Examples of using A tensorflow in English and their translations into Chinese
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How can I visualize a TensorFlow graph?
我怎样视觉化一个TensorFlowgraph？?A TensorFlow object responsible for saving model checkpoints.
We have also shared a TensorFlow Hub module for something new!
我们还分享了一个新的TensorFlowHub模块！下面是通用句子编码器。!A TensorFlow object responsible for saving model checkpoints.
负责存储模型检查点文件的TensorFlow对象。Finally, it is time to set up the sampler and run a TensorFlow session:.
最后，是时候设置采样器并运行TensorFlow会话了：.
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To initialize all the variables in a TensorFlow program, you must explicitly call a special operation, as follows:.
在TensorFlow程序中要想初始化这些变量，你必须明确调用一个特定的操作，如下：.In collaboration with Google Creative Lab,I'm excited to announce the release of a TensorFlow.
与谷歌创意实验室合作，我很高兴地宣布发布TensorFlow.The code below is the first attempt that a Tensorflow Python bindings user would make.
下面的代码是对TensorflowPython绑定的第一次尝试。In collaboration with Google Creative Lab,I'm excited to announce the release of a TensorFlow.
在与谷歌创意实验室的合作，我很高兴地宣布的发行TensorFlow.In this tutorial, you have learned how to build a Tensorflow model at the matrix level.
在本教程中，您已经学习了如何在矩阵级构建Tensorflow模型。TensorFlow Serving: A TensorFlow library allowing models to be served over HTTP/REST or gRPC/Protocol Buffers.
TensorFlowServing：TensorFlow库允许模型通过HTTP/REST或gRPC/协议缓冲区提供服务。The code below is the first attempt that a Tensorflow Python bindings user would make.
下面的代码是一位TensorflowPython用户第一次尝试时会写的代码。With those core building blocks in place,let's convert our simple program into a TensorFlow program.
有了这些核心构建块，让我们将简单程序转换为TensorFlow程序。
一类可运行TensorFlow会话的硬件，包括CPU、GPU和TPU。You then offload thecomputation of the entire dataflow graph(or a subgraph of it) to a TensorFlow tf.
然后，您可以将整个数据流图（或其子图）的计算卸载到TensorFlowtf.Once we have built the entire graph,we can create a TensorFlow session and run it on actual data.
一旦我们构建了整个图，我们可以创建一个TensorFlow会话并在实际数据上运行它。TensorFlow具有可用于多种语言的API，用于构建和执行TensorFlow图形。Once we have built the entire graph,we can create a TensorFlow session and run it on actual data.
在完成整体计算图的构建之后，我们方可创建TensorFlow会话并利用实际数据加以运行。This can be done easily by using the NumPy library,or by converting a Python n-dimensional array into a TensorFlow tensor.
这可以通过使用NumPy库或通过将Pythonn维数组转换为TensorFlow张量来轻松完成。Estimator class, which encapsulates logic that builds a TensorFlow graph and runs a TensorFlow session.
Estimator类的一个例子，封装logic以建立一个TensorFlow图并运行一个TensorFlowsession。If a TensorFlow operation has both CPU and GPU implementations, the GPU devices will be prioritized when the operation is assigned to a device.
如果TensorFlow操作同时有CPU和GPU的实现，操作将会优先分配给GPU设备。These ops support batches,and return a loss that can be minimised by feeding it to a TensorFlow Optimiser.
这些op支持批处理，并返回一个损失，通过将它馈入TensorFlowOptimiser可实现损失最小化。For the neural network training,this implementation uses Keras with a Tensorflow backend, though in the original paper the authors used raw Tensorflow. .
在训练神经网络的过程中，使用了带有TensorFlow后端的Keras，但是在原文中，作者使用的是原始的TensorFlow。While it is possible to initialize variables individually,you can easily initialize all the variables in a TensorFlow graph as follows:.
尽管您可以逐一初始化各个变量，但您也可以轻松地初始化一个TensorFlow图中的所有变量（如下所示）：.NumPy数组可以很容易地用辅助函数convert_to_tensor转换成TensorFlow张量，这可以帮助开发人员将Python对象转换成张量对象。This tutorial assumes you have a working Python 2 or 3 environment with SciPy,Keras 2.0 or higher with a TensorFlow or Theano backend.
本教程假定您有一个可运行的Python2或3环境，其中包含SciPy，具有TensorFlow或Theano后端的Keras2.0（或更高版本）。Using a dataset of more than 15,000 labeled Kepler signals,we created a TensorFlow model to distinguish planets from non-planets.
使用超过15000个标记开普勒信号的数据集，我们创建了一个TensorFlow模型来区分行星和非行星。We have a lot of TensorFlow.
我们有很多TensorFlow。I'm doing a lot of TensorFlow.
我们有很多TensorFlow。TensorFlow Model: A trained TensorFlow model saved on disk.
TensorFlow模型：一个保存在磁盘上训练好的TensorFlow模型。
