diff --git a/resource/logo_source.png b/resource/logo_source.png new file mode 100644 index 0000000000000000000000000000000000000000..7240ef4e5e49b975bb70dd3d27ae9f273f7e2172 Binary files /dev/null and b/resource/logo_source.png differ diff --git a/tutorials/source_en/advanced_use/computer_vision_application.md b/tutorials/source_en/advanced_use/computer_vision_application.md index 388e9816c43842d474f2816a6ddd057e9600e4ed..66c808266b91d8aeb5d9b2c1ed8889759f13b679 100644 --- a/tutorials/source_en/advanced_use/computer_vision_application.md +++ b/tutorials/source_en/advanced_use/computer_vision_application.md @@ -16,6 +16,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_en/advanced_use/computer_vision_application.md) + ## Overview Computer vision is the most widely researched and mature technology field of deep learning, and is widely used in scenarios such as mobile phone photographing, intelligent security protection, and automated driving. Since AlexNet won the ImageNet competition in 2012, deep learning has greatly promoted the development of the computer vision field. Almost all the most advanced computer vision algorithms are related to deep learning. Deep neural network can extract image features layer by layer and retain local invariance. It is widely used in visual tasks such as classification, detection, segmentation, tracking, retrieval, recognition, promotion, and reconstruction. diff --git a/tutorials/source_en/advanced_use/customized_debugging_information.md b/tutorials/source_en/advanced_use/customized_debugging_information.md index 0e4fdd17dc346225ce4d59736305ca8b56033546..3d99caa4cb08e14367afd6bee1ae6b81f7f03d3b 100644 --- a/tutorials/source_en/advanced_use/customized_debugging_information.md +++ b/tutorials/source_en/advanced_use/customized_debugging_information.md @@ -13,6 +13,9 @@ - [Log-related Environment Variables and Configurations](#log-related-environment-variables-and-configurations) + +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_en/advanced_use/customized_debugging_information.md) + ## Overview This section describes how to use the customized capabilities provided by MindSpore, such as callback, metrics, and log printing, to help you quickly debug the training network. diff --git a/tutorials/source_en/advanced_use/distributed_training.md b/tutorials/source_en/advanced_use/distributed_training.md index 0c14495a2720ded6a4d6e0999a634f8907c4fff0..147ba6877f8fa472bf56699bac25b3122a037513 100644 --- a/tutorials/source_en/advanced_use/distributed_training.md +++ b/tutorials/source_en/advanced_use/distributed_training.md @@ -17,6 +17,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_en/advanced_use/distributed_training.md) + ## Overview MindSpore supports `DATA_PARALLEL` and `AUTO_PARALLEL`. Automatic parallel is a distributed parallel mode that integrates data parallel, model parallel, and hybrid parallel. It can automatically establish cost models and select a parallel mode for users. diff --git a/tutorials/source_en/advanced_use/mindspore_cpu_win_install.md b/tutorials/source_en/advanced_use/mindspore_cpu_win_install.md index 81aef71b70a9a04ff7793ec405c9c23d80446581..88476fab7a72fbf7ebae05c5e82f84e1b03846a2 100644 --- a/tutorials/source_en/advanced_use/mindspore_cpu_win_install.md +++ b/tutorials/source_en/advanced_use/mindspore_cpu_win_install.md @@ -14,6 +14,8 @@ This document describes how to quickly install MindSpore on a Windows system wit +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_en/advanced_use/mindspore_cpu_win_install.md) + ## Environment Requirements ### System Requirements and Software Dependencies diff --git a/tutorials/source_en/advanced_use/mixed_precision.md b/tutorials/source_en/advanced_use/mixed_precision.md index 64e0ae52d3a7fc695b865e233cd87fa6225b2e01..d99664218cf3a5036d783ad33e94873be4ad5a71 100644 --- a/tutorials/source_en/advanced_use/mixed_precision.md +++ b/tutorials/source_en/advanced_use/mixed_precision.md @@ -10,6 +10,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_en/advanced_use/mixed_precision.md) + ## Overview The mixed precision training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. diff --git a/tutorials/source_en/advanced_use/model_security.md b/tutorials/source_en/advanced_use/model_security.md index 1ecc9389c4b74c11fb3d70674acfd2303c3df75f..01a6059d345eff304c6ade49cbfdb89b3a9908da 100644 --- a/tutorials/source_en/advanced_use/model_security.md +++ b/tutorials/source_en/advanced_use/model_security.md @@ -15,6 +15,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_en/advanced_use/model_security.md) + ## Overview This tutorial describes the model security protection methods provided by MindArmour, helping you quickly use MindArmour and provide certain security protection capabilities for your AI model. diff --git a/tutorials/source_en/advanced_use/nlp_application.md b/tutorials/source_en/advanced_use/nlp_application.md index 9ec0a1de9e6d74c21826194235656113e62c738b..089d52008e258f6c8aedcac95a371fdc1850fa27 100644 --- a/tutorials/source_en/advanced_use/nlp_application.md +++ b/tutorials/source_en/advanced_use/nlp_application.md @@ -20,6 +20,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_en/advanced_use/nlp_application.md) + ## Overview Sentiment classification is a subset of text classification in NLP, and is the most basic application of NLP. It is a process of analyzing and inferencing affective states and subjective information, that is, analyzing whether a person's sentiment is positive or negative. diff --git a/tutorials/source_en/advanced_use/on_device_inference.md b/tutorials/source_en/advanced_use/on_device_inference.md index 7a93e30dafc1cb77eab4f7fdcc21a85c009844d0..431c42d157775da19eb7a7d7dc74aafbd366c99b 100644 --- a/tutorials/source_en/advanced_use/on_device_inference.md +++ b/tutorials/source_en/advanced_use/on_device_inference.md @@ -11,6 +11,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_en/advanced_use/on_device_inference.md) + ## Overview MindSpore Predict is a lightweight deep neural network inference engine that provides the inference function for models trained by MindSpore on the device side. This tutorial describes how to use and compile MindSpore Predict. diff --git a/tutorials/source_en/advanced_use/visualization_tutorials.md b/tutorials/source_en/advanced_use/visualization_tutorials.md index d1a74a55f4a83393e3b0d317e16592934f6b9b56..ccbe28ce2b30fc7e8fdfc2b5e414e3a24b8af4a8 100644 --- a/tutorials/source_en/advanced_use/visualization_tutorials.md +++ b/tutorials/source_en/advanced_use/visualization_tutorials.md @@ -19,6 +19,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_en/advanced_use/visualization_tutorials.md) + ## Overview Scalars, images, computational graphs, and model hyperparameters during training are recorded in files and can be viewed on the web page. diff --git a/tutorials/source_en/quick_start/quick_start.md b/tutorials/source_en/quick_start/quick_start.md index 0ad4925190e0871799c9addc858df112243c53b0..ea0535d449972f906bf1cc210a77acde11a75db9 100644 --- a/tutorials/source_en/quick_start/quick_start.md +++ b/tutorials/source_en/quick_start/quick_start.md @@ -2,6 +2,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_en/quick_start/quick_start.md) + - [Implementing an Image Classification Application](#implementing-an-image-classification-application) - [Overview](#overview) - [Preparations](#preparations) diff --git a/tutorials/source_en/use/data_preparation/converting_datasets.md b/tutorials/source_en/use/data_preparation/converting_datasets.md index 474cd00aab80b07960c493d386f5626205aacf8a..cd311c1f037945b85d06bc3e8e40c2950ae7bba2 100644 --- a/tutorials/source_en/use/data_preparation/converting_datasets.md +++ b/tutorials/source_en/use/data_preparation/converting_datasets.md @@ -14,6 +14,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_en/use/data_preparation/converting_datasets.md) + ## Overview You can convert non-standard datasets and common datasets into the MindSpore data format so that they can be easily loaded to MindSpore for training. In addition, the performance of MindSpore in some scenarios is optimized, which delivers better user experience when you use datasets in the MindSpore data format. diff --git a/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md b/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md index 4a34368bc79617433e46ea5dc2b35bb16f1a4e18..d31697d5275aa19351048bdad0ada24a6c8d4054 100644 --- a/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md +++ b/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md @@ -16,6 +16,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md) + ## Overview Data is the basis of deep learning. Data input plays an important role in the deep neural network training. Therefore, after the original dataset is obtained and before data is loaded and trained, data processing or augmentation is often required due to data size and performance restrictions, to obtain optimized data input. diff --git a/tutorials/source_en/use/data_preparation/loading_the_datasets.md b/tutorials/source_en/use/data_preparation/loading_the_datasets.md index 355d673c2e28293b0a5a19db3c4b9c02ab353535..e97eddb9e8727c9a0b04a2fb962305ad117dc45b 100644 --- a/tutorials/source_en/use/data_preparation/loading_the_datasets.md +++ b/tutorials/source_en/use/data_preparation/loading_the_datasets.md @@ -13,6 +13,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_en/use/data_preparation/loading_the_datasets.md) + ## Overview MindSpore helps you load common datasets, datasets of specific data formats, or custom datasets. Before loading a dataset, you need to import the required library `mindspore.dataset`. diff --git a/tutorials/source_en/use/debugging_in_pynative_mode.md b/tutorials/source_en/use/debugging_in_pynative_mode.md index 3f9ba9b59daa0faf85696b09434d2e877460a1da..4fc8067242a16617eb38dedd8fd178e96c736b00 100644 --- a/tutorials/source_en/use/debugging_in_pynative_mode.md +++ b/tutorials/source_en/use/debugging_in_pynative_mode.md @@ -11,6 +11,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_en/use/debugging_in_pynative_mode.md) + ## Overview MindSpore supports the following running modes which are optimized in terms of debugging or running: diff --git a/tutorials/source_en/use/saving_and_loading_model_parameters.md b/tutorials/source_en/use/saving_and_loading_model_parameters.md index 88d80140d166b2ab3309a71335de78899101b7b5..6d321d0c0e0bde68b3142d778b21df0058aa6007 100644 --- a/tutorials/source_en/use/saving_and_loading_model_parameters.md +++ b/tutorials/source_en/use/saving_and_loading_model_parameters.md @@ -13,6 +13,7 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_en/use/saving_and_loading_model_parameters.md) ## Overview diff --git a/tutorials/source_zh_cn/advanced_use/checkpoint_for_hybrid_parallel.md b/tutorials/source_zh_cn/advanced_use/checkpoint_for_hybrid_parallel.md index b70c466575034f7da535b51ca666c5588b20f88a..5c2b7d5a55e3532fc6dd4f7bdacabbf13fbc1e69 100644 --- a/tutorials/source_zh_cn/advanced_use/checkpoint_for_hybrid_parallel.md +++ b/tutorials/source_zh_cn/advanced_use/checkpoint_for_hybrid_parallel.md @@ -26,6 +26,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/advanced_use/checkpoint_for_hybrid_parallel.md) + ## 概述 ### 背景 diff --git a/tutorials/source_zh_cn/advanced_use/computer_vision_application.md b/tutorials/source_zh_cn/advanced_use/computer_vision_application.md index 0135c636c84649fa8aa449126fe3abb40be2b3c0..79db9460645100aeb1913ca854e2bc74bad032c7 100644 --- a/tutorials/source_zh_cn/advanced_use/computer_vision_application.md +++ b/tutorials/source_zh_cn/advanced_use/computer_vision_application.md @@ -16,6 +16,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/advanced_use/computer_vision_application.md) + ## 概述 计算机视觉是当前深度学习研究最广泛、落地最成熟的技术领域，在手机拍照、智能安防、自动驾驶等场景有广泛应用。从2012年AlexNet在ImageNet比赛夺冠以来，深度学习深刻推动了计算机视觉领域的发展，当前最先进的计算机视觉算法几乎都是深度学习相关的。深度神经网络可以逐层提取图像特征，并保持局部不变性，被广泛应用于分类、检测、分割、跟踪、检索、识别、提升、重建等视觉任务中。 diff --git a/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md b/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md index a1e4bb676e4c3b538eff9a61e93b971cee864d64..1651a262f4792501243aa0c53e2d81a1f1b4c859 100644 --- a/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md +++ b/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md @@ -12,6 +12,9 @@ - [日志相关的环境变量和配置](#日志相关的环境变量和配置) + +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md) + ## 概述 本文介绍如何使用MindSpore提供的Callback、metrics、print算子、日志打印等自定义能力，帮助用户快速调试训练网络。 diff --git a/tutorials/source_zh_cn/advanced_use/distributed_training.md b/tutorials/source_zh_cn/advanced_use/distributed_training.md index 8d6fa923cc640e43d4868b7ab0f7cdd567aeace5..b7ded83975566aec6ada249397d00703e3cbfff1 100644 --- a/tutorials/source_zh_cn/advanced_use/distributed_training.md +++ b/tutorials/source_zh_cn/advanced_use/distributed_training.md @@ -17,6 +17,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/advanced_use/distributed_training.md) + ## 概述 在深度学习中，当数据集和参数量的规模越来越大，训练所需的时间和硬件资源会随之增加，最后会变成制约训练的瓶颈。分布式并行训练，可以降低对内存、计算性能等硬件的需求，是进行训练的重要优化手段。根据并行的原理及模式不同，业界主流的并行类型有以下几种： diff --git a/tutorials/source_zh_cn/advanced_use/mindspore_cpu_win_install.md b/tutorials/source_zh_cn/advanced_use/mindspore_cpu_win_install.md index 4f58546b958d8c452d7f161cf0d945f3e8098ab0..3d9c3fbcb955d17cee1b847ad95e2ae4b2a16d6e 100644 --- a/tutorials/source_zh_cn/advanced_use/mindspore_cpu_win_install.md +++ b/tutorials/source_zh_cn/advanced_use/mindspore_cpu_win_install.md @@ -14,6 +14,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/advanced_use/mindspore_cpu_win_install.md) + ## 环境要求 ### 系统要求和软件依赖 diff --git a/tutorials/source_zh_cn/advanced_use/mixed_precision.md b/tutorials/source_zh_cn/advanced_use/mixed_precision.md index b09ba667ef39fea743208bc768e153b572c5e905..b7c66a54a1c266b9c8690f6ce599feadfe3bb9b3 100644 --- a/tutorials/source_zh_cn/advanced_use/mixed_precision.md +++ b/tutorials/source_zh_cn/advanced_use/mixed_precision.md @@ -10,6 +10,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/advanced_use/mixed_precision.md) + ## 概述 混合精度训练方法是通过混合使用单精度和半精度数据格式来加速深度神经网络训练的过程，同时保持了单精度训练所能达到的网络精度。混合精度训练能够加速计算过程，同时减少内存使用和存取，并使得在特定的硬件上可以训练更大的模型或batch size。 diff --git a/tutorials/source_zh_cn/advanced_use/model_security.md b/tutorials/source_zh_cn/advanced_use/model_security.md index 88ed988d64685fbe5a20ec75d18a3d6edc4809fe..595649d3d4b2b5e94dc1a59cd15fd88dbe0eb596 100644 --- a/tutorials/source_zh_cn/advanced_use/model_security.md +++ b/tutorials/source_zh_cn/advanced_use/model_security.md @@ -15,6 +15,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/advanced_use/model_security.md) + ## 概述 本教程介绍MindArmour提供的模型安全防护手段，引导您快速使用MindArmour，为您的AI模型提供一定的安全防护能力。 diff --git a/tutorials/source_zh_cn/advanced_use/network_migration.md b/tutorials/source_zh_cn/advanced_use/network_migration.md index 5e3a53a75a16b6069d109ba84a35f44883abbe77..0b5619d8b0403d6be85af6c30f39a3c3fcfb0812 100644 --- a/tutorials/source_zh_cn/advanced_use/network_migration.md +++ b/tutorials/source_zh_cn/advanced_use/network_migration.md @@ -17,6 +17,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/advanced_use/network_migration.md) + ## 概述 你可能已经编写过TensorFlow、PyTorch等框架的脚本，本教程介绍如何将已有的TensorFlow、PyTorch等的网络迁移到MindSpore，包括主要步骤和操作建议，帮助你快速进行网络迁移。 diff --git a/tutorials/source_zh_cn/advanced_use/nlp_application.md b/tutorials/source_zh_cn/advanced_use/nlp_application.md index bc4c7bb18b9c0b768c63b1780d3679d87ed09c3f..869a8bf9def1ed04d9c98f9cb3f5472418ddb46f 100644 --- a/tutorials/source_zh_cn/advanced_use/nlp_application.md +++ b/tutorials/source_zh_cn/advanced_use/nlp_application.md @@ -20,6 +20,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/advanced_use/nlp_application.md) + ## 概述 情感分类是自然语言处理中文本分类问题的子集，属于自然语言处理最基础的应用。它是对带有感情色彩的主观性文本进行分析和推理的过程，即分析说话人的态度，是倾向正面还是反面。 diff --git a/tutorials/source_zh_cn/advanced_use/on_device_inference.md b/tutorials/source_zh_cn/advanced_use/on_device_inference.md index 5830298f8b606a206ef1f1840d00366af559e679..d32e6916ba396a67846dbf9ed33dc02d7f929e1d 100644 --- a/tutorials/source_zh_cn/advanced_use/on_device_inference.md +++ b/tutorials/source_zh_cn/advanced_use/on_device_inference.md @@ -11,6 +11,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/advanced_use/on_device_inference.md) + ## 概述 MindSpore Predict是一个轻量级的深度神经网络推理引擎，提供了将MindSpore训练出的模型在端侧进行推理的功能。本教程介绍MindSpore Predict的编译方法和使用指南。 diff --git a/tutorials/source_zh_cn/advanced_use/use_on_the_cloud.md b/tutorials/source_zh_cn/advanced_use/use_on_the_cloud.md index 172505d597565847faf4b0ce3e5b50034004d01a..367600fbc5414680f0f4c28925e38f2441df0b51 100644 --- a/tutorials/source_zh_cn/advanced_use/use_on_the_cloud.md +++ b/tutorials/source_zh_cn/advanced_use/use_on_the_cloud.md @@ -3,6 +3,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/advanced_use/use_on_the_cloud.md) + - [在云上使用MindSpore](#在云上使用mindspore) - [概述](#概述) - [准备工作](#准备工作) diff --git a/tutorials/source_zh_cn/advanced_use/visualization_tutorials.md b/tutorials/source_zh_cn/advanced_use/visualization_tutorials.md index a632b536ea59492240ba1facaecaeebcdc83b2a2..a082b1ad23d51cbd70c363545e2b1e82d7065897 100644 --- a/tutorials/source_zh_cn/advanced_use/visualization_tutorials.md +++ b/tutorials/source_zh_cn/advanced_use/visualization_tutorials.md @@ -24,6 +24,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/advanced_use/visualization_tutorials.md) + ## 概述 训练过程中的标量、图像、计算图以及模型超参等信息记录到文件中，通过可视化界面供用户查看。 diff --git a/tutorials/source_zh_cn/quick_start/quick_start.md b/tutorials/source_zh_cn/quick_start/quick_start.md index f4ffebd70e9d5c714bea5fe571313d09e362a36b..a7319bbb4675d337c575ab5c7b04ff591d34543b 100644 --- a/tutorials/source_zh_cn/quick_start/quick_start.md +++ b/tutorials/source_zh_cn/quick_start/quick_start.md @@ -24,6 +24,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/quick_start/quick_start.md) + ## 概述 下面我们通过一个实际样例，带领大家体验MindSpore基础的功能，对于一般的用户而言，完成整个样例实践会持续20~30分钟。 diff --git a/tutorials/source_zh_cn/use/data_preparation/converting_datasets.md b/tutorials/source_zh_cn/use/data_preparation/converting_datasets.md index b8927be925e06028d389ba83233d0d106fd6fcd4..501a38bcd012eaaab35453ffe18216124506af46 100644 --- a/tutorials/source_zh_cn/use/data_preparation/converting_datasets.md +++ b/tutorials/source_zh_cn/use/data_preparation/converting_datasets.md @@ -14,6 +14,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/use/data_preparation/converting_datasets.md) + ## 概述 用户可以将非标准的数据集和常见的数据集转换为MindSpore数据格式，从而方便地加载到MindSpore中进行训练。同时，MindSpore在部分场景做了性能优化，使用MindSpore数据格式可以获得更好的性能体验。 diff --git a/tutorials/source_zh_cn/use/data_preparation/data_processing_and_augmentation.md b/tutorials/source_zh_cn/use/data_preparation/data_processing_and_augmentation.md index 6571a0171a99c6118c2f7c0a2946373b9711d00a..836e36e3993b25d4011fad11e0a508a5e33084fb 100644 --- a/tutorials/source_zh_cn/use/data_preparation/data_processing_and_augmentation.md +++ b/tutorials/source_zh_cn/use/data_preparation/data_processing_and_augmentation.md @@ -16,6 +16,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/use/data_preparation/data_processing_and_augmentation.md) + ## 概述 数据是深度学习的基础，有好的数据输入，可以对整个深度神经网络训练起到非常积极的作用。所以在获取到原始的数据集后，数据加载训练前，因为数据量、性能等等限制，往往会需要先进行数据处理或者数据增强，从而获得更加优化的数据输入。 diff --git a/tutorials/source_zh_cn/use/data_preparation/loading_the_datasets.md b/tutorials/source_zh_cn/use/data_preparation/loading_the_datasets.md index f46f84ae9e6e400cec22cad29094e783387e21df..07e4af0f5e7fe5d36a78f2943dbc274b0628461f 100644 --- a/tutorials/source_zh_cn/use/data_preparation/loading_the_datasets.md +++ b/tutorials/source_zh_cn/use/data_preparation/loading_the_datasets.md @@ -13,6 +13,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/use/data_preparation/loading_the_datasets.md) + ## 概述 MindSpore可以帮助你加载常见的数据集、特定数据格式的数据集或自定义的数据集。加载数据集时，需先导入所需要依赖的库`mindspore.dataset`。 diff --git a/tutorials/source_zh_cn/use/debugging_in_pynative_mode.md b/tutorials/source_zh_cn/use/debugging_in_pynative_mode.md index 9a9e9e6749669813445bffef4e63d2835f4be8e1..c22bb3405a41d25be257962f1d72da561da2b0ca 100644 --- a/tutorials/source_zh_cn/use/debugging_in_pynative_mode.md +++ b/tutorials/source_zh_cn/use/debugging_in_pynative_mode.md @@ -11,6 +11,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/use/debugging_in_pynative_mode.md) + ## 概述 MindSpore支持两种运行模式，在调试或者运行方面做了不同的优化: diff --git a/tutorials/source_zh_cn/use/saving_and_loading_model_parameters.md b/tutorials/source_zh_cn/use/saving_and_loading_model_parameters.md index 0bdf8fa7f711904001848ef415fdb693ef694d58..c79003ab8856fa017b4b097b9503d8708643846b 100644 --- a/tutorials/source_zh_cn/use/saving_and_loading_model_parameters.md +++ b/tutorials/source_zh_cn/use/saving_and_loading_model_parameters.md @@ -13,6 +13,8 @@ +[![source](/resource/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/use/saving_and_loading_model_parameters.md) + ## 概述 在模型训练过程中，可以添加检查点(CheckPoint)用于保存模型的参数，以便进行推理及中断后再训练使用。