matlab批量替换代码-mlp-fashion-mnist:mlp-fashion-mnist

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matlab批量替换代码时尚MNIST Fashion-MNIST是的商品图片数据集-包含60,000个示例的训练集和10,000个示例的测试集。 每个示例都是一个28x28灰度图像,与来自10个类别的标签相关联。 我们打算将Fashion-MNIST用作原始机器的直接替代品,以对机器学习算法进行基准测试。 它具有相同的图像大小以及训练和测试分割的结构。 这是一个数据外观的示例(每个类占用三行): 我们为什么做Fashion-MNIST 原稿包含很多手写数字。 AI / ML /数据科学社区的成员喜欢此数据集,并将其用作验证其算法的基准。 实际上,MNIST通常是研究人员尝试的第一个数据集。 他们说: “如果它在MNIST上不起作用,那么它将根本不起作用。” “好吧,如果它确实可以在MNIST上运行,那么在其他系统上仍然可能失败。” 致认真的机器学习研究人员 认真地说,我们正在谈论取代MNIST。 这里有一些很好的理由: MNIST太简单了。 卷积网络在MNIST上可以达到99.7%。 经典的机器学习算法也可以轻松达到97%。 签出,并阅读“。”。 MNIST被过度使用。 在中,Goo
mlp-fashion-mnist-master.zip
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内容介绍
# Fashion-MNIST [![GitHub stars](https://img.shields.io/github/stars/zalandoresearch/fashion-mnist.svg?style=flat&label=Star)](https://github.com/zalandoresearch/fashion-mnist/) [![Gitter](https://badges.gitter.im/zalandoresearch/fashion-mnist.svg)](https://gitter.im/fashion-mnist/Lobby?utm_source=share-link&utm_medium=link&utm_campaign=share-link) [![Readme-CN](https://img.shields.io/badge/README-中文-green.svg)](README.zh-CN.md) [![Readme-JA](https://img.shields.io/badge/README-日本語-green.svg)](README.ja.md) `Fashion-MNIST` is a dataset of [Zalando](https://jobs.zalando.com/tech/)'s article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. We intend `Fashion-MNIST` to serve as a direct **drop-in replacement** for the original [MNIST dataset](http://yann.lecun.com/exdb/mnist/) for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits. Here's an example how the data looks (*each class takes three-rows*): ![](doc/img/fashion-mnist-sprite.png) ## Why we made Fashion-MNIST The original [MNIST dataset](http://yann.lecun.com/exdb/mnist/) contains a lot of handwritten digits. Members of the AI/ML/Data Science community love this dataset and use it as a benchmark to validate their algorithms. In fact, MNIST is often the first dataset researchers try. *"If it doesn't work on MNIST, it **won't work** at all"*, they said. *"Well, if it does work on MNIST, it may still fail on others."* ### To Serious Machine Learning Researchers Seriously, we are talking about replacing MNIST. Here are some good reasons: - **MNIST is too easy.** Convolutional nets can achieve 99.7% on MNIST. Classic machine learning algorithms can also achieve 97% easily. Check out [our side-by-side benchmark for Fashion-MNIST vs. MNIST](http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/), and read "[Most pairs of MNIST digits can be distinguished pretty well by just one pixel](https://gist.github.com/dgrtwo/aaef94ecc6a60cd50322c0054cc04478)." - **MNIST is overused.** In [this April 2017 Twitter thread](https://twitter.com/goodfellow_ian/status/852591106655043584), Google Brain research scientist and deep learning expert Ian Goodfellow calls for people to move away from MNIST. - **MNIST can not represent modern CV tasks**, as noted in [this April 2017 Twitter thread](https://twitter.com/fchollet/status/852594987527045120), deep learning expert/Keras author François Chollet. ## Get the Data You can use direct links to download the dataset. The data is stored in the **same** format as the original [MNIST data](http://yann.lecun.com/exdb/mnist/). | Name | Content | Examples | Size | Link | MD5 Checksum| | --- | --- |--- | --- |--- |--- | | `train-images-idx3-ubyte.gz` | training set images | 60,000|26 MBytes | [Download](http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz)|`8d4fb7e6c68d591d4c3dfef9ec88bf0d`| | `train-labels-idx1-ubyte.gz` | training set labels |60,000|29 KBytes | [Download](http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz)|`25c81989df183df01b3e8a0aad5dffbe`| | `t10k-images-idx3-ubyte.gz` | test set images | 10,000|4.3 MBytes | [Download](http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz)|`bef4ecab320f06d8554ea6380940ec79`| | `t10k-labels-idx1-ubyte.gz` | test set labels | 10,000| 5.1 KBytes | [Download](http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz)|`bb300cfdad3c16e7a12a480ee83cd310`| Alternatively, you can clone this GitHub repository; the dataset appears under `data/fashion`. This repo also contains some scripts for benchmark and visualization. ```bash git clone git@github.com:zalandoresearch/fashion-mnist.git ``` ### Labels Each training and test example is assigned to one of the following labels: | Label | Description | | --- | --- | | 0 | T-shirt/top | | 1 | Trouser | | 2 | Pullover | | 3 | Dress | | 4 | Coat | | 5 | Sandal | | 6 | Shirt | | 7 | Sneaker | | 8 | Bag | | 9 | Ankle boot | ## Usage ### Loading data with Python (requires [NumPy](http://www.numpy.org/)) Use `utils/mnist_reader` in this repo: ```python import mnist_reader X_train, y_train = mnist_reader.load_mnist('data/fashion', kind='train') X_test, y_test = mnist_reader.load_mnist('data/fashion', kind='t10k') ``` ### Loading data with Tensorflow Make sure you have [downloaded the data](#get-the-data) and placed it in `data/fashion`. Otherwise, *Tensorflow will download and use the original MNIST.* ```python from tensorflow.examples.tutorials.mnist import input_data data = input_data.read_data_sets('data/fashion') data.train.next_batch(BATCH_SIZE) ``` Note, Tensorflow (master ver.) supports passing in a source url to the `read_data_sets`. You may use: ```python data = input_data.read_data_sets('data/fashion', source_url='http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/') ``` ### Loading data with other machine learning libraries To date, the following libraries have included `Fashion-MNIST` as a built-in dataset. Therefore, you don't need to download `Fashion-MNIST` by yourself. Just follow their API and you are ready to go. - [Apache MXNet Gluon (master ver.)](https://mxnet.incubator.apache.org/versions/master/api/python/gluon.html#vision) - [deeplearn.js](https://pair-code.github.io/deeplearnjs/demos/model-builder/model-builder-demo.html) - [Kaggle](https://www.kaggle.com/zalando-research/fashionmnist) - [Pytorch](https://github.com/pytorch/vision#mnist) - [Keras](https://keras.io/datasets/#fashion-mnist-database-of-fashion-articles) - [Edward](http://edwardlib.org/api/observations/fashion_mnist) - [Tensorflow (master ver.)](https://github.com/tensorflow/tensorflow/pull/12983) You are welcome to make pull requests to other open-source machine learning packages, improving their support on `Fashion-MNIST` dataset. ### Loading data with other languages As one of the Machine Learning community's most popular datasets, MNIST has inspired people to implement loaders in many different languages. You can use these loaders with the `Fashion-MNIST` dataset as well. (Note: may require decompressing first.) To date, we haven't yet tested all of these loaders with Fashion-MNIST. - [C](https://stackoverflow.com/a/10409376) - [C++](https://github.com/wichtounet/mnist) - [Java](https://stackoverflow.com/a/8301949) - [Python](https://pypi.python.org/pypi/python-mnist) and [this](https://pypi.python.org/pypi/mnist) - [Scala](http://mxnet.io/tutorials/scala/mnist.html) - [Go](https://github.com/schuyler/neural-go/blob/master/mnist/mnist.go) - [C#](https://jamesmccaffrey.wordpress.com/2013/11/23/reading-the-mnist-data-set-with-c/) - [NodeJS](https://github.com/ApelSYN/mnist_dl) and [this](https://github.com/cazala/mnist) - [Swift](https://github.com/simonlee2/MNISTKit) - [R](https://gist.github.com/brendano/39760) and [this](https://github.com/maddin79/darch) - [Matlab](http://ufldl.stanford.edu/wiki/index.php/Using_the_MNIST_Dataset) and [this](https://de.mathworks.com/matlabcentral/fileexchange/27675-read-digits-and-labels-from-mnist-database?focused=5154133&tab=function) - [Ruby](https://github.com/gbuesing/mnist-ruby-test/blob/master/train/mnist_loader.rb) ## Benchmark We built an automatic benchmarking system based on `scikit-learn` that covers 129 classifiers (but no deep learning) with different parameters. [Find the results here](http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/). You can reproduce the results by running `benchmark/runner.py`. We recommend building and deploying [this Dockerfile](Dockerfile). You are welcome to submit your benchmark; simply create a new issue and we'll list your results here. Before
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