Solaris 2. x. Cray UNICOS 8. Microsoft Windows 9. NT, 2. 00. 0, XP, Vista. If you port the code to a new operating system, please send me a note. The major prerequisite for installing NCO on a particular. CDF library. and, as of 2. UDUnits library. Unidata has shown a commitment to maintaining net. CDF and UDUnits on all. UNIX platforms, and is moving towards full support for. Microsoft Windows operating system OS. Given this, the only difficulty in implementing NCO on a. C language API. NCO code is tested for ANSI compliance by. C9. 9 compilers including those from. GNU gcc stdc. DBSDSOURCE DPOSIXSOURCE Wall. Comeau Computing como c. HPCompaqDEC cc. IBM xlc c qlanglvlextc. Intel icc stdc. LLVM clang. Path. Scale QLogic pathcc stdc. PGI pgcc c. 9x. FAQ Keras Documentation. How should I cite Keras Please cite Keras in your publications if it helps your research. Here is an example Bib. Te. X entry miscchollet. Keras. authorChollet, Franccois and others. Git. Hub. howpublishedurlhttps github. How can I run Keras on GPU If you are running on the Tensor. Flow or CNTK backends, your code will automatically run on GPU if any available GPU is detected. If you are running on the Theano backend, you can use one of the following methods Method 1 use Theano flags. THEANOFLAGSdevicegpu,float. Xfloat. 32 python mykerasscript. The name gpu might have to be changed depending on your devices identifier e. Method 2 set up your. Instructions. Method 3 manually set theano. X at the beginning of your code import theano. X float. 32. How can I run a Keras model on multiple GPUsWe recommend doing so using the Tensor. Flow backend. There are two ways to run a single model on multiple GPUs data parallelism and device parallelism. In most cases, what you need is most likely data parallelism. Data parallelism. Data parallelism consists in replicating the target model once on each device, and using each replica to process a different fraction of the input data. Keras has a built in utility, keras. GPUs. For more information, see the documentation for multigpumodel. Here is a quick example from keras. Replicates model on 8 GPUs. This assumes that your machine has 8 available GPUs. This fit call will be distributed on 8 GPUs. Since the batch size is 2. GPU will process 3. Device parallelism. Device parallelism consists in running different parts of a same model on different devices. It works best for models that have a parallel architecture, e. This can be achieved by using Tensor. Flow device scopes. Here is a quick example Model where a shared LSTM is used to encode two different sequences in parallel. Inputshape1. 40, 2. Inputshape1. 40, 2. LSTM6. 4. Process the first sequence on one GPU. Process the next sequence on another GPU. Concatenate results on CPU. What does sample, batch, epoch mean Below are some common definitions that are necessary to know and understand to correctly utilize Keras Sample one element of a dataset. Example one image is a sample in a convolutional network. Example one audio file is a sample for a speech recognition model. Batch a set of N samples. The samples in a batch are processed independently, in parallel. If training, a batch results in only one update to the model. A batch generally approximates the distribution of the input data better than a single input. The larger the batch, the better the approximation however, it is also true that the batch will take longer to processes and will still result in only one update. For inference evaluatepredict, it is recommended to pick a batch size that is as large as you can afford without going out of memory since larger batches will usually result in faster evaluatingprediction. Epoch an arbitrary cutoff, generally defined as one pass over the entire dataset, used to separate training into distinct phases, which is useful for logging and periodic evaluation. When using evaluationdata or evaluationsplit with the fit method of Keras models, evaluation will be run at the end of every epoch. Within Keras, there is the ability to add callbacks specifically designed to be run at the end of an epoch. Examples of these are learning rate changes and model checkpointing saving. How can I save a Keras model Savingloading whole models architecture weights optimizer stateIt is not recommended to use pickle or c. Pickle to save a Keras model. You can use model. Keras model into a single HDF5 file which will contain the architecture of the model, allowing to re create the modelthe weights of the modelthe training configuration loss, optimizerthe state of the optimizer, allowing to resume training exactly where you left off. You can then use keras. Example from keras. HDF5 file mymodel. Savingloading only a models architecture. If you only need to save the architecture of a model, and not its weights or its training configuration, you can do save as JSON. The generated JSON YAML files are human readable and can be manually edited if needed. You can then build a fresh model from this data model reconstruction from JSON. YAML. from keras. Savingloading only a models weights. If you need to save the weights of a model, you can do so in HDF5 with the code below. Note that you will first need to install HDF5 and the Python library h. Keras. model. saveweightsmymodelweights. Assuming you have code for instantiating your model, you can then load the weights you saved into a model with the same architecture model. If you need to load weights into a different architecture with some layers in common, for instance for fine tuning or transfer learning, you can load weights by layer name model. True. For example. Assuming the original model looks like this. Sequential. model. Dense2, inputdim3, namedense1. Dense3, namedense2. Sequential. model. Dense2, inputdim3, namedense1 will be loaded. Dense1. 0, namenewdense will not be loaded. True. Handling custom layers or other custom objects in saved models. If the model you want to load includes custom layers or other custom classes or functions. Assuming your model includes instance of an Attention. Layer class. model loadmodelmymodel. Attention. Layer Attention. Layer. Alternatively, you can use a custom object scope from keras. Custom. Object. Scope. Custom. Object. ScopeAttention. Layer Attention. Layer. model loadmodelmymodel. Custom objects handling works the same way for loadmodel, modelfromjson, modelfromyaml from keras. Attention. Layer Attention. Layer. Why is the training loss much higher than the testing loss A Keras model has two modes training and testing. Regularization mechanisms, such as Dropout and L1L2 weight regularization, are turned off at testing time. Besides, the training loss is the average of the losses over each batch of training data. Because your model is changing over time, the loss over the first batches of an epoch is generally higher than over the last batches. On the other hand, the testing loss for an epoch is computed using the model as it is at the end of the epoch, resulting in a lower loss. One simple way is to create a new Model that will output the layers that you are interested in from keras. Model. model. Modelinputsmodel. Alternatively, you can build a Keras function that will return the output of a certain layer given a certain input, for example from keras import backend as K. Sequential model. K. functionmodel. Similarly, you could build a Theano and Tensor. Flow function directly. Note that if your model has a different behavior in training and testing phase e. Dropout, Batch. Normalization, etc., you will need. K. functionmodel. K. learningphase. How can I use Keras with datasets that dont fit in memory You can do batch training using model. See the models documentation. Alternatively, you can write a generator that yields batches of training data and use the method model. You can see batch training in action in our CIFAR1. How can I interrupt training when the validation loss isnt decreasing anymore You can use an Early. Stopping callback from keras. Early. Stopping. earlystopping Early. Stoppingmonitorvalloss, patience2. Find out more in the callbacks documentation. How is the validation split computed If you set the validationsplit argument in model. If you set it to 0. Note that the data isnt shuffled before extracting the validation split, so the validation is literally just the last x of samples in the input you passed. The same validation set is used for all epochs within a same call to fit. Is the data shuffled during training Yes, if the shuffle argument in model. True which is the default, the training data will be randomly shuffled at each epoch. Validation data is never shuffled. How can I record the training validation loss accuracy at each epochThe model. fit method returns an History callback, which has a history attribute containing the lists of successive losses and other metrics. How can I freeze Keras layers To freeze a layer means to exclude it from training, i. This is useful in the context of fine tuning a model, or using fixed embeddings for a text input.
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