Tensorflow Model Example







This guide uses tf. tflite) which is different from the normal TensorFlow model. For example, Shridhar et al 2018 used Pytorch (also see their blogs), Thomas Wiecki 2017 used PyMC3, and Tran et al 2016 introduced the package Edward and then merged into TensorFlow Probability. The RNN cell learns to reproduce sequences of pen points, the MDN models randomness and style in the handwriting, and the attention mechanism tells the model what to write. you need to call one of the above-mentioned methods before you try to load your weights. 6 and CUDA libraries, and then installs TensorFlow and tensorflow-compression with GPU support:. Weights are downloaded automatically when instantiating a model. 2: Based on PyTorch example from Justin Johnson. We are using nightly instead of latest because (at the time of writing) latest contains a bug that breaks TensorBoard , a data visualization tool. Moreover, across several experiments, we found that weight pruning is compatible with. For query-based example gen (e. js and later saved with the tf. In this tutorial, we're going to cover how to code a Recurrent Neural Network model with an LSTM in TensorFlow. It is an educational visualization platform for a layman. You cannot train a model directly with TensorFlow Lite; instead you must convert your model from a TensorFlow file (such as a. js is capable of building both machine learning and deep learning models in the browser. In Python, I like overriding the process name for long-running experiments with setproctitle so that I can remember what’s running when I look at the running processes on my GPUs or CPUs. To achieve the goal of the CCTV, it would be really easy to use, for example, AWS rekognition, train the model, and post a request for each image seen. This is the high-level API. save_model(). Install Google's TensorFlow library as per these instructions:. 9 compatibility! (TF v1. (Modules will be continuously added/updated in TensorFlow Hub) In each of the example, I have integrated it with Dataset and Iterator pipeline and saved with Saved_Model. This method is applicable to: Models created with the tf. Use the model to make predictions about unknown data. Multidimensional softmax; Placeholders; Q-learning; Reading the data; Save and Restore a Model in TensorFlow; Save Tensorflow model in Python and load with Java; Simple linear regression structure in TensorFlow with Python; Tensor indexing; TensorFlow GPU setup; Using 1D convolution; Using. In this post, I will present several ways of performing distributed training with TensorFlow, especially data parallel and model parallel training. In this post, we are going to see some TensorFlow examples and see how it's easy to define tensors, perform math operations using tensors, and other machine learning examples. They’re capable of localizing and classifying objects in real time both in images and videos. 0 basic operations. We will us our cats vs dogs neural network that we've been perfecting. Discriminator Model. py (Part I) CS230 project example code repository on github (Part II); Part I - Tensorflow Tutorial. That example created only numerical feature columns (of type tf. The second argument is the output layer name. #An example of building a TensorFlow model from R using rPython ### # For this script you need to # 1. The more complex a model, the harder it is to inspect and the more it tends to work like a black box. Training an Image Classification model from scratch requires setting millions of parameters, a ton of labeled training data and a vast amount of compute resources (hundreds of GPU hours). BASIC CLASSIFIERS: Nearest Neighbor Linear Regression Logistic Regression TF Learn (aka Scikit Flow) NEURAL NETWORKS: Convolutional Neural Network and a more in-depth version Multilayer Perceptron Convolutional Neural Network Recurrent Neural Network Bidirectional Recurrent Neural. linear_regression_multiple: Illustrate how a multiple linear regression (Y ~ XW + b) might be fit using TensorFlow. Follow the steps from here. the two TensorFlow AI models in the scripts/model subfolder (the simple model is stored as a folder structure, the CNN as a single file). I will try to explain what I have achieved so far. Description. TensorFlow is a multipurpose machine learning framework. If I am aggregating network flows from multiple devices (IDS, syslogs, etc. You can also save this page to your account. x) programs generate a DataFlow (directed, multi-) Graph Device independent intermediate program representation TensorFlow v2. Implement a Logistic Regression with TensorFlow 2. While it is well documented how to install TensorFlow on an Android or other small computer devices, most existing examples are for single images or batch processes, not for streaming image recognition use cases. I am trying to get a TensorFlow Lite example to run on a machine with an ARM Cortex-A72 processor. to_json() a full model JSON in the format of keras. It also has a process for converting many models trained in floating-point over to equivalent graphs using quantized calculations for inference. This same applies to load weights into a newly created instance of your subclassed model. TensorFlow is a fast, flexible, and scalable open-source machine learning library for research and production. """Randomly crops an image/images to a given size. examples that will train the neural. Now that you know about Deep Learning, check out the Deep Learning with TensorFlow Training by Edureka, a trusted online learning company with a network of more than 250,000 satisfied learners spread across the globe. , where you store the notebook or python file. In this article, you get to look over my shoulder as I go about debugging a TensorFlow model. Result: The negative values in the vector are replaced with. Generating Adversarial examples using Tensorflow(Running the code on InceptionV3): Here is the code to run inference on the image using these functions. The advantage is to avoid to type the full name of the library when we need to use it. 2019-05-20 update: I just added the Running TensorRT Optimized GoogLeNet on Jetson Nano post. We saw an example of this way back in lesson 2: import tensorflow as tf x. mnist_with_summaries. For example, in the tutorial, we show how a 90% sparse model for MNIST can be compressed from 12MB to 2MB. For this reason, the first layer in a Sequential model (and only the first, because following layers can do automatic shape inference) needs to receive information about its input shape. Instead we feed it examples of sums and let it learn from that. Generating Adversarial examples using Tensorflow(Running the code on InceptionV3): Here is the code to run inference on the image using these functions. Raw implementation of a convolutional neural network to classify MNIST digits dataset. Example: A pair of inputs/outputs used during training. In order to understand the following example, you need to understand how to do the following:. filepath can contain named formatting options, which will be filled the value of epoch and keys in logs (passed in on_epoch_end ). // Create the model const model = createModel(); tfvis. Listing 3 shows the Keras code for the Discriminator Model. Discriminative vs. Model package handles interaction with TensorFlow backed machine learning models. You will need to train your own model with tensorflow in order to make it work properly. Example TensorFlow script for finetuning a VGG model on your own data. If TensorFlow is your primary framework, and you are looking for a simple & high-level model definition interface to make your life easier, this tutorial is for you. This example trains and registers a TensorFlow model to classify handwritten digits using a deep neural network (DNN). Tensorflow's Optimizers Example: OCR task on MNIST dataset A1 released: Jan 20: Assignment #1 released : Lecture: Jan 25 Week 3: Structure your TensorFlow model Example: word2vec Suggested Readings: A lot. First, a collection of software “neurons” are created and connected together, allowing them to send messages to each other. import_graph_def(). For the doodle, which runs the model in the browser using a Tensorflow. This stores the raw image string feature, as well as the height, width, depth, and arbitrary label feature. numeric_column). actual prediction, put your machine learning model on the physical device itself, it can lead to some amazing user experiences. Basic Models in TensorFlow from tensorflow. relu) and pass a vector (or any array to it). Python (Theano, Tensorflow) vs others. Basic Operations. We were testing the models’ predictions on 6 months of new data where, for each user, it could see all 10 days of their trial period at once. examples that will train the neural. This example trains a Recurrent Neural Network to do addition without explicitly defining the addition operator. This site may not work in your browser. Is there any example code for deploying a Tensorflow Model via a RESTful API? I see examples for a command line program and for a mobile app. parse_single_example decoder. An alternative to this would be calling tf. Tensorflow Framework is the popular framework to design a neural network in Machine Learning. The example allows users to change the image size, explore auto-tuning, and manually set the LMS tunable parameters. parse_single_sequence_example. In other words, the conceptual model trains multiple outputs to the same input simultaneously. With Deep Cognition you can choose from a simple but powerful GUI where you can drag and drop neural networks and create Deep Learning models with AutoML, to a full autonomous IDE where you can code and interact with your favorite libraries. Prerequisites. NET model makes use of part of the TensorFlow model in its pipeline to train a model to classify images into 3 categories. This example demonstrates how to do model inference using TensorFlow with pre-trained ResNet-50 model and TFRecords as input data. The networks used in this tutorial include ResNet50, InceptionV4 and NasNet. data APIs in TensorFlow 2. io as io cat_img = io. CC-BY by Felipe Venâncio. The lowest level API, TensorFlow Core provides you with complete programming control. For example, the largest operation allows the data scientist to gauge the size of the model and how much swapping will be performed. It allows you to train neural networks to do inference, for example image recognition, natural language processing, and linear regression. A node can only belong to a single layer. In this tutorial, we'll build a TensorFlow. I'm trying out Tensorflow's rnn example. Read the training data into placeholders. This tutorial was designed for easily diving into TensorFlow, through examples. model quality performance on different feature slices; Exporting the EvalSavedModel from your model. TensorFlow has production-grade support for eight-bit calculations built it. Tensorflow sucks. filepath can contain named formatting options, which will be filled the value of epoch and keys in logs (passed in on_epoch_end ). Tadpoles on TensorFlow: Hierarchical partial pooling with tfprobability. BASIC CLASSIFIERS: Nearest Neighbor Linear Regression Logistic Regression TF Learn (aka Scikit Flow) NEURAL NETWORKS: Convolutional Neural Network and a more in-depth version Multilayer Perceptron Convolutional Neural Network Recurrent Neural Network Bidirectional Recurrent Neural. Project description. The TensorFlow Core census sample can be used to run through all of the same steps demonstrated in the Estimator API sample. TensorFlow Keras Model Training Example with Apache Arrow Dataset - tf_arrow_model_training. This iteration will start the Tensorflow execution and produce the actual result. TensorFlow is a multipurpose machine learning framework. Run your Keras models in C++ Tensorflow So you’ve built an awesome machine learning model in Keras and now you want to run it natively thru Tensorflow. Install the rPython package in R. Of course, please note that the tensorflow android detector example doesn’t use the YOLO model by default. The following command will convert an Inception v3 TensorFlow model into a SNPE DLC file. And there are many pre-processing and post processing in each algorithm, (For example in the example letter, the sender and receivier's address are not in same size, after detecting each text erea, we need to re-scale them. This example uses the TensorFlow starter model for object detection: COCO SSD Quantized MobileNet V1 neural network model. NULL means that the output of the model will be the 4D tensor output of the last convolutional layer. The following are code examples for showing how to use tensorflow. the following link provide as good reference on how the model is saved and restored A quick complete tutorial to save and restore Tensorflow models however. You can (re)train a TensorFlow model and use our compiler to convert the model into binary file to run on Vision Kit. Raspberry Pi, TensorFlow Lite and Qt: object detection app. Tensorflow Framework is the popular framework to design a neural network in Machine Learning. A TensorFlow Lite model takes as input and produces as output one or more multidimensional arrays. But to be honest, I found it quite cumbersome (e. Install the rPython package in R. js is capable of building both machine learning and deep learning models in the browser. Let's see an example: c = []. A one that I have recently tried was this : smallcorgi/Faster-RCNN_TF which is very straightforward to run, just follow the instructions to build the model and downloa. Building Faster R-CNN on TensorFlow: Introduction and Examples The widespread adoption of Convolutional Neural Networks (CNNs) has driven progress in deep learning for computer vision , and especially in object detection. As tensorflow is a low-level library when compared to Keras , many new functions can be implemented in a better way in tensorflow than in Keras for example , any activation fucntion etc… And also the fine-tuning and tweaking of the model is very flexible in tensorflow than in Keras due to much more parameters being available. TensorFlow is an open source library for high-performance numerical computation. All nodes belonging to a layer must be defined in a unique TensorFlow scope. Training an Image Classification model from scratch requires setting millions of parameters, a ton of labeled training data and a vast amount of compute resources (hundreds of GPU hours). layers import Input, Dense a = Input(shape=(32,)) b = Dense(32)(a) model = Model(inputs=a, outputs=b) This model will include all layers required in the computation of b given a. See the Python converter function save_model() for more details. Write the TFRecord file. in order to save the model and the model params, we need to explicitly specify. In this post you will discover how to develop and evaluate neural network models using Keras for a regression problem. In this post you will discover how to develop and evaluate neural network models using Keras for a regression problem. See the ML Kit quickstart sample on GitHub for an example of this API in use. In Tutorials. The false positive rate is defined as follows:. js that make training machine learning models practical, we need to convert our data to tensors. After some time with Keras, I recently switched to pure TensorFlow and now I want to be able to finetune the same network as previously, but using just TensorFlow. This site may not work in your browser. Tensorflow sucks. Google is trying to offer the best of simplicity and. Since we introduced the Model Optimization Toolkit — a suite of techniques that both novice and advanced developers can use to optimize machine. They are intended to be well-maintained, tested, and kept up to date with the latest stable TensorFlow API. Hence, in this TensorFlow Linear Model tutorial, we saw the linear model with the kernel method. These models can be used for prediction, feature extraction, and fine-tuning. For an official introduction to the Tensorflow concepts of Graph() and Session(), check out the official introduction on tensorflow. This is done with the argument model_dir. Use TensorFlow 2. com/franktheunicorn/predict-pr-c…. 2 - Basic Models. It takes a computational graph defined by users, and automatically adds swap-in and swap-out nodes for transferring tensors from GPUs to the host and vice versa. This model is also an example where we take in raw pixels as numeric values without using feature columns (and input_layer). This post is a first introduction to MCMC modeling with tfprobability, the R interface to TensorFlow Probability (TFP). This was one of the first and most popular attacks to fool a neural network. tflite) which is accepted by the TensorFlow Lite. 2016 A short early release paper to close out the week this week, which looks at how to support machine learning and data mining (MLDM) with Google's TensorFlow in a distributed setting. js kept the API that is similar to the TensorFlow API in Python. This codelab uses TensorFlow Lite to run an image recognition model on an Android device. Remove training specific code from the network, and add code to read in the previously saved network to create an inference only version. That is, the model will see 100 times the images to optimized weights. NET you can load a frozen TensorFlow model. Keras is a high-level neural networks API developed with a focus on enabling fast experimentation. Building Faster R-CNN on TensorFlow: Introduction and Examples The widespread adoption of Convolutional Neural Networks (CNNs) has driven progress in deep learning for computer vision , and especially in object detection. They are stored at ~/. The following example creates models named mymodel in mydataset in your default project. subpixel: A subpixel convolutional neural network implementation with Tensorflow Image Completion with Deep Learning in TensorFlow (August 9, 2016) How to Classify Images with TensorFlow (google research blog, tutorial) TensorFlow tutorials of image-based examples on GitHub - where cifar10 contains how to train and evaluate the model. The science behind introducing non-linearity is outside the scope of this example. The model is a stack of convolutional layers with small 3×3 filters followed by a max pooling layer. Select the export format, and then select Export to download the model. Read more →. Eager execution (an preview feature in TensorFlow 1. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. The R interface to TensorFlow includes a variety of tools designed to make exporting and serving TensorFlow models straightforward. This API requires Android SDK level 16 (Jelly Bean) or newer. Our simple regression model predicts 7. Save and Restore a Model in TensorFlow; Save Tensorflow model in Python and load with Java; Simple linear regression structure in TensorFlow with Python; Tensor indexing; TensorFlow GPU setup; Using 1D convolution; Using Batch Normalization; Using if condition inside the TensorFlow graph with tf. Our model finish to train with an overall accuracy around 81%, which means that it can predict the correct outcome (survived or not) for 81% of the total passengers. The following command will convert an Inception v3 TensorFlow model into a SNPE DLC file. 556, which is very close to the expected value of 8. In part 1, Creating Insanely Fast Image Classifiers with MobileNet in TensorFlow, we covered how to retrain a MobileNet on a new dataset. This is TensorFlow API that contains all the mathematical computations. As an example, we show how we can label a set of images from a stock neural network model that was already trained. We will use an Adam optimizer with a dropout rate of 0. TensorFlow Models. Build a model, 2. Um, What Is a Neural Network? It’s a technique for building a computer program that learns from data. Here’s an example generated by the model: Note that this isn’t a performance of an existing piece; the model is also choosing the notes to play, “composing” a performance directly. TensorFlow has a concept of a summaries, which allow you to keep track of and visualize various quantities during training and evaluation. TensorFlow. This example uses the TensorFlow starter model for object detection: COCO SSD Quantized MobileNet V1 neural network model. For visualizing TensorFlow results, the Docker ® image also contains TensorBoard. You can use ML Kit to perform on-device inference with a TensorFlow Lite model. layers import Input, Dense a = Input(shape=(32,)) b = Dense(32)(a) model = Model(inputs=a, outputs=b) This model will include all layers required in the computation of b given a. Hence, in this TensorFlow Convolutional Neural Network tutorial, we have seen TensorFlow Model Architecture, prediction of CIFAR 10 Model, and code with the example of CNN. Basic and unambiguous, this example presents many of the core elements of TensorFlow and the ways in which it is distinct from a regular Python program. Introduction to TensorFlow Alejandro Solano - EuroPython 2017 gates where we introduce example Model: makes predictions. Network architecture. 1, the Estimator API is now at tf. Moreover, across several experiments, we found that weight pruning is compatible with. I'm trying out Tensorflow's rnn example. Examples This page is a collection of TensorFlow examples, that we have found around the web for your convenience. In this short post we provide an implementation of VGG16 and the weights from the original Caffe model converted to TensorFlow. In a previous post, I built an image classification model for mushrooms using CustomVision. Now that you know about Deep Learning, check out the Deep Learning with TensorFlow Training by Edureka, a trusted online learning company with a network of more than 250,000 satisfied learners spread across the globe. For example, Shridhar et al 2018 used Pytorch (also see their blogs), Thomas Wiecki 2017 used PyMC3, and Tran et al 2016 introduced the package Edward and then merged into TensorFlow Probability. The network has two inputs:. The model needs to know what input shape it should expect. The frozen model model / frozen_inference_graph. This sample application was created to present an example for TensorFlow Java API usage. The advantage is to avoid to type the full name of the library when we need to use it. Once again, you can notice that TensorFlow. save_model(). # to translate the above python expression to R, write: x[all_dims(), i] ## End(Not run) evaluate Evaluate a Model Description Evaluate a model object. TensorFlow 10 The best example to illustrate “Unsupervised learning” is with a bunch of photos with no information included and user trains model with classification and clustering. That example created only numerical feature columns (of type tf. We compile our model with Adam optimizer and cathegorical crossentropy. In this tutorial, we'll build a TensorFlow. TensorFlow on Jetson Platform TensorFlow™ is an open-source software library for numerical computation using data flow graphs. TensorFlow is a fast, flexible, and scalable open-source machine learning library for research and production. Image Classification on Small Datasets with Keras. This post presents WaveNet, a deep generative model of raw audio waveforms. The tensorflow image processing platform allows you to detect and recognize objects in a camera image using TensorFlow. TensorFlow Model: A trained TensorFlow model saved on disk. In this blog, we will build out the basic intuition of GANs through a concrete example. You can use all these features without using any TensorFlow APIs—all you need is a compiled TensorFlow Lite model and the Edge TPU Python library. I did a lot of dumb things, so please don't judge. As you read the example’s README. But because the. fit(train_dataset, epochs=60, validation_data=test_dataset, validation_freq=1) Notice in this example, the fit function takes TensorFlow Dataset objects (train_dataset and test_dataset). This post will serve as a simple end-to-end example of how to use your own tensorflow-model to do inference in your go-application. height: `float`. TensorFlow Lite Model File : A model file format based on FlatBuffers , that has been optimized for maximum speed and minimum size. cond; Basic example; define and use functions f1. In the first two line of code, we have imported tensorflow as tf. This example trains a Recurrent Neural Network to do addition without explicitly defining the addition operator. In math, tensors are geometric objects that describe linear relations between other geometric objects. I want to train an SSD detector on a custom dataset of N by N images. In the previous module, you have seen the power of tf. It uses on-board machine learning to overlay the translated text. BASIC CLASSIFIERS: Nearest Neighbor Linear Regression Logistic Regression TF Learn (aka Scikit Flow) NEURAL NETWORKS: Convolutional Neural Network and a more in-depth version Multilayer Perceptron Convolutional Neural Network Recurrent Neural Network Bidirectional Recurrent Neural. CloudML uses Bayesian optimization to tune hyperparameters of models as described in this blog post. The code here has been updated to support TensorFlow 1. A complete guide to using Keras as part of a TensorFlow workflow. Initially, the default Tensorflow object detection model takes variable batch size, it is now fixed to 1 since the Jetson Nano is a resource-constrained device. The second argument is the output layer name. This examples lets you train a handwritten digit recognizer using either a Convolutional Neural Network (also known as a ConvNet or CNN) or a Fully Connected Neural Network (also known as a DenseNet). TensorFlow is one of the popular deep learning frameworks out there in the open source community. This iteration will start the Tensorflow execution and produce the actual result. mnist_transfer_cnn: Transfer learning toy example. In the previous post - Testing TensorFlow Lite image classification model, we built a notebook that exports TensorFlow model to TensorFlow Lite and compares them side by side. Simple model that learns W and b by minimizing mean squared errors via gradient descent. Visualize high dimensional data. Instead we feed it examples of sums and let it learn from that. Ok, it’s not as simple as that,. MobileNets are open-source Convolutional Neural Network (CNN) models for efficient on-device vision. This iteration will start the Tensorflow execution and produce the actual result. pb file) to a TensorFlow Lite file (a. Lastly, we declare a variable model and assign it to the defined architecture (model = NeuralNet()). Our example is a multi-level model describing tadpole mortality, which may be known to the reader from Richard McElreath's wonderful "Statistical. Next steps. It uses Image classification to continuously classify whatever it sees from the device's back camera. Is there a framework for this or people just load the m. Tensorflow playground is a neural network playground. Originally developed by researchers. No matter what format the output of your machine learning framework is, it can be embedded into applications to use for predictions via the framework’s API (e. js weights manifest. It has been possible to train a face recognition model. If you want to implement your own estimator, this also gives you freedom to decide whether targets can be ignored if the estimator can be trained in unsupervised fashion. ModelCheckpoint. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed-up experimentations, while remaining fully transparent and compatible with it. BigQueryExampleGen, PrestoExampleGen), pattern is a SQL query. pb can be deployed in, for example, Object Recognition with the Computer Vision Library Tensorflow. TensorFlow calls them estimators Low-level API: Build the. 7 kernel and follow the steps below. yaml and later loaded into a new model via the model_from_yaml() function. Have python 2. This post will serve as a simple end-to-end example of how to use your own tensorflow-model to do inference in your go-application. As previously mentioned, it can also take numpy ndarrays as the input. data pipeline, they don't use tf. In TesnsorFlow they are multi-dimensional array or data, ie. Our x_train variable is a 55,000 row and 784 column matrix. TensorFlow Lite for mobile and embedded devices For Production TensorFlow Extended for end-to-end ML components. An alternative to this would be calling tf. Now that you know about Deep Learning, check out the Deep Learning with TensorFlow Training by Edureka, a trusted online learning company with a network of more than 250,000 satisfied learners spread across the globe. , device placement and tensor shapes) or about the performance of specific batch steps (e. Specifically, we trained a classifier to detect Road or Not Road at more than 400 frames per second on a laptop. Tadpoles on TensorFlow: Hierarchical partial pooling with tfprobability. Moreover, across several experiments, we found that weight pruning is compatible with. You can vote up the examples you like or vote down the ones you don't like. 9+ recommended). If you clone from original tensorflow repo, you should do additional necessary steps: Download the prebuilt model by yourself and put it in build\tensorflow\examples\label_image\data. x: Input data. For CNN models , the computational graph can be quite complex. How to compress your model. This was a basic example but we can clearly see how easy and useful it is to build machine learning models straight in our browser itself. Caffe, DistBelief, CNTK) versus programmatic generation (e. Credit: The classifier example has been taken from Google TensorFlow example. I did a lot of dumb things, so please don't judge. TensorFlow Tutorial with popular machine learning algorithms implementation. The more complex a model, the harder it is to inspect and the more it tends to work like a black box. TensorFlow Lite image classification Android example application Overview. Simple model that learns W and b by minimizing mean squared errors via gradient descent. TensorFlow provides multiple APIs. This embedding matrix will contain vectors for every distinct word in the training corpus. … - Selection from Building Machine Learning Pipelines [Book]. Caffe, DistBelief, CNTK) versus programmatic generation (e. But to be honest, I found it quite cumbersome (e. Example using TensorFlow Estimator, Experiment & Dataset on MNIST data. Explore libraries to build advanced models or methods using TensorFlow, and access domain-specific application packages that extend TensorFlow. height: `float`. Tensorflow playground is a neural network playground. pb file) to a TensorFlow Lite file (a. This post’s ambition is to provide an example of how to use Tensorflow to build a sate-of-the art model (similar to this paper) for sequence tagging and share some exciting NLP knowledge! Together with this post, I am releasing the code and hope some will find it useful. On top of that, TensorFlow is equipped with a vast array of APIs to perform many machine learning algorithms. Then we will export these. TensorFlow is a fast, flexible, and scalable open-source machine learning library for research and production. For example,. pb can be deployed in, for example, Object Recognition with the Computer Vision Library Tensorflow. Is there any example code for deploying a Tensorflow Model via a RESTful API? I see examples for a command line program and for a mobile app.