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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> |
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| <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> |
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| <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> |
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| <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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| <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> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/backend/index.html">Accelerated Backend Tools</a><ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/mkldnn/mkldnn_quantization.html">Quantize with MKL-DNN backend</a></li> |
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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> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
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| <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> |
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| <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> |
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| <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> |
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| <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> |
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| <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> |
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| |
| <div class="section" id="module-mxnet.executor"> |
| <span id="mxnet-executor"></span><h1>mxnet.executor<a class="headerlink" href="#module-mxnet.executor" title="Permalink to this headline">¶</a></h1> |
| <p>Symbolic Executor component of MXNet.</p> |
| <p><strong>Classes</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.executor.Executor" title="mxnet.executor.Executor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Executor</span></code></a>(handle, symbol, ctx, grad_req, …)</p></td> |
| <td><p>Executor is the object providing efficient symbolic graph execution and optimization.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="class"> |
| <dt id="mxnet.executor.Executor"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.executor.</code><code class="sig-name descname">Executor</code><span class="sig-paren">(</span><em class="sig-param">handle</em>, <em class="sig-param">symbol</em>, <em class="sig-param">ctx</em>, <em class="sig-param">grad_req</em>, <em class="sig-param">group2ctx</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/executor.html#Executor"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.executor.Executor" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
| <p>Executor is the object providing efficient symbolic graph execution and optimization.</p> |
| <p class="rubric">Examples</p> |
| <p><strong>Attributes</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.executor.Executor.arg_dict" title="mxnet.executor.Executor.arg_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">arg_dict</span></code></a></p></td> |
| <td><p>Get dictionary representation of argument arrrays.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.executor.Executor.aux_dict" title="mxnet.executor.Executor.aux_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">aux_dict</span></code></a></p></td> |
| <td><p>Get dictionary representation of auxiliary states arrays.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.executor.Executor.grad_dict" title="mxnet.executor.Executor.grad_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">grad_dict</span></code></a></p></td> |
| <td><p>Get dictionary representation of gradient arrays.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.executor.Executor.output_dict" title="mxnet.executor.Executor.output_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">output_dict</span></code></a></p></td> |
| <td><p>Get dictionary representation of output arrays.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.executor.Executor.backward" title="mxnet.executor.Executor.backward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">backward</span></code></a>([out_grads, is_train])</p></td> |
| <td><p>Do backward pass to get the gradient of arguments.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.executor.Executor.copy_params_from" title="mxnet.executor.Executor.copy_params_from"><code class="xref py py-obj docutils literal notranslate"><span class="pre">copy_params_from</span></code></a>(arg_params[, aux_params, …])</p></td> |
| <td><p>Copy parameters from arg_params, aux_params into executor’s internal array.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.executor.Executor.debug_str" title="mxnet.executor.Executor.debug_str"><code class="xref py py-obj docutils literal notranslate"><span class="pre">debug_str</span></code></a>()</p></td> |
| <td><p>Get a debug string about internal execution plan.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.executor.Executor.forward" title="mxnet.executor.Executor.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>([is_train])</p></td> |
| <td><p>Calculate the outputs specified by the bound symbol.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.executor.Executor.get_optimized_symbol" title="mxnet.executor.Executor.get_optimized_symbol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_optimized_symbol</span></code></a>()</p></td> |
| <td><p>Get an optimized version of the symbol from the executor.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.executor.Executor.reshape" title="mxnet.executor.Executor.reshape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reshape</span></code></a>([partial_shaping, allow_up_sizing])</p></td> |
| <td><p>Return a new executor with the same symbol and shared memory, but different input/output shapes.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.executor.Executor.set_monitor_callback" title="mxnet.executor.Executor.set_monitor_callback"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_monitor_callback</span></code></a>(callback[, monitor_all])</p></td> |
| <td><p>Install callback for monitor.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># typical approach to create an executor is to bind symbol</span> |
| <span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'b'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">c</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span> |
| <span class="gp">>>> </span><span class="n">texec</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">bind</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">(),</span> <span class="p">{</span><span class="s1">'a'</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">]),</span> <span class="s1">'b'</span><span class="p">:</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">])})</span> |
| </pre></div> |
| </div> |
| <dl class="method"> |
| <dt id="mxnet.executor.Executor.arg_dict"> |
| <em class="property">property </em><code class="sig-name descname">arg_dict</code><a class="headerlink" href="#mxnet.executor.Executor.arg_dict" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get dictionary representation of argument arrrays.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p><strong>arg_dict</strong> – The dictionary that maps the names of arguments to NDArrays.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>dict of str to NDArray</p> |
| </dd> |
| </dl> |
| <p>:raises ValueError : if there are duplicated names in the arguments.:</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.executor.Executor.aux_dict"> |
| <em class="property">property </em><code class="sig-name descname">aux_dict</code><a class="headerlink" href="#mxnet.executor.Executor.aux_dict" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get dictionary representation of auxiliary states arrays.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p><strong>aux_dict</strong> – The dictionary that maps name of auxiliary states to NDArrays.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>dict of str to NDArray</p> |
| </dd> |
| </dl> |
| <p>:raises ValueError : if there are duplicated names in the auxiliary states.:</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.executor.Executor.backward"> |
| <code class="sig-name descname">backward</code><span class="sig-paren">(</span><em class="sig-param">out_grads=None</em>, <em class="sig-param">is_train=True</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/executor.html#Executor.backward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.executor.Executor.backward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Do backward pass to get the gradient of arguments.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>out_grads</strong> (<a class="reference internal" href="../../ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>list of NDArray</em><em> or </em><em>dict of str to NDArray</em><em>, </em><em>optional</em>) – Gradient on the outputs to be propagated back. |
| This parameter is only needed when bind is called |
| on outputs that are not a loss function.</p></li> |
| <li><p><strong>is_train</strong> (<em>bool</em><em>, </em><em>default True</em>) – Whether this backward is for training or inference. Note that in rare |
| cases you want to call backward with is_train=False to get gradient |
| during inference.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># Example for binding on loss function symbol, which gives the loss value of the model.</span> |
| <span class="gp">>>> </span><span class="c1"># Equivalently it gives the head gradient for backward pass.</span> |
| <span class="gp">>>> </span><span class="c1"># In this example the built-in SoftmaxOutput is used as loss function.</span> |
| <span class="gp">>>> </span><span class="c1"># MakeLoss can be used to define customized loss function symbol.</span> |
| <span class="gp">>>> </span><span class="n">net</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">net</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">FullyConnected</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'fc'</span><span class="p">,</span> <span class="n">num_hidden</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">net</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s2">"relu"</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">net</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">SoftmaxOutput</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'softmax'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">args</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'data'</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">)),</span> <span class="s1">'fc_weight'</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">)),</span> |
| <span class="gp">>>> </span> <span class="s1">'fc_bias'</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">)),</span> <span class="s1">'softmax_label'</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">))}</span> |
| <span class="gp">>>> </span><span class="n">args_grad</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'fc_weight'</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">)),</span> <span class="s1">'fc_bias'</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">6</span><span class="p">))}</span> |
| <span class="gp">>>> </span><span class="n">texec</span> <span class="o">=</span> <span class="n">net</span><span class="o">.</span><span class="n">bind</span><span class="p">(</span><span class="n">ctx</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">(),</span> <span class="n">args</span><span class="o">=</span><span class="n">args</span><span class="p">,</span> <span class="n">args_grad</span><span class="o">=</span><span class="n">args_grad</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">out</span> <span class="o">=</span> <span class="n">texec</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span><span class="o">=</span><span class="kc">True</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">out</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="go">[[ 0.00378404 0.07600445 0.07600445 0.07600445 0.20660152 0.5616011 ]]</span> |
| <span class="gp">>>> </span><span class="n">texec</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">texec</span><span class="o">.</span><span class="n">grad_arrays</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span> |
| <span class="go">[[ 0.00378404 0.00378404 0.00378404 0.00378404]</span> |
| <span class="go"> [-0.92399555 -0.92399555 -0.92399555 -0.92399555]</span> |
| <span class="go"> [ 0.07600445 0.07600445 0.07600445 0.07600445]</span> |
| <span class="go"> [ 0.07600445 0.07600445 0.07600445 0.07600445]</span> |
| <span class="go"> [ 0.20660152 0.20660152 0.20660152 0.20660152]</span> |
| <span class="go"> [ 0.5616011 0.5616011 0.5616011 0.5616011 ]]</span> |
| <span class="go">>>></span> |
| <span class="gp">>>> </span><span class="c1"># Example for binding on non-loss function symbol.</span> |
| <span class="gp">>>> </span><span class="c1"># Here the binding symbol is neither built-in loss function</span> |
| <span class="gp">>>> </span><span class="c1"># nor customized loss created by MakeLoss.</span> |
| <span class="gp">>>> </span><span class="c1"># As a result the head gradient is not automatically provided.</span> |
| <span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'b'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="c1"># c is not a loss function symbol</span> |
| <span class="gp">>>> </span><span class="n">c</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span> |
| <span class="gp">>>> </span><span class="n">args</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'a'</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">]),</span> <span class="s1">'b'</span><span class="p">:</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">])}</span> |
| <span class="gp">>>> </span><span class="n">args_grad</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'a'</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">)),</span> <span class="s1">'b'</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">))}</span> |
| <span class="gp">>>> </span><span class="n">texec</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">bind</span><span class="p">(</span><span class="n">ctx</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">(),</span> <span class="n">args</span><span class="o">=</span><span class="n">args</span><span class="p">,</span> <span class="n">args_grad</span><span class="o">=</span><span class="n">args_grad</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">out</span> <span class="o">=</span> <span class="n">texec</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span><span class="o">=</span><span class="kc">True</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span> |
| <span class="go">[ 4. 7.]</span> |
| <span class="gp">>>> </span><span class="c1"># out_grads is the head gradient in backward pass.</span> |
| <span class="gp">>>> </span><span class="c1"># Here we define 'c' as loss function.</span> |
| <span class="gp">>>> </span><span class="c1"># Then 'out' is passed as head gradient of backward pass.</span> |
| <span class="gp">>>> </span><span class="n">texec</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">out</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">texec</span><span class="o">.</span><span class="n">grad_arrays</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span> |
| <span class="go">[ 8. 14.]</span> |
| <span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">texec</span><span class="o">.</span><span class="n">grad_arrays</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span> |
| <span class="go">[ 4. 7.]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.executor.Executor.copy_params_from"> |
| <code class="sig-name descname">copy_params_from</code><span class="sig-paren">(</span><em class="sig-param">arg_params</em>, <em class="sig-param">aux_params=None</em>, <em class="sig-param">allow_extra_params=False</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/executor.html#Executor.copy_params_from"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.executor.Executor.copy_params_from" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Copy parameters from arg_params, aux_params into executor’s internal array.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>arg_params</strong> (<em>dict of str to NDArray</em>) – Parameters, dict of name to NDArray of arguments.</p></li> |
| <li><p><strong>aux_params</strong> (<em>dict of str to NDArray</em><em>, </em><em>optional</em>) – Parameters, dict of name to NDArray of auxiliary states.</p></li> |
| <li><p><strong>allow_extra_params</strong> (<em>boolean</em><em>, </em><em>optional</em>) – Whether allow extra parameters that are not needed by symbol. |
| If this is True, no error will be thrown when arg_params or aux_params |
| contain extra parameters that is not needed by the executor.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Raises</dt> |
| <dd class="field-even"><p><strong>ValueError</strong> – If there is additional parameters in the dict but <code class="docutils literal notranslate"><span class="pre">allow_extra_params=False</span></code>.</p> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># set parameters with existing model checkpoint</span> |
| <span class="gp">>>> </span><span class="n">model_prefix</span> <span class="o">=</span> <span class="s1">'mx_mlp'</span> |
| <span class="gp">>>> </span><span class="n">sym</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">load_checkpoint</span><span class="p">(</span><span class="n">model_prefix</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">texec</span><span class="o">.</span><span class="n">copy_params_from</span><span class="p">(</span><span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.executor.Executor.debug_str"> |
| <code class="sig-name descname">debug_str</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/executor.html#Executor.debug_str"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.executor.Executor.debug_str" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get a debug string about internal execution plan.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p><strong>debug_str</strong> – Debug string of the executor.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>string</p> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">c</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span> |
| <span class="gp">>>> </span><span class="n">texec</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">bind</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">(),</span> <span class="p">{</span><span class="s1">'a'</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">]),</span> <span class="s1">'b'</span><span class="p">:</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">])})</span> |
| <span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">texec</span><span class="o">.</span><span class="n">debug_str</span><span class="p">())</span> |
| <span class="go">Symbol Outputs:</span> |
| <span class="go"> output[0]=_plus0(0)</span> |
| <span class="go">Variable:a</span> |
| <span class="go">--------------------</span> |
| <span class="go">Op:_mul_scalar, Name=_mulscalar0</span> |
| <span class="go">Inputs:</span> |
| <span class="go"> arg[0]=a(0) version=0</span> |
| <span class="go">Attrs:</span> |
| <span class="go"> scalar=2</span> |
| <span class="go">--------------------</span> |
| <span class="go">Op:sin, Name=sin0</span> |
| <span class="go">Inputs:</span> |
| <span class="go"> arg[0]=a(0) version=0</span> |
| <span class="go">--------------------</span> |
| <span class="go">Op:elemwise_add, Name=_plus0</span> |
| <span class="go">Inputs:</span> |
| <span class="go"> arg[0]=_mulscalar0(0)</span> |
| <span class="go"> arg[1]=sin0(0)</span> |
| <span class="go">Total 0 MB allocated</span> |
| <span class="go">Total 11 TempSpace resource requested</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.executor.Executor.forward"> |
| <code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">is_train=False</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/executor.html#Executor.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.executor.Executor.forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Calculate the outputs specified by the bound symbol.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>is_train</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether this forward is for evaluation purpose. If True, |
| a backward call is expected to follow.</p></li> |
| <li><p><strong>**kwargs</strong> – Additional specification of input arguments.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># doing forward by specifying data</span> |
| <span class="gp">>>> </span><span class="n">texec</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">mydata</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="c1"># doing forward by not specifying things, but copy to the executor before hand</span> |
| <span class="gp">>>> </span><span class="n">mydata</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">texec</span><span class="o">.</span><span class="n">arg_dict</span><span class="p">[</span><span class="s1">'data'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">texec</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="c1"># doing forward by specifying data and get outputs</span> |
| <span class="gp">>>> </span><span class="n">outputs</span> <span class="o">=</span> <span class="n">texec</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">mydata</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.executor.Executor.get_optimized_symbol"> |
| <code class="sig-name descname">get_optimized_symbol</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/executor.html#Executor.get_optimized_symbol"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.executor.Executor.get_optimized_symbol" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get an optimized version of the symbol from the executor.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p><strong>symbol</strong> – Optimized symbol from the executor.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p><a class="reference internal" href="../../symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.executor.Executor.grad_dict"> |
| <em class="property">property </em><code class="sig-name descname">grad_dict</code><a class="headerlink" href="#mxnet.executor.Executor.grad_dict" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get dictionary representation of gradient arrays.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p><strong>grad_dict</strong> – The dictionary that maps name of arguments to gradient arrays.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>dict of str to NDArray</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.executor.Executor.output_dict"> |
| <em class="property">property </em><code class="sig-name descname">output_dict</code><a class="headerlink" href="#mxnet.executor.Executor.output_dict" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get dictionary representation of output arrays.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p><strong>output_dict</strong> – The dictionary that maps name of output names to NDArrays.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>dict of str to NDArray</p> |
| </dd> |
| </dl> |
| <p>:raises ValueError : if there are duplicated names in the outputs.:</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.executor.Executor.reshape"> |
| <code class="sig-name descname">reshape</code><span class="sig-paren">(</span><em class="sig-param">partial_shaping=False</em>, <em class="sig-param">allow_up_sizing=False</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/executor.html#Executor.reshape"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.executor.Executor.reshape" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return a new executor with the same symbol and shared memory, |
| but different input/output shapes. |
| For runtime reshaping, variable length sequences, etc. |
| The returned executor shares state with the current one, |
| and cannot be used in parallel with it.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>partial_shaping</strong> (<em>bool</em>) – Whether to allow changing the shape of unspecified arguments.</p></li> |
| <li><p><strong>allow_up_sizing</strong> (<em>bool</em>) – Whether to allow allocating new ndarrays that’s larger than the original.</p></li> |
| <li><p><strong>kwargs</strong> (<em>dict of string to tuple of int</em>) – New shape for arguments.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>exec</strong> – A new executor that shares memory with self.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="#mxnet.executor.Executor" title="mxnet.executor.Executor">Executor</a></p> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'a'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'b'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">c</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span> |
| <span class="gp">>>> </span><span class="n">texec</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">bind</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">(),</span> <span class="p">{</span><span class="s1">'a'</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="s1">'b'</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">))})</span> |
| <span class="gp">>>> </span><span class="n">new_shape</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'a'</span><span class="p">:</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="s1">'b'</span><span class="p">:</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">)}</span> |
| <span class="gp">>>> </span><span class="n">texec</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">allow_up_sizing</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">new_shape</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.executor.Executor.set_monitor_callback"> |
| <code class="sig-name descname">set_monitor_callback</code><span class="sig-paren">(</span><em class="sig-param">callback</em>, <em class="sig-param">monitor_all=False</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/executor.html#Executor.set_monitor_callback"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.executor.Executor.set_monitor_callback" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Install callback for monitor.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>callback</strong> (<em>function</em>) – Takes a string and an NDArrayHandle.</p></li> |
| <li><p><strong>monitor_all</strong> (<em>bool</em><em>, </em><em>default False</em>) – If true, monitor both input and output, otherwise monitor output only.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">def</span> <span class="nf">mon_callback</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="gp">>>> </span> <span class="nb">print</span><span class="p">(</span><span class="s2">"Do your stuff here."</span><span class="p">)</span> |
| <span class="go">>>></span> |
| <span class="gp">>>> </span><span class="n">texe</span><span class="o">.</span><span class="n">set_monitor_callback</span><span class="p">(</span><span class="n">mon_callback</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
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