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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Train the neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Use GPUs</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/image-augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/super_resolution.html">Importing an ONNX model into MXNet</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/optimizer/index.html">Optimizers</a></li> |
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| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
| </ul> |
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| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/performance/index.html">Performance</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/compression/index.html">Compression</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/compression/int8.html">Deploy with int-8</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li> |
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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Train the neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Use GPUs</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
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| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/image-augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li> |
| </ul> |
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| <h1>Source code for mxnet.contrib.autograd</h1><div class="highlight"><pre> |
| <span></span><span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span> |
| <span class="c1"># or more contributor license agreements. See the NOTICE file</span> |
| <span class="c1"># distributed with this work for additional information</span> |
| <span class="c1"># regarding copyright ownership. The ASF licenses this file</span> |
| <span class="c1"># to you under the Apache License, Version 2.0 (the</span> |
| <span class="c1"># "License"); you may not use this file except in compliance</span> |
| <span class="c1"># with the License. You may obtain a copy of the License at</span> |
| <span class="c1">#</span> |
| <span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span> |
| <span class="c1">#</span> |
| <span class="c1"># Unless required by applicable law or agreed to in writing,</span> |
| <span class="c1"># software distributed under the License is distributed on an</span> |
| <span class="c1"># "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span> |
| <span class="c1"># KIND, either express or implied. See the License for the</span> |
| <span class="c1"># specific language governing permissions and limitations</span> |
| <span class="c1"># under the License.</span> |
| |
| <span class="c1"># coding: utf-8</span> |
| <span class="sd">"""Autograd for NDArray."""</span> |
| |
| <span class="kn">from</span> <span class="nn">array</span> <span class="kn">import</span> <span class="n">array</span> |
| <span class="kn">import</span> <span class="nn">ctypes</span> |
| <span class="kn">import</span> <span class="nn">functools</span> |
| <span class="kn">from</span> <span class="nn">..base</span> <span class="kn">import</span> <span class="n">_LIB</span><span class="p">,</span> <span class="n">check_call</span><span class="p">,</span> <span class="n">string_types</span> |
| <span class="kn">from</span> <span class="nn">..base</span> <span class="kn">import</span> <span class="n">mx_uint</span><span class="p">,</span> <span class="n">NDArrayHandle</span><span class="p">,</span> <span class="n">c_array</span><span class="p">,</span> <span class="n">c_array_buf</span><span class="p">,</span> <span class="n">c_handle_array</span> |
| <span class="c1"># pylint: disable= unused-import</span> |
| <span class="kn">from</span> <span class="nn">..ndarray</span> <span class="kn">import</span> <span class="n">NDArray</span><span class="p">,</span> <span class="n">zeros_like</span><span class="p">,</span> <span class="n">_GRAD_REQ_MAP</span> |
| |
| |
| <div class="viewcode-block" id="set_is_training"><a class="viewcode-back" href="../../../api/contrib/autograd/index.html#mxnet.contrib.autograd.set_is_training">[docs]</a><span class="k">def</span> <span class="nf">set_is_training</span><span class="p">(</span><span class="n">is_train</span><span class="p">):</span> |
| <span class="sd">"""Set status to training/not training. When training, graph will be constructed</span> |
| <span class="sd"> for gradient computation. Operators will also run with ctx.is_train=True. For example,</span> |
| <span class="sd"> Dropout will drop inputs randomly when is_train=True while simply passing through</span> |
| <span class="sd"> if is_train=False.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> is_train: bool</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> previous state before this set.</span> |
| <span class="sd"> """</span> |
| <span class="n">prev</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXAutogradSetIsTraining</span><span class="p">(</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">is_train</span><span class="p">),</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">prev</span><span class="p">)))</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXAutogradSetIsRecording</span><span class="p">(</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">is_train</span><span class="p">),</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">prev</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="nb">bool</span><span class="p">(</span><span class="n">prev</span><span class="o">.</span><span class="n">value</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="TrainingStateScope"><a class="viewcode-back" href="../../../api/contrib/autograd/index.html#mxnet.contrib.autograd.TrainingStateScope">[docs]</a><span class="k">class</span> <span class="nc">TrainingStateScope</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span> |
| <span class="sd">"""Scope for managing training state.</span> |
| |
| <span class="sd"> Example::</span> |
| <span class="sd"> with TrainingStateScope(True):</span> |
| <span class="sd"> y = model(x)</span> |
| <span class="sd"> compute_gradient([y])</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">enter_state</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_enter_state</span> <span class="o">=</span> <span class="n">enter_state</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_prev</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| <span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_prev</span> <span class="o">=</span> <span class="n">set_is_training</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_enter_state</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ptype</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">trace</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prev</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_enter_state</span><span class="p">:</span> |
| <span class="n">set_is_training</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_prev</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="train_section"><a class="viewcode-back" href="../../../api/contrib/autograd/index.html#mxnet.contrib.autograd.train_section">[docs]</a><span class="k">def</span> <span class="nf">train_section</span><span class="p">():</span> |
| <span class="sd">"""Returns a training scope context to be used in 'with' statement</span> |
| <span class="sd"> and captures training code.</span> |
| |
| <span class="sd"> Example::</span> |
| <span class="sd"> with autograd.train_section():</span> |
| <span class="sd"> y = model(x)</span> |
| <span class="sd"> compute_gradient([y])</span> |
| <span class="sd"> metric.update(...)</span> |
| <span class="sd"> optim.step(...)</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">TrainingStateScope</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="test_section"><a class="viewcode-back" href="../../../api/contrib/autograd/index.html#mxnet.contrib.autograd.test_section">[docs]</a><span class="k">def</span> <span class="nf">test_section</span><span class="p">():</span> |
| <span class="sd">"""Returns a testing scope context to be used in 'with' statement</span> |
| <span class="sd"> and captures testing code.</span> |
| |
| <span class="sd"> Example::</span> |
| <span class="sd"> with autograd.train_section():</span> |
| <span class="sd"> y = model(x)</span> |
| <span class="sd"> compute_gradient([y])</span> |
| <span class="sd"> with autograd.test_section():</span> |
| <span class="sd"> # testing, IO, gradient updates...</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">TrainingStateScope</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="mark_variables"><a class="viewcode-back" href="../../../api/contrib/autograd/index.html#mxnet.contrib.autograd.mark_variables">[docs]</a><span class="k">def</span> <span class="nf">mark_variables</span><span class="p">(</span><span class="n">variables</span><span class="p">,</span> <span class="n">gradients</span><span class="p">,</span> <span class="n">grad_reqs</span><span class="o">=</span><span class="s1">'write'</span><span class="p">):</span> |
| <span class="sd">"""Mark NDArrays as variables to compute gradient for autograd.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> variables: list of NDArray</span> |
| <span class="sd"> gradients: list of NDArray</span> |
| <span class="sd"> grad_reqs: list of string</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">grad_reqs</span><span class="p">,</span> <span class="n">string_types</span><span class="p">):</span> |
| <span class="n">grad_reqs</span> <span class="o">=</span> <span class="p">[</span><span class="n">_GRAD_REQ_MAP</span><span class="p">[</span><span class="n">grad_reqs</span><span class="p">]]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">variables</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">grad_reqs</span> <span class="o">=</span> <span class="p">[</span><span class="n">_GRAD_REQ_MAP</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">grad_reqs</span><span class="p">]</span> |
| |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXAutogradMarkVariables</span><span class="p">(</span> |
| <span class="nb">len</span><span class="p">(</span><span class="n">variables</span><span class="p">),</span> |
| <span class="n">c_handle_array</span><span class="p">(</span><span class="n">variables</span><span class="p">),</span> |
| <span class="n">c_array_buf</span><span class="p">(</span><span class="n">mx_uint</span><span class="p">,</span> <span class="n">array</span><span class="p">(</span><span class="s1">'I'</span><span class="p">,</span> <span class="n">grad_reqs</span><span class="p">)),</span> |
| <span class="n">c_handle_array</span><span class="p">(</span><span class="n">gradients</span><span class="p">)))</span></div> |
| |
| |
| <div class="viewcode-block" id="backward"><a class="viewcode-back" href="../../../api/contrib/autograd/index.html#mxnet.contrib.autograd.backward">[docs]</a><span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">out_grads</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">retain_graph</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="sd">"""Compute the gradients of outputs w.r.t variables.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> outputs: list of NDArray</span> |
| <span class="sd"> out_grads: list of NDArray or None</span> |
| <span class="sd"> """</span> |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)),</span> \ |
| <span class="s2">"outputs must be a list or tuple of NDArrays"</span> |
| |
| <span class="k">if</span> <span class="n">out_grads</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXAutogradBackward</span><span class="p">(</span> |
| <span class="nb">len</span><span class="p">(</span><span class="n">outputs</span><span class="p">),</span> |
| <span class="n">c_handle_array</span><span class="p">(</span><span class="n">outputs</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_void_p</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">retain_graph</span><span class="p">)))</span> |
| <span class="k">return</span> |
| |
| <span class="n">ograd_handles</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">arr</span> <span class="ow">in</span> <span class="n">out_grads</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">arr</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">ograd_handles</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">arr</span><span class="o">.</span><span class="n">handle</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">ograd_handles</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">NDArrayHandle</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">ograd_handles</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">outputs</span><span class="p">),</span> \ |
| <span class="s2">"outputs and out_grads must have the same length"</span> |
| |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXAutogradBackward</span><span class="p">(</span> |
| <span class="nb">len</span><span class="p">(</span><span class="n">outputs</span><span class="p">),</span> |
| <span class="n">c_handle_array</span><span class="p">(</span><span class="n">outputs</span><span class="p">),</span> |
| <span class="n">c_array</span><span class="p">(</span><span class="n">NDArrayHandle</span><span class="p">,</span> <span class="n">ograd_handles</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">retain_graph</span><span class="p">)))</span></div> |
| |
| |
| <div class="viewcode-block" id="compute_gradient"><a class="viewcode-back" href="../../../api/contrib/autograd/index.html#mxnet.contrib.autograd.compute_gradient">[docs]</a><span class="k">def</span> <span class="nf">compute_gradient</span><span class="p">(</span><span class="n">outputs</span><span class="p">):</span> |
| <span class="sd">"""Deprecated. Please use backward"""</span> |
| <span class="n">backward</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="grad_and_loss"><a class="viewcode-back" href="../../../api/contrib/autograd/index.html#mxnet.contrib.autograd.grad_and_loss">[docs]</a><span class="k">def</span> <span class="nf">grad_and_loss</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">argnum</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="sd">"""Return function that computes both gradient of arguments and loss value.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> func: a python function</span> |
| <span class="sd"> The forward (loss) function.</span> |
| <span class="sd"> argnum: an int or a list of int</span> |
| <span class="sd"> The index of argument to calculate gradient for.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> grad_and_loss_func: a python function</span> |
| <span class="sd"> A function that would compute both the gradient of arguments and loss value.</span> |
| <span class="sd"> """</span> |
| <span class="nd">@functools</span><span class="o">.</span><span class="n">wraps</span><span class="p">(</span><span class="n">func</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">wrapped</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">):</span> |
| <span class="sd">"""Wrapped function."""</span> |
| <span class="n">variables</span> <span class="o">=</span> <span class="n">args</span> |
| <span class="k">if</span> <span class="n">argnum</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">argnum_</span> <span class="o">=</span> <span class="n">argnum</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">argnum</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="k">else</span> <span class="p">[</span><span class="n">argnum</span><span class="p">]</span> |
| <span class="n">variables</span> <span class="o">=</span> <span class="p">[</span><span class="n">args</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">argnum_</span><span class="p">]</span> |
| <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">variables</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">),</span> <span class="s2">"type of autograd input should NDArray."</span> |
| <span class="n">grads</span> <span class="o">=</span> <span class="p">[</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">variables</span><span class="p">]</span> |
| <span class="n">mark_variables</span><span class="p">(</span><span class="n">variables</span><span class="p">,</span> <span class="n">grads</span><span class="p">)</span> |
| <span class="k">with</span> <span class="n">train_section</span><span class="p">():</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span> |
| <span class="n">compute_gradient</span><span class="p">([</span><span class="n">outputs</span><span class="p">]</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">)</span> <span class="k">else</span> <span class="n">outputs</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">grads</span><span class="p">,</span> <span class="n">outputs</span> |
| <span class="k">return</span> <span class="n">wrapped</span></div> |
| |
| <div class="viewcode-block" id="grad"><a class="viewcode-back" href="../../../api/contrib/autograd/index.html#mxnet.contrib.autograd.grad">[docs]</a><span class="k">def</span> <span class="nf">grad</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">argnum</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="sd">"""Return function that computes gradient of arguments.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> func: a python function</span> |
| <span class="sd"> The forward (loss) function.</span> |
| <span class="sd"> argnum: an int or a list of int</span> |
| <span class="sd"> The index of argument to calculate gradient for.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> grad_func: a python function</span> |
| <span class="sd"> A function that would compute the gradient of arguments.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> # autograd supports dynamic graph which is changed</span> |
| <span class="sd"> >>> # every instance</span> |
| <span class="sd"> >>> def func(x):</span> |
| <span class="sd"> >>> r = random.randint(0, 1)</span> |
| <span class="sd"> >>> if r % 2:</span> |
| <span class="sd"> >>> return x**2</span> |
| <span class="sd"> >>> else:</span> |
| <span class="sd"> >>> return x/3</span> |
| <span class="sd"> >>> # use `grad(func)` to get the gradient function</span> |
| <span class="sd"> >>> for x in range(10):</span> |
| <span class="sd"> >>> grad_func = grad(func)</span> |
| <span class="sd"> >>> inputs = nd.array([[1, 2, 3], [4, 5, 6]])</span> |
| <span class="sd"> >>> grad_vals = grad_func(inputs)</span> |
| <span class="sd"> """</span> |
| <span class="n">grad_with_loss_func</span> <span class="o">=</span> <span class="n">grad_and_loss</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">argnum</span><span class="p">)</span> |
| <span class="nd">@functools</span><span class="o">.</span><span class="n">wraps</span><span class="p">(</span><span class="n">grad_with_loss_func</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">wrapped</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">grad_with_loss_func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="k">return</span> <span class="n">wrapped</span></div> |
| </pre></div> |
| |
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