Keras and TensorFlow can be configured to run on either CPUs or GPUs. Let's create an Android app that uses a pre-trained Tensorflow image classifier for MNIST digits to recognize what the user draws on the screen. The steps needed to take in order to install Tensorflow GPU on Windows OS are as follows: This is going to be a tutorial on how to install tensorflow GPU on Windows OS. Neither library is officially available via a conda package (yet) so we'll need to install them with pip. It's nice right? Now that you're feeling inspired, check out what this appendix convers: Installing TensorFlow using Docker; Installing Matplotlib. To briefly illustrate how TensorFlow works we’ll be reimplementing the model from our last tutorial on Multiple Linear Regression. Make sure that you run the cmd as an administrator. This machine learning platform is quite basic for developers, students and researchers for learning deep applications like neural networks. To enable this platform in your installation, add the following to your configuration. But I’m not using the common feed_dict or queue methods – the world is full of posts about them. The categories for the classification were: Shirts, T-shirts, Jackets, Jeans, Trousers, Sunglasses, Shoes, Tops, Skirts. Inferencing was carried out with the MobileNet v2 SSD and MobileNet v1 0. Introduction to RNNs. If you need Tensorflow GPU, you should have a dedicated Graphics card on your Ubuntu 18. 0 to support TensorFlow 1. Installing TensorFlow on Debian 9. Then extract weights from tensorflow and assign them manually to each layer in pytorch. Below I will show you step by step instruction how to run TensorFlow model as a simple C++ application on Windows 10 x64 and NVIDIA GTX 1050 graphics card. Keras is a Deep Learning Library which has been quite popular these days. 5 afterwards, run. Conclusion and Further reading. Read on if you want to learn about additional installation options, including installing a version of TensorFlow that takes advantage of Nvidia GPUs if you have the correct CUDA libraries installed. Most neural network libraries are written in C++ for performance but have. 0 installation has become much easier to perform. How to Install TensorFlow on Windows: 7 Steps Downloading your Python So to get started, here’s how you can download the latest 64-bit Python 3. In this one, I’m gonna show you how to create a model that was used in that example. All the credits go to this article, I just updated it as I was not able to follow that myself for current changes. So, before we install TensorFlow, let’s have a look at some of the applications of it. Keras is a high-level neural networks API developed with a focus on enabling fast experimentation. It has numerous optimization techniques to make mathematical expressions’ complexity easier and more performant. py install This completes the installation of the object detection api. But it can be. Now TensorFlow has helped a lot of companies built world-class models to solve real problems. Tensorflow model server can be installed via APT [2]. We will be using Ubuntu Server 16. TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. Installation. 7 installed. In my previous article about TensorFlow I've shown you, how to write an app on Android using TensorFlow API. This course starts off by focusing on how you can save the model parameters of a trained model using the Saved Model interface, a universal interface for TensorFlow models. We do that. Airbnb: It improves the guest experience by using TensorFlow to classify images and detect objects at scale. Models, in Tensorflow's world, can simultaneously be independent entities and checkpoints, meaning that after training a model for a long while, you can either pack up and call it a day and use that model in the wild, or you can stop for a bit and resume training later. It allows you to create large-scale neural networks with many layers. The device has a camera or can acquire images elsewhere and can process them on the edge before you send to your data lake. 5 and verify the install using simple and small Tensorflow-Python program. Reading Time: 2 minutes. 1; win-64 v1. Using your tensorflow model with go Posted on July 3, 2017 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. layers import Dense, GlobalAveragePooling2D, Dropout from keras. Step 1: Verify the python version being installed. 1; osx-64 v1. TensorFlow; cuDNN; CNTK; Here we use Tensorflow. 2 Mb (MobileNet v2) Model 5 : 6 Mb (facenet) I was trying to load all these models in separate tensorflow session and that is where it fails to load or it will take usually. Learning the use of this library is also a fundamental part of the AI & Deep Learning course curriculum. While TensorFlow models are typically defined and trained using R or Python code, it is possible to deploy TensorFlow models in a wide variety of environments without any runtime dependency on R or Python: TensorFlow Serving is an open-source software library for serving TensorFlow models using a gRPC interface. Our mission is to help you master programming in Tensorflow step by step, with simple tutorials, and from A to Z. Compared with model training, the model inference is less computational intensive. #An example of building a TensorFlow model from R using rPython ### # For this script you need to # 1. Network: A Modeler must own a network, which implements the feed-forward computation. Building on the assumption that you have just created your new virtual Downloading the TensorFlow Models ¶. py build python setup. TensorFlow is Google Brain's second-generation system. Now we need to download the Tensorflow model repository which contains all the libraries and other codes necessary for the training. compile() step. txt or any reasonable name. Then the cloud functions will be invoked through an API request to download the model and a test image from Cloud Storage. 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. Then we will use the Object detection API as an example of object recognition. let's start from a folder containing a model, it probably looks something like this:. Q&A for Work. We are ready to use TensorFlow. dmg file or run brew cask install netron. import random import cv2 from keras. conda install linux-64 v1. TensorFlow has two mobile libraries, TensorFlow Mobile and TensorFlow Lite. Installing Keras with TensorFlow backend The first part of this blog post provides a short discussion of Keras backends and why we should (or should not) care which one we are using. Erik, thanks for these notes and the AMI, I wanted to play around with GPU instances on AWS so this was very useful! WRT the AMI, actually I ended up re-running the bazel installation and re-fetching and building the latest tensorflow (I wanted to run the convolutional. This post demonstrates the steps to install and use TensorFlow on AMD GPUs. You can find several TensorFlow compatible models included at the end of the documentation. Python version 3. Airbnb: It improves the guest experience by using TensorFlow to classify images and detect objects at scale. In a previous post, we showed how to use tfprobability - the R interface to TensorFlow Probability - to build a multilevel, or partial pooling model of tadpole survival in differently sized (and thus, differing in inhabitant number) tanks. i am using ubuntu 16. Install TensorFlow on the Raspberry Pi. 1; To install this package with conda run one of the following: conda install -c conda-forge tensorflow. This codelab uses TensorFlow Lite to run an image recognition model on an Android device. You can get started on AWS with a fully-managed TensorFlow experience with Amazon SageMaker, a platform to build, train, and deploy machine learning models at scale. TensorFlow is an open source software library created by Google that is used to implement machine learning and deep learning systems. There's no need to configure anything on AWS or install TensorFlow or deal with GPU drivers or anything like that. keras module) with TensorFlow-specific enhancements. This latest news makes installing TensorFlow 1. Publisher: Infinite Skills. This post demonstrates the steps to install and use TensorFlow on AMD GPUs. download()” Let’s take a quick look at what those commands do. Part 1: Install Anaconda on Windows. For example, Figure 1 compares the performance of training and inference on two different image classification models using TensorFlow installed using conda verses the same version installed using pip. js! At first, I thought Tensorflow. However, it was taking too long to load the 85 MB model into memory, therefore I needed to load the classifier graph to memory. One feedback , can we include rpm provided by you in our library and details about it in standard documentation? Thanks!. When working with neural networks, I usually create a root folder for the problem, such as C:\Keras\Iris and then a subdirectory named Data to hold the data. In this tutorial, we shall learn how to freeze a trained Tensorflow Model and serve it on a webserver. Tensorflow in R (RStudio) To execute tensorflow in R (RStudio) you need to install tensorflow and keras as shown below. 0-beta1 Download a package. This article is an excerpt taken from the book Mastering TensorFlow 1. js (deeplearn. Introduction to RNNs. If you would like to get in touch with me, feel free to mail me at teavanist [at] gmail [dot] com ; Medium is not very conducive to conversations. In this tutorial, you'll build a deep learning model that will predict the probability of an employee leaving a company. In this tutorial, we're going to continue on that to exemplify how. Fortunately, someone has created an easy to use Python script that does just that! Head on over to mrry's GitHub account and download the tensorflow_self_check. For this purpose, Google has released it's Object Detection API which makes it easy to construct, train and deploy object detection models. You can do this for any network you have trained but we shall use the trained model for dog/cat classification in this earlier tutorial and serve it on a python Flask webserver. py install Upgrade Tensorflow. It has numerous optimization techniques to make mathematical expressions’ complexity easier and more performant. We will be assuming a fresh Ubuntu 16. TensorFlow makes it possible to turn messy, chaotic sensor data from cameras and microphones into useful information, so running models on the Pi has enabled some fascinating applications, from predicting train times, sorting…. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other. Installing TensorFlow on Linux. Now TensorFlow has helped a lot of companies built world-class models to solve real problems. It allows you to create large-scale neural networks with many layers. We can easily access Tensorflow in Python to create Deep Learning models. You will need TensorFlow and Bazel as prerequisites for training the model. 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. 1 on your local machine and Jupyter Notebook. After he shows how to get TensorFlow up and running, instructor Adam Geitgey demonstrates how to. If you need Tensorflow GPU, you should have a dedicated Graphics card on your Ubuntu 18. Step 1 : Install Prerequisites. datasets import cifar10 from keras. Edit 2017/03/07: Updated to work with Tensorflow 1. Installing TensorFlow with native pip If Steps 1 to 6 have been completed, install TensorFlow by invoking one of the following commands, for Python 2. Tensorflow is an open source software library used for computation of data flow graphs, with applications in deep neural networks research and machine learning. TensorFlow: Getting Started. What is the next step in installing tensorflow now, since the virtualization step isn't mentioned for Pi in this guide? Create / activate a new environment, making sure it uses python 3 Once activated you can install tensorflow with. In this one, I'm gonna show you how to create a model that was used in that example. But I’m not using the common feed_dict or queue methods – the world is full of posts about them. You can find models for a sorts of different applications on Kaggle Kernels. In this tutorial, we shall learn how to freeze a trained Tensorflow Model and serve it on a webserver. If you are compiling a model from the TensorFlow Model Zoo for use with the NCSDK and Neural Compute API, follow the Guidance for Compiling TensorFlow™ Model Zoo Networks. This tutorial shows how to build an image recognition service in Go using pre-trained TensorFlow Inception-V3 model. Linear Regression in TensorFlow. For more complex architectures, you should use the Keras functional API , which allows to build arbitrary graphs of layers. It allows users to evaluate their models on large amounts of data in a distributed manner, using the same metrics defined in their trainer. What you'll Learn. conda activate deepspeech-venv and follow the installation-process described here: GitHub mozilla/DeepSpeech. In this tutorial, get tips on how to bring existing TensorFlow ® Keras models into MATLAB ® using the Neural Network Toolbox™ Importer for TensorFlow Keras Models. 1 Download and install CUDA 9. Download Models. The steps on how to reproduce it are pretty brutal and I wouldn’t recommend going through it unless you want to waste five hours of your live. Instead it can be deployed to Kubernetes, for example on the IBM Cloud which provides a free plan that can be used to run this sample. Check the tensorflow documentation but currently the best supported combination is CUDA v7. Finally, Testing of your Installation. Installing TensorFlow. * Installation. You can find those models in Tensorflow detection model zoo, the "Speed (ms)" metric will give you a guideline on the complexity of the model. Install Visual Studio Code from here. TensorFlow ships with a few demo models. TensorFlow can be used anywhere from training huge models across clusters in the cloud, to running models locally on an embedded system like your phone. Then, install the Deployment Toolkit. These packages are available via the Anaconda Repository, and installing them is as easy as running "conda install tensorflow" or "conda install tensorflow-gpu" from a command line interface. In this course, learn how to install TensorFlow and use it to build a simple deep learning model. Install either Python 2. py install This completes the installation of the object detection api. After hours of failed attempts I have come here. The following sections provide a step by step instructions about how to install TensorFlow in a Python virtual environment on Debian 9. Depending on your device you might need to change the installation a little. Let's create an Android app that uses a pre-trained Tensorflow image classifier for MNIST digits to recognize what the user draws on the screen. 04 machine with one or more NVIDIA GPUs. We can download the model Mobile_net V1 from this link. One more thing: this step installs TensorFlow with CPU support only; if you want GPU support too, check this out. After he shows how to get TensorFlow up and running, instructor Adam Geitgey demonstrates how to. A Newbie's Install of Keras & Tensorflow on Windows 10 with R (tensorflow) install_tensorflow(conda="tf=keras") Here is the code I used to prepare the data. Once you complete the installation, you'll validate your installation by running a short TensorFlow program and then use TensorFlow to perform image recognition. To briefly illustrate how TensorFlow works we’ll be reimplementing the model from our last tutorial on Multiple Linear Regression. Installation or Setup As of Tensorflow version 1. TensorFlow can be used anywhere from training huge models across clusters in the cloud, to running models locally on an embedded system like your phone. #An example of building a TensorFlow model from R using rPython ### # For this script you need to # 1. Keras is a neural network API that is written in Python. TensorFlow 1. Introduction to Deep Learning Models With TensorFlow Learn How to Work With TensorFlow to Create and Run a TensorFlow Graph, and Build a Deep Learning Model. And almost over a year ago Google announces Tensorflow. Get an introduction to GPUs, learn about GPUs in machine learning, learn the benefits of utilizing the GPU, and learn how to train TensorFlow models using GPUs. Read on if you want to learn about additional installation options, including installing a version of TensorFlow that takes advantage of Nvidia GPUs if you have the correct CUDA libraries installed. Example objects. TensorFlow: Getting Started. Because we will use Docker to run Tensorflow image, the only modification we have to make to our machines is to update the NVIDIA driver. Vision of this tutorial: to create TensorFlow object detection model, that could detect CS:GO players. We are ready to use TensorFlow. How to use TensorFlow with AMD GPU's Most machine learning frameworks that run with a GPU support Nvidia GPUs, but if you own a AMD GPU you are out of luck. 1; win-64 v1. While the official TensorFlow documentation does have the basic information you need, it may not entirely make sense right away, and it can be a little hard to sift through. If you were running TensorFlow on only one node you would now not be able to continue with our model training until you resolve this experiment. Install Tensorflow 2. It's possible to create neural networks from raw code. In this course, learn how to install TensorFlow and use it to build a simple deep learning model. In this tutorial, we shall learn how to freeze a trained Tensorflow Model and serve it on a webserver. 2 MB (Landmarks) Model 3,4: 9. Below I will show you step by step instruction how to run TensorFlow model as a simple C++ application on Windows 10 x64 and NVIDIA GTX 1050 graphics card. Installing TensorFlow on AWS 2015-11-12. This kind of setup can be a choice when we are not using TensorFlow to build a new AI model but instead only for obtaining the prediction (inference) served by a trained AI model. You can find several TensorFlow compatible models included at the end of the documentation. FakeApp uses TensorFlow, a Machine Learning framework which supports GPU-accelerated computation using NVIDIA graphics cards. It can be run on your local machine and conveyed to a cluster if the TensorFlow versions are the same or later. 0 installation has become much easier to perform. TensorFlow was initially built for only internal use at Google, but it was subsequently released in November 2015 under a permissive open source license. And after that process to Run your model step. How to develop a stock price predictive model using Reinforcement Learning and TensorFlow. You are now ready to take advantage of CPU-optimized TensorFlow for your project. I'd recommend to install the CPU version if you need to design and train simple machine learning models, or if you're just starting out. It takes both the test data and labels as input and gives a quantitative measure of the accuracy. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other. In this blog post, I’ll show you how to convert the Places 365 model to TensorFlow. In the installation of TensorFlow with native pip we need to follow these steps. Install TensorFlow on the Raspberry Pi. TensorFlow 2. Installing Python 3 and venv. x if you have an older version or if you simply don’t have it. These are the instructions for an apt-get install:. The TFLearn offers two different classes of the models: DNN (Deep Neural Network) model: This class allows you to create a multilayer perceptron from the network that you have created from the layers SequenceGenerator model: This class allows you to create a deep neural network that can generate sequences. The models used were the inbuilt TensorFlow models for object detection customized for the classification of our data. We strongly suggest against trying to compile and run on your native computer OS - that way we don't get. js (deeplearn. Installing Python 3 and venv. You can find those models in Tensorflow detection model zoo, the "Speed (ms)" metric will give you a guideline on the complexity of the model. TensorFlow is Google Brain's second-generation system. 2 MB (Landmarks) Model 3,4: 9. You are now ready to take advantage of CPU-optimized TensorFlow for your project. onnx file using the torch. Tensorflow 1. The Caffe Model Zoo is an extraordinary place where reasearcher share their models. The CPU version is much easier to install and configure so is the best starting place especially when you are first learning how to use Keras. In this tutorial, get tips on how to bring existing TensorFlow ® Keras models into MATLAB ® using the Neural Network Toolbox™ Importer for TensorFlow Keras Models. Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately. Python version 3. 04: Install TensorFlow and Keras for Deep Learning On January 7th, 2019, I released version 2. This data flows through the computational graph through tensors, impacted by operations - hence the name TensorFlow. TensorFlow Tutorials and Deep Learning Experiences in TF. I personally did pip install tensorflow-gpu on my Machine's anaconda installation to avoid messing up things. 1 (stable) Install TensorFlow 1. onnx file using the torch. Tensorflow to tensorflow lite. Step 1: To install TensorFlow, start a terminal. First, you'll be introduced to the installation process, building simple and advanced models, and utilizing additional libraries that make development even easier. Training pix2pix. Keras is a neural network API that is written in Python. Great help, was able to install TensorFlow Model. For a simple example on MNIST, read the official tutorial, but keep in mind that some of the techniques are not recommended for big projects (they use placeholders instead of the new tf. Thinking about training your custom object detection model with a free data center GPU, check out my previous tutorial - How to train an object detection model easy for free. This part shows you how to install the TensorFlow model code on a development system and run the model on the MovieLens dataset. In this course, learn how to install TensorFlow and use it to build a simple deep learning model. After he shows how to get TensorFlow up and running, instructor Adam Geitgey demonstrates how to. 1; Red Hat 6. You can get started on AWS with a fully-managed TensorFlow experience with Amazon SageMaker, a platform to build, train, and deploy machine learning models at scale. To start off, we would need to install PyTorch, TensorFlow, ONNX, and ONNX-TF (the package to convert ONNX models to TensorFlow). 1 and cuDNN 7. The speed up in model training is really. 0 can be found here, while the latest version is available here. Besides training, this blog post has also shown how to add TensorFlow into your iOS apps. Inferencing was carried out with the MobileNet v2 SSD and MobileNet v1 0. start('[FILE]'). We do that. See also- Mandelbrot Set Compute Quickly Using TensorFlow For reference. Now we can try it out by going into the object detection directory and typing jupyter notebook to open jupyter. I had to use Keras library for Recurrent Neural Networks and found that I need to install Tensorflow to use Keras. Justin is also on the software team for the university's engineering club 'Autonomous Robotic Vehicle Project' (arvp. Once the TensorFlow is installed, it is time to select the dataset we want to use to train our model. The Jetson TX2 ships with TensorRT, which is the run time for TensorFlow. Operating System. I'd recommend to install the CPU version if you need to design and train simple machine learning models, or if you're just starting out. datasets import cifar10 from keras. Reading Time: 2 minutes. When you wrap up this course, you'll be ready to start building and deploying your own models with Keras. 7 by default, and the sample for this tutorial uses Python 2. It's nice right? Now that you're feeling inspired, check out what this appendix convers: Installing TensorFlow using Docker; Installing Matplotlib. Object detection model installation and configuration step by step. In this course, learn how to install TensorFlow and use it to build a simple deep learning model. 12 gives warning 'tf. 1; win-64 v1. How do you install Tensorflow on a Raspberry Pi?. The core data structure of Keras is a model, a way to organize layers. By default, Debian 9 ships with Python 3. 6 by default. We'll navigate to the directory where they're located and run a simple model for classifying handwritten digits from the MNIST dataset:. 04 installation. Keras has a model visualization function, that can plot out the structure of a model. The above steps illustrates how to install tensorflow for Nvidia GPU environment. 5 and cuDNN v5. We're going to write a function to classify a piece of fruit Image. 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. Installing TensorFlow on Debian 9. We need to somehow capture audio data from a microphone. pip uninstall-yq tensorflow pip uninstall-yq tf-nightly pip install-Uq tf-nightly-gpu pip install-q tensorflow-model-optimization Then you can load your previous trained model and make it "prunable". 7 pip3 install --upgrade tensorflow # for Python 3. The core data structure of Keras is a model, a way to organize layers. SSH to server. Concepts and Models. The Tensorflow Object Detection API uses. Now, there is an option if you don't want to install TensorFlow at all. Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. js (deeplearn. Object detection model installation and configuration step by step. TensorFlow is an open source machine learning library, invented by Google, and used to design, construct, and train deep learning models. TensorFlow is an open source software library created by Google that is used to implement machine learning and deep learning systems. Invoke the command to install TensorFlow. Install Visual Studio Code from here. Back in The MagPi issue 71 we noted that it was getting easier to install TensorFlow on a Raspberry Pi. Next, run the following command to install TensorFlow: $ conda install tensorflow A list of packages to be installed alongside TensorFlow will be shown. If using virtualenv in Linux, you could run the command below (replace tensorflow with tensorflow-gpu if you have NVidia CUDA installed). Download Models. Now we have some trained models, it’s time to add some code to interact with them. Learning the use of this library is also a fundamental part of the AI & Deep Learning course curriculum. TensorFlow is a Python library for fast numerical computing created and released by Google. Install Google's TensorFlow library as per these instructions:. It has numerous optimization techniques to make mathematical expressions’ complexity easier and more performant. I'd recommend to install the CPU version if you need to design and train simple machine learning models, or if you're just starting out. Finally, install the package using the following command: pip install -e. This post walks through the steps required to train an object detection model locally. 1; osx-64 v1. It contains several models that are maintained by the respective authors. The downloaded zip file contains a model. TensorFlow Lite is TensorFlow's solution to lightweight models for mobile and embedded devices. In the installation of TensorFlow with native pip we need to follow these steps. Just install and run: conda create -n deepspeech-venv python=3. But Tensorflow is flexible and it can be used at scale, or not. py install This completes the installation of the object detection api. I installed the main binary first, then followed by both batches. Note: Precompiled packages are currently only provided for Linux (Python 2. 1 of my deep learning book to existing customers (free upgrade as always) and new customers. We need to be able to run a specific version/commit of TensorFlow and the dependancy requirements for TF are very extreme. The TensorFlow Object Detection API built on top of TensorFlow that makes it easy to construct, train and deploy object detection models. 2019-05-20 update: I just added the Running TensorRT Optimized GoogLeNet on Jetson Nano post. TensorFlow can be a bit tricky, so we're going to have to test the installation to see if everything went fine. To start, create a new EC2 instance in the AWS control panel. Get started quickly by running Colab notebooks directly in your browser. Now TensorFlow has helped a lot of companies built world-class models to solve real problems. However, its greatest performance capabilities can be discovered when using GPUs. Converting a Caffe model to TensorFlow Wed, Jun 7, 2017 Converting a Caffe model to TensorFlow. This kind of setup can be a choice when we are not using TensorFlow to build a new AI model but instead only for obtaining the prediction (inference) served by a trained AI model. evaluate() – It finds the loss and metrics specified in the model. In this blog, we give a quick hands on tutorial on how to train the ResNet model in TensorFlow. If you wish to avoid running this manually, you can add it as a new line to the end of your ~/. ML Kit is part of the Firebase ecosystem, and it contains a set of machine learning model APIs that offer out-of-the-box models for face detection, barcode scanning, text recognition, image labeling, smart reply, and language identification. Linear Regression in TensorFlow. We'll navigate to the directory where they're located and run a simple model for classifying handwritten digits from the MNIST dataset:. How to develop a stock price predictive model using Reinforcement Learning and TensorFlow. # From within TensorFlow/models/research/ python setup. You're not going to find any tricks or hacks here. Our ML model is just a simple linear regression that takes a 1-dimensional value as its input and attempts to fit a straight line to the dataset. But Tensorflow is flexible and it can be used at scale, or not. Although the Mobile_net is fast and small, it's accuracy isn’t high as other models like Inception, Resnet. Install TensorFlow on the Raspberry Pi. TensorFlow Model Analysis. (except blockchain processing). Let's create an Android app that uses a pre-trained Tensorflow image classifier for MNIST digits to recognize what the user draws on the screen. Installing the necessary packages.