tensorflow简介

Here you will get TensorFlow introduction.

在这里，您将获得TensorFlow简介。

TensorFlow is a framework which is used for and deep learning applications like neural network. It is used for data flow programming as an open source software library over various range of tasks. It is symbolic  math library used in research and production both.

TensorFlow是一个框架，可用于和深度学习应用(例如神经网络)。 它用作各种任务范围内的开源软件库，用于数据流编程。 它是用于研究和生产的符号数学库。

Tensorflow is used for general purpose computing on graphics processing units that are able to run on multiple CPUs and GPUs unlike the reference implementation that runs on single devices. It is available on Linux, macOS, windows with 64-bit configuration and also on mobile computing platforms like Android and IOS. As the neural network performs operations on multidimensional data arrays that derives from tensorflow and these arrays are called tensors.

Tensorflow用于可在多个CPU和GPU上运行的图形处理单元上进行通用计算，这与在单个设备上运行的参考实现不同。 它可在Linux，macOS，具有64位配置的Windows以及Android和IOS等移动计算平台上使用。 当神经网络对源自张量流的多维数据数组执行操作时，这些数组称为张量。

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Tensorflow has multiple application programmable interfaces. The lowest level application programmable interface is tensorflow core that provides complete programming control. Generally tensorflow core is used by machine learning researchers and those who require control of fine levels over their models. The higher level application programmable interface unit makes task in repetition with easy task and maintain consistent between different users.

Tensorflow具有多个应用程序可编程接口。 最底层的应用程序可编程接口是tensorflow内核，可提供完整的编程控制。 tensorflow core通常由机器学习研究人员以及需要对其模型进行精细控制的人员使用。 更高级别的应用程序可编程接口单元使重复任务变得容易，并且使不同用户之间保持一致。

The higher level APIs are easier to use than tensorflow core and built on top of tensor flow core.

更高级别的API比tensorflow核心更易于使用，并建立在tensor flow核心之上。

张量 (Tensors)

Tensor is the central unit of data in tensorflow and it comprises of primitive values set shaped as an array of multi-dimension. Tensorflow is a framework with generalized tensor of vectors and matrices of higher dimensions. Tensors are represented as an n-dimensional array with n as rank of tensor of base data types. In tensor flow programming, tf.tensor is the main object that can be manipulated and passed around. The tf.tensor is an object that will produce value by partial computation. Firstly tf.tensor graph is built that describes detailed description about each tensor.

张量是张量流中数据的中心单元，它包含原始值集，该原始值集的形状被设置为多维数组。 Tensorflow是一个框架，它具有矢量和矩阵的广义张量。 张量表示为n维数组，其中n为基本数据类型的张量等级。 在张量流编程中，tf.tensor是可以操纵和传递的主要对象。 tf.tensor是一个将通过部分计算产生值的对象。 首先建立tf.tensor图，它描述了每个张量的详细描述。

Tf.tensor has a data type and a shape as its properties. The element of tensor has same known data type and partially known shape i.e. the dimension.

Tf.tensor具有数据类型和形状作为其属性。 张量的元素具有相同的已知数据类型和部分已知的形状(即尺寸)。

GPU在TensorFlow系统上的交互 (Interaction of GPUs on TensorFlow System)

Tensorflow by default maps almost all the GPU memory visible to the process. This process is efficiently used by reducing memory fragmentation of precious GPU memory resources on the devices. Tensorflow gives two configurations on the session to control the growth of memory usage, it only allocate a subset of memory as is needed by the process.

Tensorflow默认情况下映射几乎所有可见的GPU内存。 通过减少设备上宝贵的GPU内存资源的内存碎片，可以有效地使用此过程。 Tensorflow在会话上提供了两种配置来控制内存使用量的增长，它仅根据流程需要分配一部分内存。

The first configuration option is “allow_growth” which allows to allocate only GPU memory based on runtime allocations. In starting it allocates very less amount of memory and thereafter as per the requirement they extent the GPU memory needed by tensorflow process. However it does not release memory automatically and this scenario leads to memory fragmentation. To release memory automatically we have to turn on this option by setting the option in configProto by:-

第一个配置选项是“ allow_growth”，它允许根据运行时分配仅分配GPU内存。 在启动时，它分配的内存量非常少，然后根据要求，它们扩展了张量流过程所需的GPU内存。 但是，它不会自动释放内存，这种情况会导致内存碎片。 要自动释放内存，我们必须通过以下方式通过在configProto中设置选项来打开此选项：

config=tf.ConfigProto()config.gpu_options.allow_growth=truesession=tf.Session(config=config,...)

The second configuration method is “per process GPU memory fraction option”, it determines the amount of memory that the particular GPU should allocate.

第二种配置方法是“每个进程GPU内存比例选项”，它确定特定GPU应该分配的内存量。

config = tf.ConfigProto()config.gpu_options.per_process_gpu_memory_fraction = 0.4session = tf.Session(config=config, ...)

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关于作者：

Shubham Sharma, currently working as Analytics Engineer in Data Science Domain. Has around 2+ years of experience in Data Science. Skilled in Python, Pandas, Anaconda, Tensorflow, Keras, Scikit learn, Numpy, Scipy, Microsoft Excel, SQL, Cassandra and Statistical Data Analysis, Hadoop, Hive, Pig, Spark, Pyspark. Connect with him at 

Shubham Sharma，目前在数据科学领域担任分析工程师。 在数据科学领域拥有大约2年以上的经验。 精通Python，Pandas，Anaconda，Tensorflow，Keras，Scikit学习，Numpy，Scipy，Microsoft Excel，SQL，Cassandra和统计数据分析，Hadoop，Hive，Pig，Spark，Pyspark。 通过与他联系

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tensorflow简介