diff --git a/CONTRIBUTING_DOC.md b/CONTRIBUTING_DOC.md index 6c283862aeb81f5c3566fe00eec4d9041e789394..3820974024e2bd6eb17b39f69103c9202f414755 100644 --- a/CONTRIBUTING_DOC.md +++ b/CONTRIBUTING_DOC.md @@ -77,7 +77,7 @@ By default, tutorials and documents of the latest version are displayed on the o ![master_doc_en](./resource/_static/master_doc_en.png) -Take **Quick Start for Beginners** as an example. The document link is . +Take **Quick Start for Beginners** as an example. The document link is . ## API diff --git a/docs/federated/docs/source_en/image_classification_application.md b/docs/federated/docs/source_en/image_classification_application.md index f0cf6d3532132765d79eb27cc5b08beba49fae38..867a4d1ac3d5b9d835173d5b7d93c1567cc03514 100644 --- a/docs/federated/docs/source_en/image_classification_application.md +++ b/docs/federated/docs/source_en/image_classification_application.md @@ -20,7 +20,7 @@ Users can also define the dataset by themselves. Note that the dataset must be a 1. **Define the network and training process** - For the definition of the specific network and training process, please refer to [Beginners Getting Started](https://www.mindspore.cn/tutorials/en/master/quick_start.html#%E5%88%9B%E5%BB%BA%E6%20%A8%A1%E5%9E%8B). + For the definition of the specific network and training process, please refer to [Beginners Getting Started](https://www.mindspore.cn/tutorials/en/master/beginner/quick_start.html). We provide the network definition file [model.py](https://gitee.com/mindspore/mindspore/blob/master/tests/st/fl/mobile/src/model.py) and the training process definition file [run_export_lenet](https://gitee.com/mindspore/mindspore/blob/master/tests/st/fl/cross_device_lenet/cloud/run_export_lenet.py) for your reference. diff --git a/docs/mindspore/api/source_en/api_python/mindspore.ops.rst b/docs/mindspore/api/source_en/api_python/mindspore.ops.rst index ba5f688e5a79e0231302b3b51899dbd368b90828..bbbd05d082d959c014985b964b1d38b46a0c0bde 100644 --- a/docs/mindspore/api/source_en/api_python/mindspore.ops.rst +++ b/docs/mindspore/api/source_en/api_python/mindspore.ops.rst @@ -269,7 +269,7 @@ The functional operators are the pre-instantiated Primitive operators, which can * - mindspore.ops.stack - Refer to :class:`mindspore.ops.Stack`. * - mindspore.ops.stop_gradient - - Disable update during back propagation. (`stop_gradient `_) + - Disable update during back propagation. (`stop_gradient `_) * - mindspore.ops.strided_slice - Refer to :class:`mindspore.ops.StridedSlice`. * - mindspore.ops.string_concat diff --git a/docs/mindspore/migration_guide/source_en/api_mapping/pytorch_api_mapping.md b/docs/mindspore/migration_guide/source_en/api_mapping/pytorch_api_mapping.md index 2cadb0329508d720ba0b11714184fd30851e3a24..2c997dace7d2f31be4a98b0698b088a9af392263 100644 --- a/docs/mindspore/migration_guide/source_en/api_mapping/pytorch_api_mapping.md +++ b/docs/mindspore/migration_guide/source_en/api_mapping/pytorch_api_mapping.md @@ -162,9 +162,9 @@ More MindSpore developers are also welcome to participate in improving the mappi | PyTorch 1.5.0 APIs | MindSpore APIs | Description | | ----------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- | | [torch.autograd.backward](https://pytorch.org/docs/1.5.0/autograd.html#torch.autograd.backward) | [mindspore.ops.GradOperation](https://mindspore.cn/docs/api/en/master/api_python/ops/mindspore.ops.GradOperation.html#mindspore.ops.GradOperation) | [diff](https://www.mindspore.cn/docs/migration_guide/en/master/api_mapping/pytorch_diff/GradOperation.html) | -| [torch.autograd.enable_grad](https://pytorch.org/docs/1.5.0/autograd.html#torch.autograd.enable_grad) | [mindspore.ops.stop_gradient](https://www.mindspore.cn/tutorials/en/master/autograd.html#stop-gradient) | [diff](https://www.mindspore.cn/docs/migration_guide/en/master/api_mapping/pytorch_diff/stop_gradient.html) | +| [torch.autograd.enable_grad](https://pytorch.org/docs/1.5.0/autograd.html#torch.autograd.enable_grad) | [mindspore.ops.stop_gradient](https://www.mindspore.cn/tutorials/en/master/beginner/autograd.html#stopping-gradient) | [diff](https://www.mindspore.cn/docs/migration_guide/en/master/api_mapping/pytorch_diff/stop_gradient.html) | | [torch.autograd.grad](https://pytorch.org/docs/1.5.0/autograd.html#torch.autograd.grad) | [mindspore.ops.GradOperation](https://mindspore.cn/docs/api/en/master/api_python/ops/mindspore.ops.GradOperation.html#mindspore.ops.GradOperation) | [diff](https://www.mindspore.cn/docs/migration_guide/en/master/api_mapping/pytorch_diff/GradOperation.html) | -| [torch.autograd.no_grad](https://pytorch.org/docs/1.5.0/autograd.html#torch.autograd.no_grad) | [mindspore.ops.stop_gradient](https://www.mindspore.cn/tutorials/en/master/autograd.html#stop-gradient) | [diff](https://www.mindspore.cn/docs/migration_guide/en/master/api_mapping/pytorch_diff/stop_gradient.html) | +| [torch.autograd.no_grad](https://pytorch.org/docs/1.5.0/autograd.html#torch.autograd.no_grad) | [mindspore.ops.stop_gradient](https://www.mindspore.cn/tutorials/en/master/beginner/autograd.html#stopping-gradient) | [diff](https://www.mindspore.cn/docs/migration_guide/en/master/api_mapping/pytorch_diff/stop_gradient.html) | | [torch.autograd.variable](https://pytorch.org/docs/1.5.0/autograd.html#torch.autograd.variable-deprecated)| [mindspore.Parameter](https://mindspore.cn/docs/api/en/master/api_python/mindspore/mindspore.Parameter.html#mindspore.Parameter) | | ## troch.cuda diff --git a/docs/mindspore/migration_guide/source_en/api_mapping/pytorch_diff/stop_gradient.md b/docs/mindspore/migration_guide/source_en/api_mapping/pytorch_diff/stop_gradient.md index b991c1353871fac6c2babfa35e5c0fdcc59eb122..521ab2d00f5ba603af8b296e333863b09c2f6ec9 100644 --- a/docs/mindspore/migration_guide/source_en/api_mapping/pytorch_diff/stop_gradient.md +++ b/docs/mindspore/migration_guide/source_en/api_mapping/pytorch_diff/stop_gradient.md @@ -24,10 +24,10 @@ For more information, see [torch.autograd.no_grad](https://pytorch.org/docs/1.5. mindspore.ops.stop_gradient(input) ``` -For more information, see [mindspore.ops.stop_gradient](https://www.mindspore.cn/tutorials/en/master/autograd.html#stop-gradient). +For more information, see [mindspore.ops.stop_gradient](https://www.mindspore.cn/tutorials/en/master/beginner/autograd.html#stopping-gradient). ## Differences PyTorch: Use `torch.autograd.enable_grad` to enable gradient calculation, and `torch.autograd.no_grad` to disable gradient calculation. -MindSpore: Use [stop_gradient](https://www.mindspore.cn/tutorials/en/master/autograd.html#stop-gradient) to disable calculation of gradient for certain operators. +MindSpore: Use [stop_gradient](https://www.mindspore.cn/tutorials/en/master/beginner/autograd.html#stopping-gradient) to disable calculation of gradient for certain operators. diff --git a/docs/mindspore/programming_guide/source_en/api_structure.ipynb b/docs/mindspore/programming_guide/source_en/api_structure.ipynb index e5272f967db1628ba43b56860e4f879a9554d5d4..9d2bb23660d1a29f24e4e09eadefa63215c7f8f3 100644 --- a/docs/mindspore/programming_guide/source_en/api_structure.ipynb +++ b/docs/mindspore/programming_guide/source_en/api_structure.ipynb @@ -25,7 +25,7 @@ "\n", "MindSpore originates from the best practices of the entire industry and provides unified model training, inference, and export APIs for data scientists and algorithm engineers. It supports flexible deployment in different scenarios such as the device, edge, and cloud, and promotes the prosperity of domains such as deep learning and scientific computing.\n", "\n", - "MindSpore provides the Python programming paradigm. Users can use the native control logic of Python to build complex neural network models, simplifying AI programming. For details, see [Quick Start for Beginners](https://www.mindspore.cn/tutorials/en/master/quick_start.html).\n", + "MindSpore provides the Python programming paradigm. Users can use the native control logic of Python to build complex neural network models, simplifying AI programming. For details, see [Quick Start for Beginners](https://www.mindspore.cn/tutorials/en/master/beginner/quick_start.html).\n", "\n", "Currently, there are two execution modes of a mainstream deep learning framework: a static graph mode and a dynamic graph mode. The static graph mode has a relatively high training performance, but is difficult to debug. On the contrary, the dynamic graph mode is easy to debug, but is difficult to execute efficiently. MindSpore provides an encoding mode that unifies dynamic and static graphs, which greatly improves the compatibility between static and dynamic graphs. Instead of developing multiple sets of code, users can switch between the dynamic and static graph modes by changing only one line of code. For example, set `context.set_context(mode=context.PYNATIVE_MODE)` to switch to the dynamic graph mode, or set `context.set_context(mode=context.GRAPH_MODE)` to switch to the static graph mode, which facilitates development and debugging, and improves performance experience.\n", "\n", @@ -127,4 +127,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/mindspore/programming_guide/source_en/index.rst b/docs/mindspore/programming_guide/source_en/index.rst index 9bd2367b425ad5651f50dad993e1a800ab22d2f2..70671ea96a8f92c4d8a5e5e859fccd838cdf096e 100644 --- a/docs/mindspore/programming_guide/source_en/index.rst +++ b/docs/mindspore/programming_guide/source_en/index.rst @@ -34,8 +34,8 @@ MindSpore Programming Guide :caption: Quickstart :hidden: - Implementing Simple Linear Function Fitting↗ - Implementing an Image Classification Application↗ + Implementing Simple Linear Function Fitting↗ + Implementing an Image Classification Application↗ .. toctree:: :glob: diff --git a/docs/mindspore/programming_guide/source_en/loss.md b/docs/mindspore/programming_guide/source_en/loss.md index 4977c8abdca4df64fcc7fadd9b36d5f806437ad7..94bad2f59c5b9bd79da58b6931ea3c427aa39eb9 100644 --- a/docs/mindspore/programming_guide/source_en/loss.md +++ b/docs/mindspore/programming_guide/source_en/loss.md @@ -136,7 +136,7 @@ Now we train model by the defined L1Loss. Taking the simple linear function fitting as an example. The dataset and network structure is defined as follows: -> For a detailed introduction of linear fitting, please refer to the tutorial [Implementing Simple Linear Function Fitting](https://www.mindspore.cn/tutorials/en/master/linear_regression.html). +> For a detailed introduction of linear fitting, please refer to the tutorial [Implementing Simple Linear Function Fitting](https://www.mindspore.cn/tutorials/en/r1.6/linear_regression.html). 1. Defining the Dataset diff --git a/docs/mindspore/programming_guide/source_en/quick_start/quick_video/quick_start_video.md b/docs/mindspore/programming_guide/source_en/quick_start/quick_video/quick_start_video.md index 663455732943f8182b48ef26552510af449c9c92..ce303257c20059ade4c7f2e0bb420e6354497027 100644 --- a/docs/mindspore/programming_guide/source_en/quick_start/quick_video/quick_start_video.md +++ b/docs/mindspore/programming_guide/source_en/quick_start/quick_video/quick_start_video.md @@ -8,4 +8,4 @@ **View code**: -**View the full tutorial**: +**View the full tutorial**: diff --git a/docs/mindspore/programming_guide/source_en/run.md b/docs/mindspore/programming_guide/source_en/run.md index f6b5500bb41a73d77b737fd59a02247a78f017ac..bd2ed551edcffd492c572185c7858ad02783699d 100644 --- a/docs/mindspore/programming_guide/source_en/run.md +++ b/docs/mindspore/programming_guide/source_en/run.md @@ -264,7 +264,7 @@ epoch: 1 step: 1500, loss is 0.032973606 epoch: 1 step: 1875, loss is 0.06105463 ``` -> For details about how to obtain the MNIST dataset used in the example, see [Downloading the Dataset](https://www.mindspore.cn/tutorials/en/master/quick_start.html#downloading-the-dataset). +> For details about how to obtain the MNIST dataset used in the example, see [Downloading the Dataset](https://www.mindspore.cn/tutorials/en/master/beginner/quick_start.html#downloading-the-dataset). > Use the PyNative mode for debugging, including the execution of single operator, common function, and network training model. For details, see [Debugging in PyNative Mode](https://www.mindspore.cn/docs/programming_guide/en/master/debug_in_pynative_mode.html). ### Executing an Inference Model @@ -406,4 +406,4 @@ In the preceding information: - `checkpoint_lenet-1_1875.ckpt`: name of the saved checkpoint model file. - `load_param_into_net`: loads parameters to the network. -> For details about how to save the `checkpoint_lenet-1_1875.ckpt` file, see [Training the Network](https://www.mindspore.cn/tutorials/en/master/quick_start.html#training-and-saving-the-model). +> For details about how to save the `checkpoint_lenet-1_1875.ckpt` file, see [Training the Network](https://www.mindspore.cn/tutorials/en/master/beginner/quick_start.html#training-and-saving-the-model). diff --git a/docs/probability/docs/source_en/probability.md b/docs/probability/docs/source_en/probability.md index e2b3e5f67b7a28a39aa660e0d07d49769898d0f0..0e267d0bd582cd9d847997dc7041cc7beff7ec19 100644 --- a/docs/probability/docs/source_en/probability.md +++ b/docs/probability/docs/source_en/probability.md @@ -804,7 +804,7 @@ decoder = Decoder() cvae = ConditionalVAE(encoder, decoder, hidden_size=400, latent_size=20, num_classes=10) ``` -Load a dataset, for example, Mnist. For details about the data loading and preprocessing process, see [Quick Start for Beginners](https://www.mindspore.cn/tutorials/en/master/quick_start.html). The create_dataset function is used to create a data iterator. +Load a dataset, for example, Mnist. For details about the data loading and preprocessing process, see [Quick Start for Beginners](https://www.mindspore.cn/tutorials/en/master/beginner/quick_start.html). The create_dataset function is used to create a data iterator. ```python ds_train = create_dataset(image_path, 128, 1) diff --git a/docs/probability/docs/source_en/using_bnn.md b/docs/probability/docs/source_en/using_bnn.md index dee1e5b5cecd5a82844d6284d112946e998b83da..9612ebff9ac2f0e43507f009573d008eb5fd6619 100644 --- a/docs/probability/docs/source_en/using_bnn.md +++ b/docs/probability/docs/source_en/using_bnn.md @@ -77,7 +77,7 @@ download_dataset("https://mindspore-website.obs.myhuaweicloud.com/notebook/datas ### Defining the Dataset Enhancement Method -The original training dataset of the MNIST dataset is 60,000 single-channel digital images with $28\times28$ pixels. The LeNet5 network containing the Bayesian layer used in this training received the training data tensor as `(32,1 ,32,32)`, through the custom create_dataset function to enhance the original dataset to meet the training requirements of the data, the specific enhancement operation explanation can refer to [Quick Start for Beginners](https://www.mindspore.cn/tutorials/en/master/quick_start.html). +The original training dataset of the MNIST dataset is 60,000 single-channel digital images with $28\times28$ pixels. The LeNet5 network containing the Bayesian layer used in this training received the training data tensor as `(32,1 ,32,32)`, through the custom create_dataset function to enhance the original dataset to meet the training requirements of the data, the specific enhancement operation explanation can refer to [Quick Start for Beginners](https://www.mindspore.cn/tutorials/en/master/beginner/quick_start.html). ```python import mindspore.dataset.vision.c_transforms as CV diff --git a/docs/probability/docs/source_en/using_the_uncertainty_toolbox.md b/docs/probability/docs/source_en/using_the_uncertainty_toolbox.md index 751c7a77f8793bd7fd4a5f0f81d13359d9073786..29c5510b1ea6e63c880c99f5d8a021c4592efe33 100644 --- a/docs/probability/docs/source_en/using_the_uncertainty_toolbox.md +++ b/docs/probability/docs/source_en/using_the_uncertainty_toolbox.md @@ -156,7 +156,7 @@ MindSpore uses the Uncertainty Toolbox `UncertaintyEvaluation` interface to meas ### Preparing the Model Weight Parameter File -In this example, the corresponding model weight parameter file `checkpoint_lenet.ckpt` has been prepared. This parameter file is the weight parameter file saved after training for 5 epochs in [Quick Start for Beginners](https://www.mindspore.cn/tutorials/en/master/quick_start.html), execute the following command to download: +In this example, the corresponding model weight parameter file `checkpoint_lenet.ckpt` has been prepared. This parameter file is the weight parameter file saved after training for 5 epochs in [Quick Start for Beginners](https://www.mindspore.cn/tutorials/en/master/beginner/quick_start.html), execute the following command to download: ```bash download_dataset("https://obs.dualstack.cn-north-4.myhuaweicloud.com/mindspore-website/notebook/models/checkpoint_lenet.ckpt", ".") diff --git a/docs/serving/docs/source_en/serving_example.md b/docs/serving/docs/source_en/serving_example.md index e1d8ec03eb41ea2cb7634c5a3f3ff966408b663c..a0c559bd2bed11011983e1e18addd158b10802c4 100644 --- a/docs/serving/docs/source_en/serving_example.md +++ b/docs/serving/docs/source_en/serving_example.md @@ -67,7 +67,7 @@ if __name__ == "__main__": ``` To use MindSpore for neural network definition, inherit `mindspore.nn.Cell`. (A `Cell` is a base class of all neural networks.) Define each layer of a neural network in the `__init__` method in advance, and then define the `construct` method to complete the forward construction of the neural network. Use `export` of the `mindspore` module to export the model file. -For more detailed examples, see [Quick Start for Beginners](https://www.mindspore.cn/tutorials/en/master/quick_start.html). +For more detailed examples, see [Quick Start for Beginners](https://www.mindspore.cn/tutorials/en/master/beginner/quick_start.html). Execute the `add_model.py` script to generate the `tensor_add.mindir` file. The input of the model is two 2D tensors with shape [2,2], and the output is the sum of the two input tensors. diff --git a/docs/serving/docs/source_en/serving_multi_subgraphs.md b/docs/serving/docs/source_en/serving_multi_subgraphs.md index 701895669da806814cb2881cff92f8021eb94bde..4848861fbb3e831aeed30aa9d048c31fcec6386a 100644 --- a/docs/serving/docs/source_en/serving_multi_subgraphs.md +++ b/docs/serving/docs/source_en/serving_multi_subgraphs.md @@ -83,7 +83,7 @@ if __name__ == "__main__": ``` To use MindSpore for neural network definition, inherit `mindspore.nn.Cell`. (A `Cell` is a base class of all neural networks.) Define each layer of a neural network in the `__init__` method in advance, and then define the `construct` method to complete the forward construction of the neural network. Use `export` of the `mindspore` module to export the model file. -For more detailed examples, see [Quick Start for Beginners](https://www.mindspore.cn/tutorials/en/master/quick_start.html). +For more detailed examples, see [Quick Start for Beginners](https://www.mindspore.cn/tutorials/en/master/beginner/quick_start.html). Execute the `export_matmul.py` script to generate the `matmul_0.mindir` and `matmul_1.mindir` files. The inputs shapes of these subgraphs are [128,96] and [8,96]. diff --git a/tutorials/source_en/beginner/quick_start.md b/tutorials/source_en/beginner/quick_start.md index 19a5312c14898df32c42b2090a354e8f06ea4776..c52a6d2312484d51a1f0cb441fa260cf8bb2cd31 100644 --- a/tutorials/source_en/beginner/quick_start.md +++ b/tutorials/source_en/beginner/quick_start.md @@ -187,7 +187,7 @@ Loss functions supported by MindSpore include `SoftmaxCrossEntropyWithLogits`, ` net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') ``` -> For more information about using loss functions in mindspore, see [Loss Functions](https://www.mindspore.cn/tutorials/en/master/optimization.html#loss-functions). +> For more information about using loss functions in mindspore, see [Loss Functions](https://www.mindspore.cn/tutorials/en/master/beginner/train.html#loss-functions). MindSpore supports the `Adam`, `AdamWeightDecay`, and `Momentum` optimizers. The following uses the `Momentum` optimizer as an example. @@ -196,7 +196,7 @@ MindSpore supports the `Adam`, `AdamWeightDecay`, and `Momentum` optimizers. The net_opt = nn.Momentum(net.trainable_params(), learning_rate=0.01, momentum=0.9) ``` -> For more information about using an optimizer in mindspore, see [Optimizer](https://www.mindspore.cn/tutorials/en/master/optimization.html#optimizer). +> For more information about using an optimizer in mindspore, see [Optimizer](https://www.mindspore.cn/tutorials/en/master/beginner/train.html#optimizer-functions). ## Training and Saving the Model