Tensorflow Dataset Map

At each upsampling stage we concatenate the output from the previous layer with that from its counterpart in the compression stage. Welcome to Machine Learning Studio, the Azure Machine Learning solution you’ve grown to love. Deep-Learning-TensorFlow Documentation, Release stable This repository is a collection of various Deep Learning algorithms implemented using the TensorFlow library. The Databricks Runtime is built on top of Apache Spark and is natively built for the Azure cloud. load() function is invoked. float64 intermediate and return values are used for integer. The TensorFlowOnSpark program below performs distributed training of Inception using the ImageNet data set. TypeError: missing 2 required positional arguments: 'coarse_label' and 'label' when running my code. This blog will showcase Object Detection using TensorFlow for Custom Dataset. Arts and Entertainment Reference, Education and News. This TensorFlow Dataset tutorial will show you how to use this Dataset framework to enable you to produce highly efficient input data pipelines. In all those articles, we focused on how Self-Organizing Maps utilize unsupervised learning for clustering data. 04, cuda 10, cudnn7. Dataset namespace that enables programmers to construct a pipeline of data by means of method chaining so-called lazy operators, e. Note: If eager is not enabled, this function will be executed as a tensorflow graph (in tf. A Gaussian mixture model. map(map_func), TensorFlow defines a subgraph for all the ops created in the function map_func, and arranges to execute it efficiently in the same session as the rest of your graph. Flexible Data Ingestion. Learn how to use the Custom Vision service to build custom image classifiers in the Azure cloud. Map: In Map transformation, you. 0 (we'll use this today!) Easier to use. CIFAR-10 dataset. js understands well. I use TensorFlow 1. 0, return padded and batched tensors from text data where each line is a sentence. 0 builds on the capabilities of TensorFlow 1. If you would like to see the results I achieved with a GTX 980 Ti on the dataset. Datasets和Estimators是Tensorflow中两个最重要的模块:Dataset是创造input pipeline的最佳实践;Estimator是一个封装好的比较高层的创建Tensorflow模型的方法,Estimator包括预先训练好的模型,也可以用来创建自…. A solution here might be to extend the map function to support generators. What happens if the filter doesn’t evenly map to the size of the input ? as opposed to the entire dataset. Since they are TensorFlow ops, they are executed in C++ and in parallel with model training. It also ensures that the GPU has never to wait for new data to come in. map(_parse_function). TFRecordDataset class enables you to stream over the contents of one or more TFRecord files as part of an input pipeline. Dataset download The dataset for fine-tuning the pre-trained model was prepared using over 600 traffic light images from ImageNet6. map(f) function which can process the data. decode_example)). 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. List of Data Loader Patterns for Tensorflow. TensorFlow's Dataset API handles many common cases for feeding data into a model. There are many options to indexing, which give numpy indexing great power, but with power comes some complexity and the potential for confusion. To do this, we need the Images, matching TFRecords for the training and testing data, and then we need to setup the. # source data - numpy array data = np. I reinstalled ubuntu 18. map method to apply a function to each element of a Dataset. Out of the many possible weight parameters we can generate, the goal is to find one that best fits the data. TensorFlow is an end-to-end machine learning platform for experts as well as beginners, and its new version, TensorFlow 2. 텐서플로의 pipeline framework인 Dataset에 대해서 알아봅니다. FeatureConnector for integer class labels. Once the dataset is in TF, you can process it, for example, you can use. Whether you're just learning to code or you're a seasoned machine learning practitioner, you'll find information and exercises in this resource center to help you develop your skills and advance your projects. This isn't my first problem with the tut. 0, return padded and batched tensors from text data where each line is a sentence. TensorFlow Tutorial For Beginners Learn how to build a neural network and how to train, evaluate and optimize it with TensorFlow Deep learning is a subfield of machine learning that is a set of algorithms that is inspired by the structure and function of the brain. It is possible to do all of this with the original torch-based pix2pix (in which case you have to install torch instead of tensorflow for step 3. The primary aim is to help you get familiar with the basic terminology, concepts and syntax around training models with TensorFlow. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. GoogLeNet and Directional Feature Maps. 3版本中引入的一个新的模块,主要服务于数据读取,构建输入数据的pipeline。此前,在TensorFlow中读取数据一般有两种方法:使用placeholder读内存中的数据使用queue读硬盘中的数据(关…. CVPR 2016 (To appear). Once the data is loaded in with. Apply this function to each item in the dataset using the map method. The dataset contains over ten million URLS of images from various classes. However, when I ran eval. ImageNet dataset with 21,000 labels [15]. decode_example)). js understands well. The Tensorflow model, trained on the Ciryscapes dataset is here. 0 builds on the capabilities of TensorFlow 1. I reinstalled ubuntu 18. This API is much more performant than using feed_dict or the queue-based pipelines, and it's cleaner and easier to use. We will use this dataset to train a binary classification model, able to predict whether a review is positive or negative. How to use Dataset in TensorFlow. This guide is an introduction to the data analysis process using the Python data ecosystem and an interesting open dataset. This might be a case-by-case problem. Data can be feed into TensorFlow using iterator. Using the new Dataset API from TensorFlow 1. Register now. If it was a neural neutral the computations were definitely faster. dataset = dataset. In addition to batch, repeat, and shuffle, there are many other functions the TensorFlow Dataset API comes with. Creating a custom prediction routine with Keras Walks through creating a custom prediction routine with TensorFlow Keras. 03s to preprocess 10K records. This tutorial is among a series explaining how to structure a deep learning project: first post: installation, get started with the code for the projects; second post: (TensorFlow) explain the global structure of the. Datasets is a new way to create input pipelines to TensorFlow models. Models converted from Keras or TensorFlow tf. TensorFlow Datasets package is the easiest way to load pre-defined data. In this article, I will be sharing how we can train a DeepLab semantic segmentation model for our own data-set in TensorFlow. As our dataset is a flat array, we will need to pass the shape as the second parameter to this function. Args: tfexample_data: Data or dictionary of data, as read by the tf-example reader. - speed - running time in ms per 600x600 image - mAP stands for mean average precision, which. pip3 install --upgrade tensorflow-gpu. We're devoting this article to —a data structure describing the features that an Estimator requires for training and inference. js and sheds light onto the limits of using machine learning in the frontend. Tensorflow detection model zoo. We used the same dataset of drum performances as Groove to train Drumify. TensorFlow is an end-to-end machine learning platform for experts as well as beginners, and its new version, TensorFlow 2. The estimator uses a CNN Classifier with 2 convolution layers (32 & 64 feature maps respectively, map size [5,5]) and 2 densely connected layers. In this tutorial, we are going to be covering some basics on what TensorFlow is, and how to begin using it. At the New York Summit a few days ago we launched two new features: a new batch inference feature called Batch Transform that allows customers to make predictions in non-real time scenarios across petabytes of data and Pipe Input Mode support for TensorFlow containers. When you use Dataset. Datasets和Estimators是Tensorflow中两个最重要的模块:Dataset是创造input pipeline的最佳实践;Estimator是一个封装好的比较高层的创建Tensorflow模型的方法,Estimator包括预先训练好的模型,也可以用来创建自…. 04, cuda 10, cudnn7. io Find an R package R language docs Run R in your browser R Notebooks. Data can be feed into TensorFlow using iterator. Decode the feature dict to TF compatible input. In this project we cover several segments as follows: [x] YOLO v3 architecture [x] Training tensorflow-yolov3 with GIOU loss function [x] Basic working demo [x] Training pipeline [x] Multi-scale training method [x] Compute VOC mAP. The Dataset is a high-level TensorFlow APIs which makes a more streamlined and efficient way of creating data input pipelines. After that, in the third article, we have done the same thing in a different technology and implemented Self-Organizing Maps using C#. We look forward to hearing from you about what other uses you come up with for it! Additional Resources. The TensorFlow Estimator census sample is the introductory example for AI Platform. 12 in eager execution. If you haven’t signed up yet, try Databricks now. 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. The Tensor Processing Unit (TPU) hardware accelerators we will be using in this lab are very fast. I've followed the instructions here (Create Mobile Map Package—Data Management toolbox | ArcGIS Desktop ) and my network dataset follows all of the conventions described on this page. In this post, I will show you how to turn a Keras image classification model to TensorFlow estimator and train it using the Dataset API to create input pipelines. In computer vision, image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as image objects). Try any of our 60 free missions now and start your data science journey. GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. It is based on the map() function that is commonly applied to lists (and other structures) in functional programming languages. map(func) 由于从tfrecord文件中导入的样本是刚才写入的tf_serialized序列化样本,所以我们需要对每一个样本进行解析。. In this tutorial we will learn how to use TensorFlow's Dataset module tf. In this article, I will be sharing how we can train a DeepLab semantic segmentation model for our own data-set in TensorFlow. I have a very expensive function which I map onto this dataset using tf. t('satellite') | capitalize }} {{ shared. from_tensors: It also accepts single or multiple numpy arrays or tensors. tensorflow was needed just occasionally, or not at all. 0), improves its simplicity and ease of use. If dataset1 would be a TensorFlow Dataset, then each Tuple is an element consisting of two components. The answer to it seems convoluted, and I can't imagine such functionality does not exist, as it would seem like a common task. After training, the encoder is discarded and controller sequences are provided by user input. Learning rate and radius (sigma) are both initialized to 0. Artificial neural networks are statistical learning models, inspired by biological neural networks (central nervous systems, such as the brain), that are used in machine learning. The TensorFlow Object Detection API provides detailed documentation on adapting and using existing models with custom datasets. We are going to use the Boston dataset in the MASS package. The basic process for training a model is: Convert the PASCAL VOC primitive dataset to a TFRecord. If it was a neural neutral the computations were definitely faster. in tfdatasets: Interface to 'TensorFlow' Datasets rdrr. Since they are TensorFlow ops, they are executed in C++ and in parallel with model training. For this post, I'm using TensorFlow with Keras, using some of the new APIs from TensorFlow I/O for the integration with Kafka. map ( lambda qus1, qus2, labels:. The toolbox supports transfer learning with a library of pretrained models (including NASNet, SqueezeNet, Inception-v3, and ResNet-101). (d) Try out your machine learning model to check it's working (e) Evaluate your machine learning model, using a second topic as a source of test data. tensorflow object detection API是一个开源的基于tensorflow的框架,使得创建,训练以及应用目标检测模型变得简单。 在谷歌我们已经确定发现这个代码对我们的计算机视觉研究需要很有用,我们希望这个对你也会很有用。. It shows the step by step how to integrate Google Earth Engine and TensorFlow 2. Facilities to help determine the appropriate number of components are also provided. FeatureConnector for integer class labels. Download the dataset. For a dataset created with the csv_dataset() function, the passed. You can vote up the examples you like or vote down the ones you don't like. In addition to batch, repeat, and shuffle, there are many other functions the TensorFlow Dataset API comes with. com/Hvass-Labs/TensorFlow-Tutorials. decision boundary. x to TensorFlow 2. TensorFlow dataset. Due to Python’s dynamic nature, we don’t need the Dataset to be strongly-typed in Python. Transform is a library for TensorFlow that allows you to define both instance-level and full-pass data transformations through data preprocessing pipelines. With an appropriate number of photos (my example have 50 photos of dog), I created the annotations. T" is the transpose function. dataset = dataset. tensorflow/datasets is a library of public datasets ready to use with TensorFlow. In your entry_point script, you can use PipeModeDataset like. Creating Label Map¶ TensorFlow requires a label map, which namely maps each of the used labels to an integer values. In this tutorial we will learn how to use TensorFlow’s Dataset module tf. We will use this dataset to train a binary classification model, able to predict whether a review is positive or negative. This is a well-known dataset in the world of neural networks. To use your own dataset in Tensorflow Object Detection API, you must convert it into the TFRecord file format. [email protected] Your new model should now appear in the Resources panel. We look forward to hearing from you about what other uses you come up with for it! Additional Resources. 0 with image classification as the example. pbtxt), assuming that our dataset containes 2 labels, dogs and cats:. Coupled hover events with Shiny and Plotly. 3, we expect to move this API to core at 1. map(map_func), TensorFlow defines a subgraph for all the ops created in the function map_func, and arranges to execute it efficiently in the same session as the rest of your graph. The simplified method should also not be used in cases where the data set is truncated; that is, when the Spearman correlation coefficient is desired for the top X records (whether by pre-change rank or post-change rank, or both), the user should use the Pearson correlation coefficient formula given above. It also ensures that the GPU has never to wait for new data to come in. We have delivered and continue to deliver "Machine Learning using Tensorflow" training in India, USA, Singapore, Hong Kong, and Indonesia. (d) Try out your machine learning model to check it's working (e) Evaluate your machine learning model, using a second topic as a source of test data. Furthermore, important changes have recently been made to Tensorflow's Object Detection api, that made obsolete other available tutorials. In the next code segment, I setup the training dataset using the Dataset API in TensorFlow, and extracting the CIFAR10 data from the Keras datasets library:. We pride ourselves on high-quality, peer-reviewed code, written by an active community of volunteers. These missions study rainfall and other types precipitation around the globe. This is a well-known dataset in the world of neural networks. TensorFlow tutorials. Loading Unsubscribe from Bijoy CP? How to create a 3D Terrain with Google Maps and height maps in Photoshop - 3D Map Generator Terrain - Duration:. We will do this using transposed convolution (also known as deconvolution). Create Dataset TensorFlow Bijoy CP. Models are indicated by the model icon:. dataset_map: Map a function across a dataset. If you want to know the details, you should continue reading! Motivation. By storing datasets in-memory during a job, Spark has great performance for iterative queries common in machine learning workloads. We will get to know, how to improve the linear model which will use in TensorFlow by adding explicit kernel methods to the model. Generator ,you can write a generator to read data for network:first, generator won’t eat your whole RAM, it just eat the. We also have state-of-art training facilities based on client requirement. Dialogflow is the most widely used tool to build Actions for more than 400M+ Google Assistant devices. imshow(image). Semantic segmentation is understanding an. def input_fn (filenames, batch_size): # Create a dataset containing the text lines. # source data - numpy array data = np. I reinstalled ubuntu 18. TensorFlow Implementation. Datasets: 입력 파이프라인을 만드는(즉, 데이터를 프로그램으로 읽어오는) 완전히 새로운 방식입니다. Note: all code examples have been updated to the Keras 2. Annotating images and serializing the dataset. Dataset download. Libraries like TensorFlow and Theano are not simply deep learning. Dataset potentially can solve most of my data pipeline woes. It shows the step by step how to integrate Google Earth Engine and TensorFlow 2. It is based very loosely on how we think the human brain works. pbtxt; and the script to convert it all to TFRecord is create_pet_tf_record. The toolbox supports transfer learning with a library of pretrained models (including NASNet, SqueezeNet, Inception-v3, and ResNet-101). The final output is a mask of size the original image, obtained via 1x1-convolution; no final dense layer is required, instead the output layer is just a convolutional layer with a single filter. In addition to batch, repeat, and shuffle, there are many other functions the TensorFlow Dataset API comes with. Reading data from the TFRecord file is much faster in TensorFlow than reading from other image formats. To use your own dataset in Tensorflow Object Detection API, you must convert it into the TFRecord file format. 1) Data pipeline with dataset API. Facilities to help determine the appropriate number of components are also provided. In this exercise you will implement a convolutional neural network for digit classification. Datasets can be created from Hadoop InputFormats (such as HDFS files) or by transforming other Datasets. tensorflow/datasets is a library of public datasets ready to use with TensorFlow. Dataset API是TensorFlow 1. Each dataset definition contains the logic necessary to download and prepare the dataset, as well as to read it into a model using the tf. Welcome to Part 2 of a blog series that introduces TensorFlow Datasets and Estimators. This TensorFlow Image Classification article will provide you with a detailed and comprehensive knowlwdge of image classification. The first argument is a string specifying the dataset name to load. Once the dataset is in TF, you can process it, for example, you can use. I have a very expensive function which I map onto this dataset using tf. Return the shape/dtype of features after encoding (for the adapter). When looking at the config file used for training: the field anchor_generator looks like this: (which follow. Loading Unsubscribe from Bijoy CP? How to create a 3D Terrain with Google Maps and height maps in Photoshop - 3D Map Generator Terrain - Duration:. Dataset download The dataset for fine-tuning the pre-trained model was prepared using over 600 traffic light images from ImageNet6. If you do have a function that returns a Dataset, you can use Dataset. The dataset contains over ten million URLS of images from various. 0), improves its simplicity and ease of use. However, by fusing the two transformations together, the implementation can be more efficient. pbtxt), assuming that our dataset containes 2 labels, dogs and cats:. map(_parse_function). scikit-image is a collection of algorithms for image processing. 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. Functionally, it is equivalent to map followed by batch. Inside the tensorlfow zoo models repository’s folder, we have a table that explain how precise the model is (with mAP — mean Average Precision ) and how faster this model is. How to use Dataset in TensorFlow. Linear Support Vector Machines (SVMs) The linear SVM is a standard method for large-scale classification tasks. g label_map. OpenFace is a Python and Torch implementation of face recognition with deep neural networks and is based on the CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering by Florian Schroff, Dmitry Kalenichenko, and James Philbin at Google. This book will help you understand and utilize the latest TensorFlow features. It also ensures that the GPU has never to wait for new data to come in. However, by fusing the two transformations together, the implementation can be more efficient. x to TensorFlow 2. 0 builds on the capabilities of TensorFlow 1. The dataset has 65,000 one-second long utterances of 30 short words, by thousands of different people, contributed by members of the public through the AIY website. js weights manifest. 12 in eager execution. 0 seamlessly; Book Description. This looks like the following:. Implementation of YOLO v3 object detector in Tensorflow. If you would like to see the results I achieved with a GTX 980 Ti on the dataset. The code is available in TensorFlow. It is sort of "Hello World. The lowest level TensorFlow API, TensorFlow Core, provides the complete programming control, recommended for machine learning researchers who require fine levels of control over their model. Photo by Fredy Jacob on Unsplash Summary. shuffle(180000). Return the shape/dtype of features after encoding (for the adapter). Classification. This computes the internal data stats related to the data-dependent transformations, based on an array of sample data. In this example, 6×6 Self-Organizing Map is created, with the 4 input nodes (because data set in this example is having 4 features). Linear Support Vector Machines (SVMs) The linear SVM is a standard method for large-scale classification tasks. Hence, going forward in this article, I am referring ‘Dataset’ (capital D) as Tensorflow Dataset class and ‘dataset’ as dataset of X_train, y_train etc. momentum: Momentum for the moving mean and the moving variance. mixture is a package which enables one to learn Gaussian Mixture Models (diagonal, spherical, tied and full covariance matrices supported), sample them, and estimate them from data. flat_map() to flatten and concatenate all of the returned datasets into a single dataset, as follows:. SageMaker TensorFlow provides an implementation of tf. dataset in TF 2. Conclusion. Welcome to Part 2 of a blog series that introduces TensorFlow Datasets and Estimators. zip and test1. The datasets introduced in Chapter 6 of my PhD thesis are below. Hence, going forward in this article, I am referring 'Dataset' (capital D) as Tensorflow Dataset class and 'dataset' as dataset of X_train, y_train etc. This is a well-known dataset in the world of neural networks. In the Federated EMNIST data, the value of 1. 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. Develop, manage, collaborate, and govern at scale with our enterprise platform. The TensorFlow Object Detection API provides detailed documentation on adapting and using existing models with custom datasets. To do this, we need the Images, matching TFRecords for the training and testing data, and then we need to setup the. You can also watch the Spark Summit presentation on A Tale of Three Apache Spark APIs: RDDs vs DataFrames and Datasets. Migrate your existing code from TensorFlow 1. Each color index represents a unique class (with unique color) known as a color map. pip3 install --upgrade tensorflow-gpu. In this example, 6×6 Self-Organizing Map is created, with the 4 input nodes (because data set in this example is having 4 features). map(_parse_function). Hardened according to a CIS Benchmark - the consensus-based best practice for secure configuration. center: If True, add offset of beta to normalized tensor. To implement the convolutional neural network, we will use a deep learning framework called Caffe and some Python code. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and. The Dataset. pbtxt; and the script to convert it all to TFRecord is create_pet_tf_record. The images and annotations used above come from the Oxford-IIIT Pets Dataset; the labels map is pet_label_map. In the battle of "best" data science tools, python and R both have their pros and cons. Dataset (and actually a little more than that). Describes the sample applications made for AI Platform. There are four sections covering selected topics as munging data, aggregating data, visualizing data and time series. - speed - running time in ms per 600x600 image - mAP stands for mean average precision, which. Functionally, it is equivalent to map followed by batch. As usual for any machine learning task, the first step is to prepare the training and validation data. These models can be useful for out-of-the-box inference if you are interested in categories already in those datasets. After pre-processing and cleaning of the. This package is intended as a command line utility you can use to quickly train and evaluate popular Deep Learning models. TensorFlow Datasets package is the easiest way to load pre-defined data. This is a well-known dataset in the world of neural networks. to_categorical. The model was trained using Tensorflow and converted to a web application using Tensorflow. The Caltech dataset consisted of images in the jpg format and their corresponding annotations in XML format. 0 builds on the capabilities of TensorFlow 1. dataset in TF 2. tensorflow was needed just occasionally, or not at all. Thousands of datasets can be stored in a single file, categorized and tagged however you want. ImageNet dataset with 21,000 labels [15]. Computerworld covers a range of technology topics, with a focus on these core areas of IT: Windows, Mobile, Apple/enterprise, Office and productivity suites, collaboration, web browsers and. Our first step will be to create our training and testing dataset. arange(10) # create a dataset from numpy array dataset = tf. But we already preprocess our dataset and all we need to do is apply batching and, maybe, shuffling. 0 in the same pipeline (EE->Tensorflow->EE). TensorFlow Datasets package is the easiest way to load pre-defined data. As our dataset is a flat array, we will need to pass the shape as the second parameter to this function. MapPLUTO merges PLUTO tax lot data with tax lot features from the Department of Finance's Digital Tax Map (DTM) and is available as shoreline clipped and water included. Welcome to part two of Deep Learning with Neural Networks and TensorFlow, and part 44 of the Machine Learning tutorial series. We're devoting this article to —a data structure describing the features that an Estimator requires for training and inference. In the coming weeks, we’ll have a series of blogs on Structured Streaming. Google’s TensorFlow provides a Dataset API that is very useful for importing machine learning datasets. 4 version of the Dataset API also adds support for Python generators. Dataset that makes it easy to take advantage of Pipe input mode in SageMaker. Now shuffle and. When I build it I receive no errors. We look forward to hearing from you about what other uses you come up with for it! Additional Resources. Training the Primitive Model. TensorFlow Enterprise introduces some improvements in the way TensorFlow Dataset reads data from Cloud Storage. It is based on the map() function that is commonly applied to lists (and other structures) in functional programming languages. Dataset created. To learn more about Apache Spark, attend Spark Summit East in New York in Feb 2016. Reading data from the TFRecord file is much faster in TensorFlow than reading from other image formats. ), Data Wrangling, R, Python, Julia, and SQL Server. Learning rate and radius (sigma) are both initialized to 0. It seems to work, however I get the following error, Do you think this might have something to do with faulty installation?. Iris Data Set Classification Problem. The CIFAR-100 dataset This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each. Several days ago I met a problem that my computer will freeze when I call dataset. It is also assumed that model weights can be accessed from relative paths described by the paths fields in weights manifest. Packt is the online library and learning platform for professional developers. The term ML model refers to the model artifact that is created by the training process. Fun with tf. For the time being however, in TensorFlow 1. Datasets和Estimators是Tensorflow中两个最重要的模块:Dataset是创造input pipeline的最佳实践;Estimator是一个封装好的比较高层的创建Tensorflow模型的方法,Estimator包括预先训练好的模型,也可以用来创建自…. The first component is a 3D tensors containing an image (for visibility I just gave them a name and didn't try to write a three times nested list) and the second component is a vector containing symbolically the one-hot-encoding class vector.