既知の問題 | TensorFlow Core
XLA を使用してコンパイルすると、プログラムのパフォーマンスを大幅に向上させることができますが、TensorFlow 相互運用性にはいくつかの既知の問題があります。 異なるデバイス上の tf.Variable エラーメッセージ: INVALID_ARGUMENT: Trying to access resource <Variable> (defined @ <Loc>) located in device CPU:0 from device GPU:0 XLA クラスタは 1 台のデバイスでしか実行できません。別のデバイスにある tf.Variable に対して読み取りまたは書き込みを行うことはできません。通常、このエラーメッセージは、変数が最初から適切なデバイスに配置されていないことを示しています。エラーメッセージは、問題のある変数の場所を正確に指定します。 注意: int32 型の
Classification on imbalanced data | TensorFlow Core
This tutorial demonstrates how to classify a highly imbalanced dataset in which the number of examples in one class greatly outnumbers the examples in another. You will work with the Credit Card Fraud Detection dataset hosted on Kaggle. The aim is to detect a mere 492 fraudulent transactions from 284,807 transactions in total. You will use Keras to define the model and class weights to help the mo
Build from source | TensorFlow
Install the TensorFlow pip package dependencies (if using a virtual environment, omit the --user argument): pip install -U --user pip Install Bazel To build TensorFlow, you will need to install Bazel. Bazelisk is an easy way to install Bazel and automatically downloads the correct Bazel version for TensorFlow. For ease of use, add Bazelisk as the bazel executable in your PATH. If Bazelisk is not a
TensorFlow Probability
Deploy ML on mobile, microcontrollers and other edge devices
Module: tf.contrib | TensorFlow v1.15.0
Contrib module containing volatile or experimental code. Modules autograph module: This is the legacy module for AutoGraph, kept for backward compatibility. batching module: Ops and modules related to batch. bayesflow module: Ops for representing Bayesian computation. checkpoint module: Tools for working with object-based checkpoints. cloud module: Module for cloud ops. cluster_resolver module: St
Customization basics: tensors and operations | TensorFlow Core
This is an introductory TensorFlow tutorial that shows how to: Import the required package. Create and use tensors. Use GPU acceleration. Build a data pipeline with tf.data.Dataset. Import TensorFlow To get started, import the tensorflow module. As of TensorFlow 2, eager execution is turned on by default. Eager execution enables a more interactive frontend to TensorFlow, which you will later explo
TensorBoard: Embedding Visualization
Deploy ML on mobile, microcontrollers and other edge devices
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