what is assign tensorflow

Defined in tensorflow/python/ops/state_ops.py .

See the guide: Variables > Variable helper functions

Update 'ref' by assigning 'value' to it.

This operation outputs a Tensor that holds the new value of 'ref' after the value has been assigned. This makes it easier to chain operations that need to use the reset value.

• ref : A mutable Tensor . Should be from a Variable node. May be uninitialized.
• value : A Tensor . Must have the same type as ref . The value to be assigned to the variable.
• validate_shape : An optional bool . Defaults to True . If true, the operation will validate that the shape of 'value' matches the shape of the Tensor being assigned to. If false, 'ref' will take on the shape of 'value'.
• use_locking : An optional bool . Defaults to True . If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
• name : A name for the operation (optional).

A Tensor that will hold the new value of 'ref' after the assignment has completed.

© 2018 The TensorFlow Authors. All rights reserved. Licensed under the Creative Commons Attribution License 3.0. Code samples licensed under the Apache 2.0 License. https://www.tensorflow.org/api_docs/python/tf/assign

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TensorFlow basics

This guide provides a quick overview of TensorFlow basics . Each section of this doc is an overview of a larger topic—you can find links to full guides at the end of each section.

TensorFlow is an end-to-end platform for machine learning. It supports the following:

• Multidimensional-array based numeric computation (similar to NumPy .)
• GPU and distributed processing

Automatic differentiation

• Model construction, training, and export

TensorFlow operates on multidimensional arrays or tensors represented as tf.Tensor objects. Here is a two-dimensional tensor:

The most important attributes of a tf.Tensor are its shape and dtype :

• Tensor.shape : tells you the size of the tensor along each of its axes.
• Tensor.dtype : tells you the type of all the elements in the tensor.

TensorFlow implements standard mathematical operations on tensors, as well as many operations specialized for machine learning.

For example:

Running large calculations on CPU can be slow. When properly configured, TensorFlow can use accelerator hardware like GPUs to execute operations very quickly.

Refer to the Tensor guide for details.

Normal tf.Tensor objects are immutable. To store model weights (or other mutable state) in TensorFlow use a tf.Variable .

Refer to the Variables guide for details.

Gradient descent and related algorithms are a cornerstone of modern machine learning.

To enable this, TensorFlow implements automatic differentiation (autodiff), which uses calculus to compute gradients. Typically you'll use this to calculate the gradient of a model's error or loss with respect to its weights.

At x = 1.0 , y = f(x) = (1**2 + 2*1 - 5) = -2 .

The derivative of y is y' = f'(x) = (2*x + 2) = 4 . TensorFlow can calculate this automatically:

This simplified example only takes the derivative with respect to a single scalar ( x ), but TensorFlow can compute the gradient with respect to any number of non-scalar tensors simultaneously.

Refer to the Autodiff guide for details.

Graphs and tf.function

While you can use TensorFlow interactively like any Python library, TensorFlow also provides tools for:

• Performance optimization : to speed up training and inference.
• Export : so you can save your model when it's done training.

These require that you use tf.function to separate your pure-TensorFlow code from Python.

The first time you run the tf.function , although it executes in Python, it captures a complete, optimized graph representing the TensorFlow computations done within the function.

On subsequent calls TensorFlow only executes the optimized graph, skipping any non-TensorFlow steps. Below, note that my_func doesn't print tracing since print is a Python function, not a TensorFlow function.

A graph may not be reusable for inputs with a different signature ( shape and dtype ), so a new graph is generated instead:

These captured graphs provide two benefits:

• In many cases they provide a significant speedup in execution (though not this trivial example).
• You can export these graphs, using tf.saved_model , to run on other systems like a server or a mobile device , no Python installation required.

Refer to Intro to graphs for more details.

Modules, layers, and models

tf.Module is a class for managing your tf.Variable objects, and the tf.function objects that operate on them. The tf.Module class is necessary to support two significant features:

• You can save and restore the values of your variables using tf.train.Checkpoint . This is useful during training as it is quick to save and restore a model's state.
• You can import and export the tf.Variable values and the tf.function graphs using tf.saved_model . This allows you to run your model independently of the Python program that created it.

Here is a complete example exporting a simple tf.Module object:

Save the Module :

The resulting SavedModel is independent of the code that created it. You can load a SavedModel from Python, other language bindings, or TensorFlow Serving . You can also convert it to run with TensorFlow Lite or TensorFlow JS .

The tf.keras.layers.Layer and tf.keras.Model classes build on tf.Module providing additional functionality and convenience methods for building, training, and saving models. Some of these are demonstrated in the next section.

Refer to Intro to modules for details.

Training loops

Now put this all together to build a basic model and train it from scratch.

First, create some example data. This generates a cloud of points that loosely follows a quadratic curve:

Create a quadratic model with randomly initialized weights and a bias:

First, observe your model's performance before training:

Now, define a loss for your model:

Given that this model is intended to predict continuous values, the mean squared error (MSE) is a good choice for the loss function. Given a vector of predictions, \(\hat{y}\), and a vector of true targets, \(y\), the MSE is defined as the mean of the squared differences between the predicted values and the ground truth.

\(MSE = \frac{1}{m}\sum_{i=1}^{m}(\hat{y}_i -y_i)^2\)

Write a basic training loop for the model. The loop will make use of the MSE loss function and its gradients with respect to the input in order to iteratively update the model's parameters. Using mini-batches for training provides both memory efficiency and faster convergence. The tf.data.Dataset API has useful functions for batching and shuffling.

Now, observe your model's performance after training:

That's working, but remember that implementations of common training utilities are available in the tf.keras module. So, consider using those before writing your own. To start with, the Model.compile and Model.fit methods implement a training loop for you:

Begin by creating a Sequential Model in Keras using tf.keras.Sequential . One of the simplest Keras layers is the dense layer, which can be instantiated with tf.keras.layers.Dense . The dense layer is able to learn multidimensional linear relationships of the form \(\mathrm{Y} = \mathrm{W}\mathrm{X} + \vec{b}\). In order to learn a nonlinear equation of the form, \(w_1x^2 + w_2x + b\), the dense layer's input should be a data matrix with \(x^2\) and \(x\) as features. The lambda layer, tf.keras.layers.Lambda , can be used to perform this stacking transformation.

Observe your Keras model's performance after training:

Refer to Basic training loops and the Keras guide for more details.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2023-08-19 UTC.

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This article is a brief introduction to TensorFlow library using Python programming language.

Introduction

TensorFlow is an open-source software library. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well! Let us first try to understand what the word TensorFlow actually mean! TensorFlow is basically a software library for numerical computation using data flow graphs where:

• nodes in the graph represent mathematical operations.
• edges in the graph represent the multidimensional data arrays (called tensors ) communicated between them. (Please note that tensor is the central unit of data in TensorFlow).

TensorFlow APIs

TensorFlow provides multiple APIs (Application Programming Interfaces). These can be classified into 2 major categories:

• complete programming control
• recommended for machine learning researchers
• provides fine levels of control over the models
• TensorFlow Core is the low level API of TensorFlow.
• built on top of TensorFlow Core
• easier to learn and use than TensorFlow Core
• make repetitive tasks easier and more consistent between different users
• tf.contrib.learn is an example of a high level API.

In this article, we first discuss the basics of TensorFlow Core and then explore the higher level API, tf.contrib.learn .

TensorFlow Core

1. Installing TensorFlow

An easy to follow guide for TensorFlow installation is available here: Installing TensorFlow . Once installed, you can ensure a successful installation by running this command in python interpreter:

2. The Computational Graph

Any TensorFlow Core program can be divided into two discrete sections:

• Building the computational graph.A computational graph is nothing but a series of TensorFlow operations arranged into a graph of nodes.
• Running the computational graph.To actually evaluate the nodes, we must run the computational graph within a session . A session encapsulates the control and state of the TensorFlow runtime.

Now, let us write our very first TensorFlow program to understand above concept: 

Let us try to understand above code:

• In above program, the nodes node1 and node2 are of tf.constant type. A constant node takes no inputs, and it outputs a value it stores internally. Note that we can also specify the data type of output tensor using dtype argument.
• node3 is of tf.add type. It takes two tensors as input and returns their sum as output tensor.
• Step 2 : Run the computational graph In order to run the computational graph, we need to create a session . To create a session, we simply do:
• Now, we can invoke the run method of session object to perform computations on any node:
• Here, node3 gets evaluated which further invokes node1 and node2 . Finally, we close the session using:

Note: Another(and better) method of working with sessions is to use with block like this:

The benefit of this approach is that you do not need to close the session explicitly as it gets automatically closed once control goes out of the scope of with block.

3. Variables

TensorFlow has Variable nodes too which can hold variable data. They are mainly used to hold and update parameters of a training model. Variables are in-memory buffers containing tensors. They must be explicitly initialized and can be saved to disk during and after training. You can later restore saved values to exercise or analyze the model. An important difference to note between a constant and Variable is:

A constant’s value is stored in the graph and its value is replicated wherever the graph is loaded. A variable is stored separately, and may live on a parameter server.

Given below is an example using Variable : 

In above program:

• We define a node of type Variable and assign it some initial value.
• To initialize the variable node in current session’s scope, we do:
• To assign a new value to a variable node, we can use assign method like this:

4. Placeholders

A graph can be parameterized to accept external inputs, known as placeholders . A placeholder is a promise to provide a value later. While evaluating the graph involving placeholder nodes, a feed_dict parameter is passed to the session’s run method to specify Tensors that provide concrete values to these placeholders. Consider the example given below: 

Let us try to understand above program:

• We define placeholder nodes a and b like this:
• The first argument is the data type of the tensor and one of the optional argument is shape of the tensor.
• We define another node c which does the operation of matrix multiplication ( matmul ). We pass the two placeholder nodes as argument.
• Finally, when we run the session, we pass the value of placeholder nodes in feed_dict argument of sess.run :
• Consider the diagrams shown below to clear the concept:

5. An example : Linear Regression model

Given below is an implementation of a Linear Regression model using TensorFlow Core API. 

Let us try to understand the above code.

• First of all, we define some parameters for training our model, like:
• Then we define placeholder nodes for feature and target vector.
• Then, we define variable nodes for weight and bias.
• linear_model is an operational node which calculates the hypothesis for the linear regression model.
• Loss (or cost) per gradient descent is calculated as the mean squared error and its node is defined as:
• Finally, we have the optimizer node which implements the Gradient Descent Algorithm.
• Now, the training data is fit into the linear model by applying the Gradient Descent Algorithm. The task is repeated training_epochs number of times. In each epoch, we perform the gradient descent step like this:
• After every display_step number of epochs, we print the value of current loss which is found using:
• The model is evaluated on test data and testing_cost is calculated using:

tf.contrib.learn

tf.contrib.learn is a high-level TensorFlow library that simplifies the mechanics of machine learning, including the following:

• running training loops
• running evaluation loops
• managing data sets
• managing feeding

Let us try to see the implementation of Linear regression on same data we used above using tf.contrib.learn . 

• The shape and type of feature matrix is declared using a list. Each element of the list defines the structure of a column. In above example, we have only 1 feature which stores real values and has been given a name X .
• Then, we need an estimator. An estimator is nothing but a pre-defined model with many useful methods and parameters. In above example, we use a Linear Regression model estimator.
• For training purpose, we need to use an input function which is responsible for feeding data to estimator while training. It takes the feature column values as dictionary. Many other parameters like batch size, number of epochs, etc can be specified.
• To fit training data to estimator, we simply use fit method of estimator in which input function is passed as an argument.
• Once training is complete, we can get the value of different variables using get_variable_value method of estimator. You can get a list of all variables using get_variable_names method.
• The mean squared error/loss can be computed as:

This brings us to the end of this Introduction to TensorFlow article! From here, you can try to explore this tutorial: MNIST For ML Beginners . If you like GeeksforGeeks and would like to contribute, you can also write an article using write.geeksforgeeks.org or mail your article to [email protected]. See your article appearing on the GeeksforGeeks main page and help other Geeks. Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above.

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Computer Science > Distributed, Parallel, and Cluster Computing

Title: support vector machine implementation on mpi-cuda and tensorflow framework.

Abstract: Support Vector Machine (SVM) algorithm requires a high computational cost (both in memory and time) to solve a complex quadratic programming (QP) optimization problem during the training process. Consequently, SVM necessitates high computing hardware capabilities. The central processing unit (CPU) clock frequency cannot be increased due to physical limitations in the miniaturization process. However, the potential of parallel multi-architecture, available in both multi-core CPUs and highly scalable GPUs, emerges as a promising solution to enhance algorithm performance. Therefore, there is an opportunity to reduce the high computational time required by SVM for solving the QP optimization problem. This paper presents a comparative study that implements the SVM algorithm on different parallel architecture frameworks. The experimental results show that SVM MPI-CUDA implementation achieves a speedup over SVM TensorFlow implementation on different datasets. Moreover, SVM TensorFlow implementation provides a cross-platform solution that can be migrated to alternative hardware components, which will reduces the development time.

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AnacondaでGPU対応のTensorFlowをインストールする簡単な方法

本記事は、Anacondaを使用してGPU対応のTensorFlowを導入する際に、お手軽にインストールする方法について解説します。

本記事の前提知識は、以下の2点です。

• GPUドライバの適切なインストールされている（ nvidia-smi が動作している）
• Anacondaがインストールされていてる

なお、筆者の実装環境は Ubuntu 22.04 です。

最初に確認しておくこと

Nvidia-smi が認識できるかチェック.

以下のコマンドでGPUドライバが認識できるかを確認します。

この結果にはGPUのメタ情報とともに Driver Version: XXX と CUDA Version: XXX が表示されます。 CUDA Version は Driver Version がサポートするCUDAの最大バージョンです。 このDriverのバージョン確認は重要で、適切なTensorFlowのバージョンがこれに依存します 。

※この時、 nvidia-smi で、 command not found や Failed to initialize NVML などのエラーが表示される場合は、GPUドライバが正常にインストールされていない可能性があります。この場合はドライバのインストールから始める必要がありますが、本記事ではその詳細は割愛します。

Anacondaが入っているかチェック

以下のコマンドでAnacondaがインストールされているかを確認します。

これによってAnacondaのバージョンが表示されれば、Anacondaが正常に機能しています。もし conda: Command not found. などのエラーが表示された場合は、Anacondaをインストールする必要がありますが、その詳細は本記事では省略します。

GPU対応のTensorFlowの環境作成

このセクションでは、Anacondaを使用して初めてGPU対応のTensorFlowを導入する手順を説明します。

Anaconda仮想環境にTensorFlowをインストール

AnacondaでGPU対応のTensorFlowを導入するには、以下のコマンドを使用します。

これにより、適切なバージョンのTensorFlow、CUDA（GPUで使う）、cuDNN（GPUで使う）、PythonなどのGPUでTensorFlowを使う上で必要なライブラリが一緒に互換性を考慮してインストールされます。

なお、本記事では仮想環境の名前を tf-gpu-env としていますが、環境名は任意に変更可能です。

TensorFlowのような大規模なライブラリを扱う際には、新しい環境（他のライブラリを入れていない環境）を作成し、そこに最初にインストールする ことでエラーが起きにくくなると思います。

Anacondaに慣れている方は、 conda search tensorflow と入力して表示されるバージョンの中から Build Channel が gpu_XXXX となっているものを選択して、適切なTensorFlowのバージョンを指定することもできます。

TensorFlowのGPU認識の確認

インストールが完了したら、GPUが正常に動作しているか確認します。まず、 conda activate tf-gpu-env を使用して仮想環境に入ります。

次に、以下のPythonコードを使用してGPUが動作しているかを確認します。

と出ていればGPUが正常に動作しています。これで終わりです。折りたたみ部分はスキップして大丈夫です。

GPU is available: False のような結果が表示された場合は、CUDAのバージョンとGPUドライバのバージョンの依存関係に問題がある可能性があります。その場合は折りたたみの箇所に進みます。

GPUが使えない( False )が出た場合

Gpuドライバのバージョンとcudaのバージョンの確認.

まず、 tensorflow を入れた環境（本記事の例では tf-gpu-env ）に入った状態で、以下のコードを使用してCUDAのバージョンを確認します。ここで表示されるTensorFlowのバージョンは後の手順で使用します。

次に、 nvidia-smi を実行し、表示される CUDA Version を確認します。この時、 conda list で表示された cudatoolkit のバージョンが、 CUDA Version よりも大きい場合が考えられます。その場合は次の手順に進みます。

※ nvcc --version で表示されるバージョンがCUDAのバージョンとならないので注意してください。詳細は 補足 をご覧ください。

仮想環境を消してtensorflowのバージョンをダウングレード

このとき、 cudatoolkit だけをダウングレードすればいいというわけではありません。 cudatoolkit は tensorflow や他のライブラリと依存関係にあるため、ハマる可能性があります。そのため、一度仮想環境を削除し、適切なバージョンの tensorflow を指定して再インストールします。

仮想環境から deactivate して GPU is available: False と表示された環境を削除します。

仮想環境が削除できたら、以下のコマンドを入力してAnacondaが利用可能なGPU対応のtensorflowを確認します。

このように異なるバージョンの tensorflow が表示されます。例えば、 GPUが使えない(False)が出た場合 で表示されたバージョンが2.12だとすると、それよりも低いバージョンの2.11をインストールする場合は次のようにします。

インストールが完了したら仮想環境に入り、再び TensorFlowのGPU認識の確認 の手順に戻り、GPUが正常に認識されるか確認します。必要に応じて、再帰的にTensorFlowのバージョンをダウングレードしていくことで、基本的にはこれでGPUが使えるようになると思います。

本記事では、Anacondaを使用してGPU対応のTensorFlowを導入する手順について解説しました。

ポイントとしては、以下の2点です。

• GPUドライバの対応したバージョンのtensorflowを使う。
• 仮想環境を作成時（他のライブラリを入れていない環境）にインストールする。

GPUでTensorFlowを動かしてみたい方の一助になれば幸いです。

nvcc --version のCUDAのバージョンとの違いについて

CUDAのバージョンを理解するとき、 nvcc との関係を抑えておく必要があります。 nvcc と cudatoolkit の用語の意味は以下のようになります。

nvcc: NVIDIA CUDA Compilerの略で、CUDAのコンパイラです。CUDAプログラムをコンパイルしてGPU上で実行可能なバイナリに変換します。 nvcc --version は、基本的にシステム全体のCUDAバージョンを示します。 wihch nvcc で確認すると使われている nvcc のパスを確認できます。

cudatoolkit: NVIDIAが提供するCUDAのソフトウェア開発キットです。これには nvcc などのツールやCUDAランタイムライブラリなどが含まれます。通常、ユーザーはこれを直接使うことはありませんが、CUDAアプリケーションをビルドするためには必要です。

Anaconda環境では cudatoolkit が専用に提供されており、これがAnaconda内で使用されるCUDAバージョンとして機能します。したがって、 nvcc --version で表示されるのはシステム全体のCUDAバージョンであり、Anaconda環境内で動作するTensorFlowなどのライブラリは、Anaconda内に導入された cudatoolkit を使用します。 そのため、 nvcc --version はシステム全体のCUDAバージョンを示すことになり、Anaconda環境内で実際に動作するCUDAバージョンとは違う場合があります。

実際にTensorFlowで使われているCUDAバージョンをPythonで確認するには、以下のコードを使います。

tensorflow-gpu でインストールするときは numpy エラーに注意

AnacondaでGPU対応のTensorFlowをインストールするもう一つの方法として、 tensorflow-gpu を使う手がありますが、 numpy のバージョンでハマることがあるので注意が必要です。

以下に、この問題に遭遇した具体的なケースを紹介します。

まず、次のコマンドを使用して tensorflow-gpu をインストールします。この手順でCUDA、cuDNN、PythonなどTensorFlowで必要となるモジュールが同時にインストールされます。

インストールが完了したら、作成した仮想環境に activate して入ります。

Pythonで import tensorflow を実行します。この時、以下のエラーが発生する可能性があります。

このエラーは、 numpy と tensorflow のバージョンの互換性に関するものです。仮想環境で tensorflow-gpu を構築した場合でも、互換性の問題が発生する可能性があるため、注意が必要です。

詳細は下記の記事にまとめておりますので、良かったらそちらをご覧ください。