Tf Dataset Map

I have a dataset containing 592 examples, but tf. Args: map_func: A function mapping a nested structure of tensors (having shapes and types defined by self. The code for TF-Ranking including samples and tutorials is available on GitHub. Data and Resources. The dataset itself is an iterator now and can be itarated with a for-loop. Having efficient data pipelines is of paramount importance for any machine learning model. Pre-trained models and datasets built by Google and the community. Download files. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy. It has 4 features. In such cases, we recommend vectorizing the user-defined function (that is, have it operate over a batch of inputs at once) and apply the batch transformation before the map transformation. \] There are several variants on the definition of term frequency and document frequency. The purpose is to aid the user in more rapidly identifying the desired map for a specific area of interest relative to other maps, roads, landmarks, etc. If all of your input data fit in memory, the simplest way to create a Dataset from them is to convert them to tf. Roads and Maritime Services. We released two large scale datasets for research on learning to rank: MSLR-WEB30k with more than 30,000 queries and a random sampling of it MSLR-WEB10K with 10,000 queries. 这一部分主要参考tensorflow的官方教程:tf. I have a very expensive function which I map onto this dataset using tf. Maps Institution IBRD IDA tf investment voting power allocations Suggest a dataset. Dataset object. OS VectorMap District lets you display just the. The Tensor Processing Unit (TPU) hardware accelerators we will be using in this lab are very fast. With torchdata you can do all that with simple map (perform your operations on images, whatever you want) and cache (save it in RAM memory) like this: dataset = Images(". Tensor, representing the number of batches to create in parallel. The BigQuery module is packaged in a self-contained GitHub repository for you to easily download (or reference) and deploy. Inside this function — which I developed by simply for-looping over the dataset in eager execution — I convert the tensors to NumPy arrays using EagerTensor. You might find it best to order separate tiles for your areas of interest. The following are code examples for showing how to use tensorflow. TFRecordDataset. MapReduce implements various mathematical. map() dataset = dataset. I am trying to use the TensorFlow (v1. Example message using this. 这一部分主要参考tensorflow的官方教程:tf. “TensorFlow - Importing data” Nov 21, 2017. “Convolutional neural networks (CNN) tutorial” Mar 16, 2017. from_tensor_slices((filenames, labels))后,dataset的一个元素是(filename, label)。filename是图片的文件名,label是图片对应的标签。 之后通过map,将filename对应的图片读入,并缩放为28x28的大小。此时dataset中的一个元素是(image_resized, label). Machine learning is an ever-growing area of interest for developers, businesses, tech enthusiasts and the general public alike. tfrecord", "file2. Reading numerous files into memory is infeasible. map: Calls the decode_csv function with each element in the dataset as an argument (since we are using TextLineDataset, each element will be a line of CSV text). When this new dataset is evaluated, the data operations defined in the function will be applied to all elements in the set. map 😀 datasets in TF eager mode 😓 datasets in non-eager mode; Please take a moment to go through this checklist in your head. 1 Semantics. Hi, this is part 2 of the series. This produces a complex model to explore all possible connections among nodes. The national dataset for this product is a big download. jl does not support this file type, so in order to follow the exercise, we need to extract the data from the tfrecord-dataset. Show enough detail to give the lie of the land while avoiding overwhelming detail. map map接收 一个函数 ,Dataset中的每个元素都会被当作这个函数的输入,并将函数返回值作为新的Dataset,如我们可以对dataset中每个元素的值加1:. In a fully connected network, all nodes in a layer are fully connected to all the nodes in the previous layer. from_tensor_slices((filenames, labels))后,dataset的一个元素是(filename, label)。filename是图片的文件名,label是图片对应的标签。 之后通过map,将filename对应的图片读入,并缩放为28x28的大小。此时dataset中的一个元素是(image_resized, label). Having efficient data pipelines is of paramount importance for any machine learning model. zip 2)dataset1. string_split(). from_generator. 本篇博客结合 mnist 的经典例子,针对不同的源数据:csv数据和tfrecord数据,分别运用 tf. Tensor 对象 dataset = dataset. data API enables you to build complex input pipelines from simple, reusable pieces. map_func: A function mapping a nested structure of tensors (having shapes and types defined by output_shapes() and output_types() to another nested structure of tensors. fully_connected() internally flattens all but the last dimension, applied the linear layer, and restores the original dimensions. For a small amount of data that can. Mapper class takes the input, tokenizes it, maps and sorts it. Plot and visualization of Hadoop large dataset with Python Datashader you need to use the tf of the complete green taxi dataset (7. filter()—that are generally useful across a wide range of datasets, unlikely to change, and implemented as methods on the tf. Additionally, we have created the TF-Slim Image Models library, which provides definitions and training scripts for many widely used image classification models, using standard datasets. In the TF-Ranking library, metrics are implemented to support unbiased evaluation and losses are implemented for unbiased learning by natively supporting re-weighting to overcome the inherent biases in user interactions datasets. prefeth() dataset = dataset. 运行dataset = tf. END_PUBLIC I dropped the following commit because it doesn't compile. This means the same weights are applied along all but the last dimension of the tensor. You might find it best to order separate tiles for your areas of interest. Will RTAB-MAP work without " /tf " topic ? Based on the answer below. So first create a tf. A new meshless approach to map electromagnetic loads for FEM analysis on DEMO TF coil system An industrial workflow for complex dataset Magnetic field. It may help to use the tensorboard visualization tool to visualize the TensorFlow graph and determine the output name. If you're not sure which to choose, learn more about installing packages. Tensor to a given shape. Loading a DataSet from XML. data Structure을 거쳐서 output 또는 input으로 반환되는 것을 볼 수 있다. I am a great fan of the flexibility provided by tf. The Boreal Ecosystem-Atmosphere Study (BOREAS) was a large-scale international interdisciplinary experiment in the boreal forests of central Canada. The provision of benchmark datasets is mandatory for research groups. In this tutorial, an introduction to TF-IDF, procedure to calculate TF-IDF and flow of actions to calculate TFIDF have been provided with Java and Python Examples. NCL Home> Application examples || Data files for some examples Example pages containing: tips | resources | functions/procedures Dataset Examples The following pages contain NCL scripts and graphics that you can download and use. Pre-trained models and datasets built by Google and the community. tfrecord file. Documentation for the TensorFlow for R interface. The sklearn. filter() dataset = dataset. The purpose is to aid the user in more rapidly identifying the desired map for a specific area of interest relative to other maps, roads, landmarks, etc. map(augmentation) These are the first 20 images after augmentation:. If one component of shape is the special value -1, the size of that dimension is computed so that the total size remains constant. Given one or more filenames and a list of defaults, a CsvDataset will produce a tuple of elements whose types correspond to the types of the defaults provided, per CSV record. A simple MNIST classifier which displays summaries in TensorBoard. The Boreal Ecosystem-Atmosphere Study (BOREAS) was a large-scale international interdisciplinary experiment in the boreal forests of central Canada. map(map_func=tf. For this reason I check the accuracy operation which is on the training dataset (on the batch fed into the optimizer) to plot the training accuracy during iterations. data to build efficient data…. You can map arbitrary transformation functions onto dataset records using the dataset_map() function. The dataset used in this example is distributed as directories of images, with one class of image per directory. prefetch() and the output_buffer_size argument in tf. Dataset object. OK, I Understand. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy. Table of Contents. A central question in text mining and natural language processing is how to quantify what a document is about. “TensorFlow - Importing data” Nov 21, 2017. In such cases, we recommend vectorizing the user-defined function (that is, have it operate over a batch of inputs at once) and apply the batch transformation before the map transformation. They are extracted from open source Python projects. TensorFlowのDataset APIは、TensorFlow1. OS Maps: online mapping and walking, running and cycling routes. 2 ## Bug Fixes and Other Changes * Fixes a potential security vulnerability where carefully crafted GIF images can produce a null pointer dereference during decodin. Maps Institution IBRD IDA tf investment voting power allocations Suggest a dataset. It allows you to do the data loading (from file or elsewhere) and some preprocessing in python before feeding. Note that a smoothing term is applied to avoid dividing by zero for terms outside the corpus. If you set `cycle_length` to 1, this transformation will handle one input element at a time, and will produce identical results to tf. parse_single_example(record)) # buffer 大小设置为 10000,打乱 dataset dataset = dataset. cache() If there are too many images (I had this commonly with tf. The MapReduce algorithm contains two important tasks, namely Map and Reduce. According to the existing standards, there are several ways of interpreting a set of triples, each being tied to what SPARQL calls an entailment regime. Be aware that the iterator will create a dictionary with key as the column names and values as Tensor with the correct row value. map(map_func=tf. Describe the expected behavior. Subscribed Topics. list_files 🤔 tf. datasets, 上一篇tensorflow数据读取是基于多线程数据抓取的方式,维护多队列(文件队列,example队列),是比较偏底层的。. OK, I Understand. map_and_batch把map和batch混在一起并行处理dataset. Should I read the file and publish the lase_scan and tf topic? If so,how should I do in detail? thanks. The Dataset API allows you to build an asynchronous, highly optimized data pipeline to prevent your GPU from data starvation. The map function takes a function and returns a new and augmented dataset. dataset to read directly a CSV finished make_csv_dataset') train_dataset = train_dataset. In this post, we will continue our journey to leverage Tensorflow TFRecord to reduce the training time by 21%. They are extracted from open source Python projects. You can vote up the examples you like or vote down the ones you don't like. num_parallel_calls: (Optional. Tensor to a given shape. Browse through the examples directory to get a full list of examples that are possible within the module. map이나 tensor_slice와 같은 함수는 기본적으로 tf. Learn how to use the TensorFlow Dataset API to create professional, high performance input data pipelines. According to the existing standards, there are several ways of interpreting a set of triples, each being tied to what SPARQL calls an entailment regime. map() provide a way to tune the performance of your input pipeline: both arguments tell TensorFlow to create a buffer of at most buffer_size elements, and a background thread to fill that buffer in the background. We will do this using transposed convolution (also known as deconvolution). In a fully connected network, all nodes in a layer are fully connected to all the nodes in the previous layer. Pre-trained models and datasets built by Google and the community. Dataset of 25,000 movies reviews from IMDB, labeled by sentiment (positive/negative). Documentation for the TensorFlow for R interface. import tensorflow as tf print(tf. It allows you to do the data loading (from file or elsewhere) and some preprocessing in python before feeding. An Easy Guide to build new TensorFlow Datasets and Estimator with Keras Model dataset = dataset. int64 scalar tf. # Release 1. These MapInfo files seem to have been created on 4th March 2013. With torchdata you can do all that with simple map (perform your operations on images, whatever you want) and cache (save it in RAM memory) like this: dataset = Images(". OK, I Understand. This tutorial provides a simple example of how to load an image dataset using tf. Learn how to use the TensorFlow Dataset API to create professional, high performance input data pipelines. tensorflow/datasets is a library of public datasets ready to use with TensorFlow. map() is not executed eagerly. from_tensor_slices(). Tensor 对象 dataset = dataset. tf_function, TF autograph and the R package tfautograph. map: Calls the decode_csv function with each element in the dataset as an argument (since we are using TextLineDataset, each element will be a line of CSV text). You can map arbitrary transformation functions onto dataset records using the dataset_map() function. __version__) Datasets in TF 2. TF-Slim and its components are already widely used within Google, and many of these improvements have already been integrated into tf. 17 8 3331 1989 3312 PU001183N Westphal EM, Natt E, Grimm T, Odievre M, Scherer G. Args: map_func: A function mapping a nested structure of tensors (having shapes and types defined by self. According to the existing standards, there are several ways of interpreting a set of triples, each being tied to what SPARQL calls an entailment regime. Its focus was improving our understanding of the exchanges of radiative energy, sensible heat, water, CO2 and trace gases between the boreal forest and the lower atmosphere. OS VectorMap District is great if your map needs a custom look. Should I read the file and publish the lase_scan and tf topic? If so,how should I do in detail? thanks. TensorFlow Enterprise introduces some improvements in the way TensorFlow Dataset reads data from Cloud Storage. TFRecordDataset() 创建不同的 Dataset 并运用四种不同的 Iterator ,分别是 单次,可初始化,可重新初始化,以及可馈送迭代器 的方式实现对源数据的预. map map( map_func, num_parallel_calls=None ) Maps map_func across this dataset. 10+ we still need to enable the Eager execution mode. Dataset API to perform some text preprocessing. It is an advanced view of the guide to running Inception v3 on Cloud TPU. Roads and Maritime Services is the delivery agency responsible for managing the operations and programs of roads and waterways in NSW. 本篇博客结合 mnist 的经典例子,针对不同的源数据:csv数据和tfrecord数据,分别运用 tf. "TensorFlow - Importing data" Nov 21, 2017. Dataset API to perform some text preprocessing. The MapReduce algorithm contains two important tasks, namely Map and Reduce. TFRecordDataset. Short URL. The launch file is based on rgbdslam_datasets. OS Open Map - Local is a street-level digital map that clearly shows roads, road names, electric car charging points and major public buildings. from_tensor_slices()函数基于images和labels构建Dataset。 然后使用map函数将函数应用到该Dataset上,本例中,将解析图像的函数_parse_function应用到Dataset上,还指定了多线程并行操作的线程数。 随后指定prefetch的buffer_size,以及batch的大小。. filter()—that are generally useful across a wide range of datasets, unlikely to change, and implemented as methods on the tf. ''' def tfrecords_to_dataset (handle):: Note: We can't excplicitly select what data to use when using tf. Stack Exchange network consists of 175 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. data tbh), you can save first 1000 images in memory and rest to disk like this:. Getting Started with TF-Ranking. Args: map_func: A function mapping a nested structure of tensors (having shapes and types defined by self. py_func(my_custom_reader, ), ) The map function does allow us to shuffle and parallelize the filenames using all of the functionality of the Dataset pipeline, however, with map, the files must be read into memory completely, and these files are large. In such cases, we recommend vectorizing the user-defined function (that is, have it operate over a batch of inputs at once) and apply the batch transformation before the map transformation. Home; People. num_parallel_batches: A tf. If you don't need to share a network dataset, or the people you share with use the same ArcGIS release. "Convolutional neural networks (CNN) tutorial" Mar 16, 2017. Without using num_parallel_calls in my dataset. Example(features = tf. from_tensor_slices(). Be aware that the iterator will create a dictionary with key as the column names and values as Tensor with the correct row value. 最近在学习tensorflow,自己准备一下数据集,从开始准备道最终验证是别的准确率记录下来。 我的数据集是卫星图片,共5类. For performance reasons however, in many cases it will be desirable to compile parts of your code into a graph. The datasets are machine learning data, in which queries and urls are represented by IDs. int64 scalar tf. Introduction. Spark MLlib TFIDF (Term Frequency - Inverse Document Frequency) - To implement TF-IDF, use HashingTF Transformer and IDF Estimator on Tokenized documents. We use cookies for various purposes including analytics. 3 Analyzing word and document frequency: tf-idf. from_tensor_slices(). Args: map_func: A function mapping a nested structure of tensors (having shapes and types defined by self. Subscribed Topics. placeholder()来定义的tensor进行初始化。 3 Transformation. If you don't need to share a network dataset, or the people you share with use the same ArcGIS release. The tiny_slam node takes insensor_msgs/LaserScan messages and odometry data from /tf topic and builds a map (nav_msgs/OccupancyGrid) that can be retrieved via the ROStopic /map. TFRecordDataset() 创建不同的 Dataset 并运用四种不同的 Iterator ,分别是 单次,可初始化,可重新初始化,以及可馈送迭代器 的方式实现对源数据的预. TextLineDataset or tf. The drawback of doing this, however, is you won't be able to include any new network dataset functionality that was introduced in later releases of ArcGIS—controls in the New Network Dataset wizard to add that functionality will be disabled. It has 4 features. 03s to preprocess 10K records. zip 2)dataset1. TF-Slim and its components are already widely used within Google, and many of these improvements have already been integrated into tf. A BAM HI RFLP at the human tyrosine aminotransferase (TAT) gene locus at 16q Nucleic Acids Res. map() dataset = dataset. This produces a complex model to explore all possible connections among nodes. make_csv_dataset. map: Calls the decode_csv function with each element in the dataset as an argument (since we are using TextLineDataset, each element will be a line of CSV text). Map a function across a dataset. Documentation for the TensorFlow for R interface. TF-Slim and its components are already widely used within Google, and many of these improvements have already been integrated into tf. For performance reasons however, in many cases it will be desirable to compile parts of your code into a graph. OK, I Understand. Machine learning is the science of getting computers to act without being explicitly programmed. For details, please refer to the accompanying publication "Towards Accurate Generative Models of Video: New Metrics & Challenges", Unterthiner, van Steenkiste, Kurach, Marinier, Michalski, Gelly, arXiv (2018). Pre-trained models and datasets built by Google and the community. This dataset provides WCAG 2. I use TensorFlow 1. In this blog, we will learn how to use TensorFlow's Dataset module tf. According to the existing standards, there are several ways of interpreting a set of triples, each being tied to what SPARQL calls an entailment regime. dataset_map_and_batch() Fused implementation of dataset_map() and dataset_batch() dataset_prepare() Prepare a dataset for analysis. Plot and visualization of Hadoop large dataset with Python Datashader you need to use the tf of the complete green taxi dataset (7. enable_eager_execution() line will need to be executed. We use cookies for various purposes including analytics. Meta Category (none) Permissions Public Tags floodplain, flood, water, flood plain, flooding, fema, emergency, river, creek. It has 4 features. Or use our free stylesheets if you prefer consistency. tfrecord file. Reading numerous files into memory is infeasible. cache() If there are too many images (I had this commonly with tf. I use TensorFlow 1. The national dataset for this product is a big download. map TensorFlow release 1. map map( map_func, num_parallel_calls=None ) Maps map_func across this dataset. Pre-trained models and datasets built by Google and the community. For convenience, words are indexed by overall frequency in the dataset, so that for instance the integer "3" encodes the 3rd most frequent word in the data. data tbh), you can save first 1000 images in memory and rest to disk like this:. 72 3 221-4 1986 3311 PU001182M Wullich B, Natt E, Wienker TF, Scherer G. In this blog, we will learn how to use TensorFlow’s Dataset module tf. In this 2020 update of ReMap we have collected, analyzed and retained after quality control 2764 new human ChIP-seq and 208 ChIP-exo datasets available from public sources. Below, you can find currently available datasets. The MapReduce algorithm contains two important tasks, namely Map and Reduce. 10+ we still need to enable the Eager execution mode. 运行dataset = tf. Introduction. Table of Contents. TF-Ranking is a great addition to the TensorFlow stack. For example, to transform the "Species" column into a one-hot encoded vector you would do this: dataset <-dataset %>% dataset_map (function (record) { record $ Species <-tf $ one_hot (record $ Species, 3L) record }). If you are creating a geodatabase-based network dataset, all feature classes participating as sources in a network should be present in one feature dataset. Should I read the file and publish the lase_scan and tf topic? If so,how should I do in detail? thanks. [Hindi] • PUBG Mobile Lite Live Stream • Winner Pass Season 6 Giveaway • Anyone Can Join • GoD丨Praveen YT 324 watching Live now. prefetch() and the output_buffer_size argument in tf. Network Maps. TF-Ranking is optimized for models that require large datasets and provides a very simple developer experience based on TensorFlow Estimators. The Tensor Processing Unit (TPU) hardware accelerators we will be using in this lab are very fast. They are extracted from open source Python projects. map map接收 一个函数 ,Dataset中的每个元素都会被当作这个函数的输入,并将函数返回值作为新的Dataset,如我们可以对dataset中每个元素的值加1:. The dataset itself is an iterator now and can be itarated with a for-loop. 0 (we'll use this today!) Easier to use. Basically, lets say I have n_seq sequences, each fixed to a length of 120. 10+ we still need to enable the Eager execution mode. Dataset API to perform some text preprocessing. Inside this function — which I developed by simply for-looping over the dataset in eager execution — I convert the tensors to NumPy arrays using EagerTensor. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy. TF-Ranking is optimized for models that require large datasets and provides a very simple developer experience based on TensorFlow Estimators. The dataset used in this example is distributed as directories of images, with one class of image per directory. An authority's Definitive Map is the authoritative source of their rights of way. Mapper class takes the input, tokenizes it, maps and sorts it. The updated human atlas totalize 5798 datasets covering a total of 1135 transcriptional regulators (TRs) with a catalog of 165 million (M) peaks. Example of basic MNIST Keras model with tf. map map( map_func, num_parallel_calls=None ) Maps map_func across this dataset. output_shapes and self. Dataset because you can't really sync three shuffles. Reviews have been preprocessed, and each review is encoded as a sequence of word indexes (integers). An Iterator over the elements of this dataset. 2019-10-23 by Grigory Starinkin & Oleg Tarasenko. DataSet records contain additional resources including cluster tools and differential expression queries. datasets package embeds some small toy datasets as introduced in the Getting Started section. 本篇博客结合 mnist 的经典例子,针对不同的源数据:csv数据和tfrecord数据,分别运用 tf. Up to date, it is not clear which solution is outperforming the others. These MapInfo files seem to have been created on 4th March 2013. The launch file is based on rgbdslam_datasets. It allows you to do the data loading (from file or elsewhere) and some preprocessing in python before feeding. We released two large scale datasets for research on learning to rank: MSLR-WEB30k with more than 30,000 queries and a random sampling of it MSLR-WEB10K with 10,000 queries. To view the area covered by each map tile, see our tile locator map. A simple MNIST classifier which displays summaries in TensorBoard. OK, I Understand. from_tensor_slices()函数基于images和labels构建Dataset。 然后使用map函数将函数应用到该Dataset上,本例中,将解析图像的函数_parse_function应用到Dataset上,还指定了多线程并行操作的线程数。 随后指定prefetch的buffer_size,以及batch的大小。. The updated human atlas totalize 5798 datasets covering a total of 1135 transcriptional regulators (TRs) with a catalog of 165 million (M) peaks. There are five main steps in creating a network dataset: Prepare the feature dataset and sources. Machine learning is an ever-growing area of interest for developers, businesses, tech enthusiasts and the general public alike. An overview of tf. Use the TextLineDataset method from tf. Features(feature=feature)) In order to give an example let's create an artificial dataset. The map function takes a function and returns a new and augmented dataset. 12 in eager execution. We use cookies for various purposes including analytics. An Easy Guide to build new TensorFlow Datasets and Estimator with Keras Model dataset = dataset. data API enables you to build complex input pipelines from simple, reusable pieces. OK, I Understand. The purpose is to aid the user in more rapidly identifying the desired map for a specific area of interest relative to other maps, roads, landmarks, etc. The Word2VecModel transforms each document into a vector using the average of all words in the document; this vector can then be used as features for prediction, document similarity calculations,. output_types) to another nested structure of tensors. Learn more about a TensorFlow 2. Commit 003deb8 authored by osdamv Committed by Vijay Vasudevan: Refactor and implementation of the camera API 1, it fixes tensorflow#8736 (tensorflow#10771) List of commits in this CL: --- Commit 4464503 authored by A. This dataset contains network maps for the NSW transport network. I don't know how to use the Intel Research Lab dataset (. The code for TF-Ranking including samples and tutorials is available on GitHub. How you get batches of data will be shown later in this tutorial. Without using num_parallel_calls in my dataset. For medium-size datasets, you might want to use tf. Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Will RTAB-MAP work without " /tf " topic ? Based on the answer below. For performance reasons however, in many cases it will be desirable to compile parts of your code into a graph. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. The image component would have a data type of tf. We use cookies for various purposes including analytics. If one component of shape is the special value -1, the size of that dimension is computed so that the total size remains constant. Should I read the file and publish the lase_scan and tf topic? If so,how should I do in detail? thanks. Fun with tf. Navigate through the available datasets until you find ml-datasets, and then select the ulb-fraud-detection table within it: Click around each tab to find out more about the dataset: The Schema tab describes the data types. \] There are several variants on the definition of term frequency and document frequency. Two of the most popular general Segmentation datasets are: Microsoft COCO and PASCAL VOC. launch and 2. This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. The new Open Images dataset gives us everything we need to train computer vision models, and just happens to be perfect for a demo!Tensorflow’s Object Detection API and its ability to handle large volumes of data make it a perfect choice, so let’s jump right in…. Additionally, we have created the TF-Slim Image Models library, which provides definitions and training scripts for many widely used image classification models, using standard datasets. We will cover: Handeling Customized Dataset, Restore Backbone with Keras's application API, Restore Backbone from disk.