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<span class="mdl-layout-title toc">Table Of Contents</span>
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<ul>
<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>
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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>
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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>
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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>
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<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>
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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>
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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>
<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>
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<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>
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<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/optimizer/index.html">Optimizers</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/viz/index.html">Visualization</a><ul>
<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li>
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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>
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<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/mkldnn/index.html">Intel MKL-DNN</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/mkldnn/mkldnn_quantization.html">Quantize with MKL-DNN backend</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/mkldnn/mkldnn_quantization.html#Improving-accuracy-with-Intel®-Neural-Compressor">Improving accuracy with Intel® Neural Compressor</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/mkldnn/mkldnn_readme.html">Install MXNet with MKL-DNN</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/tensorrt/index.html">TensorRT</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/tensorrt/tensorrt.html">Optimizing Deep Learning Computation Graphs with TensorRT</a></li>
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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>
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<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-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>
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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>
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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>
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<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>
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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>
<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>
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</ul>
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<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>
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<h1>Source code for mxnet.gluon.parameter</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"># &quot;License&quot;); 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"># &quot;AS IS&quot; 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="c1"># pylint: disable=unnecessary-pass, too-many-lines</span>
<span class="sd">&quot;&quot;&quot;Neural network parameter.&quot;&quot;&quot;</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;DeferredInitializationError&#39;</span><span class="p">,</span> <span class="s1">&#39;Parameter&#39;</span><span class="p">,</span> <span class="s1">&#39;Constant&#39;</span><span class="p">,</span>
<span class="s1">&#39;ParameterDict&#39;</span><span class="p">,</span> <span class="s1">&#39;tensor_types&#39;</span><span class="p">]</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</span><span class="p">,</span> <span class="n">defaultdict</span>
<span class="kn">import</span> <span class="nn">warnings</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">..base</span> <span class="kn">import</span> <span class="n">mx_real_t</span><span class="p">,</span> <span class="n">MXNetError</span>
<span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">symbol</span><span class="p">,</span> <span class="n">ndarray</span><span class="p">,</span> <span class="n">initializer</span><span class="p">,</span> <span class="n">context</span>
<span class="kn">from</span> <span class="nn">..context</span> <span class="kn">import</span> <span class="n">Context</span><span class="p">,</span> <span class="n">cpu</span>
<span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">autograd</span>
<span class="kn">from</span> <span class="nn">.utils</span> <span class="kn">import</span> <span class="n">_indent</span><span class="p">,</span> <span class="n">_brief_print_list</span><span class="p">,</span> <span class="n">shape_is_known</span>
<span class="kn">from</span> <span class="nn">..util</span> <span class="kn">import</span> <span class="n">is_np_shape</span><span class="p">,</span> <span class="n">is_np_array</span>
<span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">numpy</span> <span class="k">as</span> <span class="n">_mx_np</span> <span class="c1"># pylint: disable=reimported</span>
<span class="c1"># pylint: disable= invalid-name</span>
<span class="n">tensor_types</span> <span class="o">=</span> <span class="p">(</span><span class="n">symbol</span><span class="o">.</span><span class="n">Symbol</span><span class="p">,</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">NDArray</span><span class="p">)</span>
<span class="c1"># pylint: enable= invalid-name</span>
<span class="k">class</span> <span class="nc">DeferredInitializationError</span><span class="p">(</span><span class="n">MXNetError</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Error for unfinished deferred initialization.&quot;&quot;&quot;</span>
<span class="k">pass</span>
<div class="viewcode-block" id="Parameter"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter">[docs]</a><span class="k">class</span> <span class="nc">Parameter</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;A Container holding parameters (weights) of Blocks.</span>
<span class="sd"> :py:class:`Parameter` holds a copy of the parameter on each :py:class:`Context` after</span>
<span class="sd"> it is initialized with ``Parameter.initialize(...)``. If :py:attr:`grad_req` is</span>
<span class="sd"> not ``&#39;null&#39;``, it will also hold a gradient array on each :py:class:`Context`::</span>
<span class="sd"> ctx = mx.gpu(0)</span>
<span class="sd"> x = mx.nd.zeros((16, 100), ctx=ctx)</span>
<span class="sd"> w = mx.gluon.Parameter(&#39;fc_weight&#39;, shape=(64, 100), init=mx.init.Xavier())</span>
<span class="sd"> b = mx.gluon.Parameter(&#39;fc_bias&#39;, shape=(64,), init=mx.init.Zero())</span>
<span class="sd"> w.initialize(ctx=ctx)</span>
<span class="sd"> b.initialize(ctx=ctx)</span>
<span class="sd"> out = mx.nd.FullyConnected(x, w.data(ctx), b.data(ctx), num_hidden=64)</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> name : str</span>
<span class="sd"> Name of this parameter.</span>
<span class="sd"> grad_req : {&#39;write&#39;, &#39;add&#39;, &#39;null&#39;}, default &#39;write&#39;</span>
<span class="sd"> Specifies how to update gradient to grad arrays.</span>
<span class="sd"> - ``&#39;write&#39;`` means everytime gradient is written to grad :py:class:`NDArray`.</span>
<span class="sd"> - ``&#39;add&#39;`` means everytime gradient is added to the grad :py:class:`NDArray`. You need</span>
<span class="sd"> to manually call ``zero_grad()`` to clear the gradient buffer before each</span>
<span class="sd"> iteration when using this option.</span>
<span class="sd"> - &#39;null&#39; means gradient is not requested for this parameter. gradient arrays</span>
<span class="sd"> will not be allocated.</span>
<span class="sd"> shape : int or tuple of int, default None</span>
<span class="sd"> Shape of this parameter. By default shape is not specified. Parameter with</span>
<span class="sd"> unknown shape can be used for :py:class:`Symbol` API, but ``init`` will throw an error</span>
<span class="sd"> when using :py:class:`NDArray` API.</span>
<span class="sd"> dtype : numpy.dtype or str, default &#39;float32&#39;</span>
<span class="sd"> Data type of this parameter. For example, ``numpy.float32`` or ``&#39;float32&#39;``.</span>
<span class="sd"> lr_mult : float, default 1.0</span>
<span class="sd"> Learning rate multiplier. Learning rate will be multiplied by lr_mult</span>
<span class="sd"> when updating this parameter with optimizer.</span>
<span class="sd"> wd_mult : float, default 1.0</span>
<span class="sd"> Weight decay multiplier (L2 regularizer coefficient). Works similar to lr_mult.</span>
<span class="sd"> init : Initializer, default None</span>
<span class="sd"> Initializer of this parameter. Will use the global initializer by default.</span>
<span class="sd"> stype: {&#39;default&#39;, &#39;row_sparse&#39;, &#39;csr&#39;}, defaults to &#39;default&#39;.</span>
<span class="sd"> The storage type of the parameter.</span>
<span class="sd"> grad_stype: {&#39;default&#39;, &#39;row_sparse&#39;, &#39;csr&#39;}, defaults to &#39;default&#39;.</span>
<span class="sd"> The storage type of the parameter&#39;s gradient.</span>
<span class="sd"> Attributes</span>
<span class="sd"> ----------</span>
<span class="sd"> grad_req : {&#39;write&#39;, &#39;add&#39;, &#39;null&#39;}</span>
<span class="sd"> This can be set before or after initialization. Setting ``grad_req`` to ``&#39;null&#39;``</span>
<span class="sd"> with ``x.grad_req = &#39;null&#39;`` saves memory and computation when you don&#39;t</span>
<span class="sd"> need gradient w.r.t x.</span>
<span class="sd"> lr_mult : float</span>
<span class="sd"> Local learning rate multiplier for this Parameter. The actual learning rate</span>
<span class="sd"> is calculated with ``learning_rate * lr_mult``. You can set it with</span>
<span class="sd"> ``param.lr_mult = 2.0``</span>
<span class="sd"> wd_mult : float</span>
<span class="sd"> Local weight decay multiplier for this Parameter.</span>
<span class="sd"> &quot;&quot;&quot;</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">name</span><span class="p">,</span> <span class="n">grad_req</span><span class="o">=</span><span class="s1">&#39;write&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">mx_real_t</span><span class="p">,</span>
<span class="n">lr_mult</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">wd_mult</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">init</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">differentiable</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">stype</span><span class="o">=</span><span class="s1">&#39;default&#39;</span><span class="p">,</span> <span class="n">grad_stype</span><span class="o">=</span><span class="s1">&#39;default&#39;</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_var</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_grad</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_ctx_list</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_ctx_map</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_trainer</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span> <span class="o">=</span> <span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_differentiable</span> <span class="o">=</span> <span class="n">differentiable</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_allow_deferred_init</span> <span class="o">=</span> <span class="n">allow_deferred_init</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_grad_req</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
<span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">shape</span><span class="p">,)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_shape</span> <span class="o">=</span> <span class="n">shape</span>
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">name</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_dtype</span> <span class="o">=</span> <span class="n">dtype</span>
<span class="bp">self</span><span class="o">.</span><span class="n">lr_mult</span> <span class="o">=</span> <span class="n">lr_mult</span>
<span class="bp">self</span><span class="o">.</span><span class="n">wd_mult</span> <span class="o">=</span> <span class="n">wd_mult</span>
<span class="bp">self</span><span class="o">.</span><span class="n">grad_req</span> <span class="o">=</span> <span class="n">grad_req</span>
<span class="bp">self</span><span class="o">.</span><span class="n">init</span> <span class="o">=</span> <span class="n">init</span>
<span class="c1"># sparse related storage type information</span>
<span class="n">valid_stypes</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;default&#39;</span><span class="p">,</span> <span class="s1">&#39;row_sparse&#39;</span><span class="p">,</span> <span class="s1">&#39;csr&#39;</span><span class="p">]</span>
<span class="k">assert</span> <span class="n">grad_stype</span> <span class="ow">in</span> <span class="n">valid_stypes</span><span class="p">,</span> <span class="s2">&quot;grad_stype for Parameter &#39;</span><span class="si">%s</span><span class="s2">&#39; must be &quot;</span> \
<span class="s2">&quot;one of &#39;default&#39;, &#39;row_sparse&#39;, or &#39;csr&#39;, but got &#39;</span><span class="si">%s</span><span class="s2">&#39;&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">grad_stype</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">stype</span> <span class="ow">in</span> <span class="n">valid_stypes</span><span class="p">,</span> <span class="s2">&quot;stype for Parameter &#39;</span><span class="si">%s</span><span class="s2">&#39; must be &quot;</span> \
<span class="s2">&quot;one of &#39;default&#39;, &#39;row_sparse&#39;, or &#39;csr&#39;, but got &#39;</span><span class="si">%s</span><span class="s2">&#39;&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">stype</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_grad_stype</span> <span class="o">=</span> <span class="n">grad_stype</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_stype</span> <span class="o">=</span> <span class="n">stype</span>
<span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;Parameter </span><span class="si">{name}</span><span class="s1"> (shape=</span><span class="si">{shape}</span><span class="s1">, dtype=</span><span class="si">{dtype}</span><span class="s1">)&#39;</span>
<span class="k">return</span> <span class="n">s</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">grad_req</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad_req</span>
<span class="nd">@grad_req</span><span class="o">.</span><span class="n">setter</span>
<span class="k">def</span> <span class="nf">grad_req</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">req</span><span class="p">):</span>
<span class="k">assert</span> <span class="n">req</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;write&#39;</span><span class="p">,</span> <span class="s1">&#39;add&#39;</span><span class="p">,</span> <span class="s1">&#39;null&#39;</span><span class="p">],</span> \
<span class="s2">&quot;grad_req must be one of &#39;write&#39;, &#39;add&#39;, or &#39;null&#39;, but got &#39;</span><span class="si">%s</span><span class="s2">&#39;&quot;</span><span class="o">%</span><span class="n">req</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_differentiable</span><span class="p">:</span>
<span class="n">req</span> <span class="o">=</span> <span class="s1">&#39;null&#39;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad_req</span> <span class="o">==</span> <span class="n">req</span><span class="p">:</span>
<span class="k">return</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_grad_req</span> <span class="o">=</span> <span class="n">req</span>
<span class="k">if</span> <span class="n">req</span> <span class="o">==</span> <span class="s1">&#39;null&#39;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_grad</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">]</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_init_grad</span><span class="p">()</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">dtype</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;The type of the parameter.</span>
<span class="sd"> Setting the dtype value is equivalent to casting the value of the parameter</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dtype</span>
<span class="nd">@dtype</span><span class="o">.</span><span class="n">setter</span>
<span class="k">def</span> <span class="nf">dtype</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dtype</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">shape</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;The shape of the parameter.</span>
<span class="sd"> By default, an unknown dimension size is 0. However, when the NumPy semantic</span>
<span class="sd"> is turned on, unknown dimension size is -1.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shape</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="kc">None</span>
<span class="k">elif</span> <span class="n">is_np_shape</span><span class="p">():</span>
<span class="c1"># Parameters shouldn&#39;t be zero-size. If one of its dimension is 0,</span>
<span class="c1"># it means the parameter isn&#39;t initialized. In the NumPy semantics,</span>
<span class="c1"># the unknown dimension should be marked with -1.</span>
<span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">i</span> <span class="k">if</span> <span class="n">i</span> <span class="o">!=</span> <span class="mi">0</span> <span class="k">else</span> <span class="o">-</span><span class="mi">1</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shape</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shape</span>
<span class="nd">@shape</span><span class="o">.</span><span class="n">setter</span>
<span class="k">def</span> <span class="nf">shape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">new_shape</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shape</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_shape</span> <span class="o">=</span> <span class="n">new_shape</span>
<span class="k">return</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_shape</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">new_shape</span><span class="p">)</span> <span class="ow">and</span> \
<span class="nb">all</span><span class="p">(</span><span class="n">j</span> <span class="ow">in</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</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="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">new_shape</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shape</span><span class="p">)),</span> \
<span class="s2">&quot;Expected shape </span><span class="si">%s</span><span class="s2"> is incompatible with given shape </span><span class="si">%s</span><span class="s2">.&quot;</span><span class="o">%</span><span class="p">(</span>
<span class="nb">str</span><span class="p">(</span><span class="n">new_shape</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_shape</span><span class="p">))</span> <span class="c1"># -1 means unknown dim size in np_shape mode</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_shape</span> <span class="o">=</span> <span class="n">new_shape</span>
<span class="k">def</span> <span class="nf">_set_trainer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">trainer</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot; Set the trainer this parameter is associated with. &quot;&quot;&quot;</span>
<span class="c1"># trainer cannot be replaced for sparse params</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span> <span class="o">!=</span> <span class="s1">&#39;default&#39;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trainer</span> <span class="ow">and</span> <span class="n">trainer</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trainer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">trainer</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
<span class="s2">&quot;Failed to set the trainer for Parameter &#39;</span><span class="si">%s</span><span class="s2">&#39; because it was already set. &quot;</span> \
<span class="s2">&quot;More than one trainers for a </span><span class="si">%s</span><span class="s2"> Parameter is not supported.&quot;</span> \
<span class="o">%</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_trainer</span> <span class="o">=</span> <span class="n">trainer</span>
<span class="k">def</span> <span class="nf">_check_and_get</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">arr_list</span><span class="p">,</span> <span class="n">ctx</span><span class="p">):</span>
<span class="k">if</span> <span class="n">arr_list</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">if</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="nb">list</span><span class="p">:</span>
<span class="k">return</span> <span class="n">arr_list</span>
<span class="k">if</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">arr_list</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="k">return</span> <span class="n">arr_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">ctx</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">current_context</span><span class="p">()</span>
<span class="n">ctx_list</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ctx_map</span><span class="p">[</span><span class="n">ctx</span><span class="o">.</span><span class="n">device_typeid</span><span class="o">&amp;</span><span class="mi">1</span><span class="p">]</span>
<span class="k">if</span> <span class="n">ctx</span><span class="o">.</span><span class="n">device_id</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">ctx_list</span><span class="p">):</span>
<span class="n">idx</span> <span class="o">=</span> <span class="n">ctx_list</span><span class="p">[</span><span class="n">ctx</span><span class="o">.</span><span class="n">device_id</span><span class="p">]</span>
<span class="k">if</span> <span class="n">idx</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="n">arr_list</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
<span class="s2">&quot;Parameter &#39;</span><span class="si">%s</span><span class="s2">&#39; was not initialized on context </span><span class="si">%s</span><span class="s2">. &quot;</span>
<span class="s2">&quot;It was only initialized on </span><span class="si">%s</span><span class="s2">.&quot;</span><span class="o">%</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">ctx</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_ctx_list</span><span class="p">)))</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span><span class="p">:</span>
<span class="k">raise</span> <span class="n">DeferredInitializationError</span><span class="p">(</span>
<span class="s2">&quot;Parameter &#39;</span><span class="si">%s</span><span class="s2">&#39; has not been initialized yet because initialization was &quot;</span> \
<span class="s2">&quot;deferred. Actual initialization happens during the first forward pass. &quot;</span> \
<span class="s2">&quot;Please pass one batch of data through the network before accessing Parameters. &quot;</span> \
<span class="s2">&quot;You can also avoid deferred initialization by specifying in_units, &quot;</span> \
<span class="s2">&quot;num_features, etc., for network layers.&quot;</span><span class="o">%</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">))</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
<span class="s2">&quot;Parameter &#39;</span><span class="si">%s</span><span class="s2">&#39; has not been initialized. Note that &quot;</span> \
<span class="s2">&quot;you should initialize parameters and create Trainer &quot;</span> \
<span class="s2">&quot;with Block.collect_params() instead of Block.params &quot;</span> \
<span class="s2">&quot;because the later does not include Parameters of &quot;</span> \
<span class="s2">&quot;nested child Blocks&quot;</span><span class="o">%</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">_get_row_sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">arr_list</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">row_id</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot; Get row_sparse data from row_sparse parameters based on row_id. &quot;&quot;&quot;</span>
<span class="c1"># get row sparse params based on row ids</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">row_id</span><span class="p">,</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;row_id must have NDArray type, but </span><span class="si">%s</span><span class="s2"> is given&quot;</span><span class="o">%</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">row_id</span><span class="p">)))</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trainer</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Cannot get row_sparse data for Parameter &#39;</span><span class="si">%s</span><span class="s2">&#39; when no &quot;</span> \
<span class="s2">&quot;Trainer is created with it.&quot;</span><span class="o">%</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
<span class="n">results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_and_get</span><span class="p">(</span><span class="n">arr_list</span><span class="p">,</span> <span class="n">ctx</span><span class="p">)</span>
<span class="c1"># fetch row sparse params from the trainer</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_trainer</span><span class="o">.</span><span class="n">_row_sparse_pull</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">results</span><span class="p">,</span> <span class="n">row_id</span><span class="p">)</span>
<span class="k">return</span> <span class="n">results</span>
<span class="k">def</span> <span class="nf">_load_init</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">cast_dtype</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dtype_source</span><span class="o">=</span><span class="s1">&#39;current&#39;</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> (Re)initializes by loading from data.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> data : NDArray</span>
<span class="sd"> The data to load</span>
<span class="sd"> ctx : Context or list of Context</span>
<span class="sd"> Context(s) initialize loaded parameters on.</span>
<span class="sd"> cast_dtype : bool, default False</span>
<span class="sd"> Cast the data type of the parameter</span>
<span class="sd"> dtype_source : str, default &#39;current&#39;</span>
<span class="sd"> must be in {&#39;current&#39;, &#39;saved&#39;}</span>
<span class="sd"> Only valid if cast_dtype=True, specify the source of the dtype for casting</span>
<span class="sd"> the parameters</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">cast_dtype</span><span class="p">:</span>
<span class="k">assert</span> <span class="n">dtype_source</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;current&#39;</span><span class="p">,</span> <span class="s1">&#39;saved&#39;</span><span class="p">]</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span>
<span class="n">unknown_dim_size</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> <span class="k">if</span> <span class="n">is_np_shape</span><span class="p">()</span> <span class="k">else</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">self_dim</span><span class="p">,</span> <span class="n">data_dim</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">):</span>
<span class="k">assert</span> <span class="n">self_dim</span> <span class="ow">in</span> <span class="p">(</span><span class="n">unknown_dim_size</span><span class="p">,</span> <span class="n">data_dim</span><span class="p">),</span> \
<span class="s2">&quot;Failed loading Parameter &#39;</span><span class="si">%s</span><span class="s2">&#39; from saved params: &quot;</span> \
<span class="s2">&quot;shape incompatible expected </span><span class="si">%s</span><span class="s2"> vs saved </span><span class="si">%s</span><span class="s2">&quot;</span><span class="o">%</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">i</span> <span class="k">if</span> <span class="n">i</span> <span class="o">!=</span> <span class="n">unknown_dim_size</span> <span class="k">else</span> <span class="n">j</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">:</span>
<span class="k">if</span> <span class="n">cast_dtype</span> <span class="ow">and</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">type</span> <span class="o">!=</span> <span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="p">:</span>
<span class="k">if</span> <span class="n">dtype_source</span> <span class="o">==</span> <span class="s1">&#39;current&#39;</span><span class="p">:</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">dtype_source</span> <span class="o">==</span> <span class="s1">&#39;saved&#39;</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">dtype</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">([(</span><span class="s1">&#39;bfloat16&#39;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">uint16</span><span class="p">)]):</span>
<span class="k">assert</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> <span class="o">==</span> <span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> \
<span class="s2">&quot;Failed loading Parameter &#39;</span><span class="si">%s</span><span class="s2">&#39; from saved params: &quot;</span> \
<span class="s2">&quot;dtype incompatible expected </span><span class="si">%s</span><span class="s2"> vs saved </span><span class="si">%s</span><span class="s2">. &quot;</span> \
<span class="s2">&quot;Set cast_dtype=True to cast the dtype of saved params.&quot;</span><span class="o">%</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">assert</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> \
<span class="s2">&quot;Failed loading Parameter &#39;</span><span class="si">%s</span><span class="s2">&#39; from saved params: &quot;</span> \
<span class="s2">&quot;dtype incompatible expected </span><span class="si">%s</span><span class="s2"> vs saved </span><span class="si">%s</span><span class="s2">. &quot;</span> \
<span class="s2">&quot;Set cast_dtype=True to cast the dtype of saved params.&quot;</span><span class="o">%</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="p">))</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span> <span class="o">!=</span> <span class="n">data</span><span class="o">.</span><span class="n">stype</span><span class="p">:</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">tostype</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_stype</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">Context</span><span class="p">):</span>
<span class="n">ctx</span> <span class="o">=</span> <span class="p">[</span><span class="n">ctx</span><span class="p">]</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span><span class="p">:</span>
<span class="k">assert</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="nb">set</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span> <span class="o">==</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> \
<span class="s2">&quot;Failed to load Parameter &#39;</span><span class="si">%s</span><span class="s2">&#39; on </span><span class="si">%s</span><span class="s2"> because it was &quot;</span> \
<span class="s2">&quot;previous initialized on </span><span class="si">%s</span><span class="s2">.&quot;</span><span class="o">%</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">ctx</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">list_ctx</span><span class="p">()))</span>
<span class="n">ctx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="k">elif</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">ctx</span> <span class="o">=</span> <span class="p">[</span><span class="n">cpu</span><span class="p">()]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_init_impl</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">ctx</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">assert</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="nb">set</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span> <span class="o">==</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">list_ctx</span><span class="p">()),</span> \
<span class="s2">&quot;Failed to load Parameter &#39;</span><span class="si">%s</span><span class="s2">&#39; on </span><span class="si">%s</span><span class="s2"> because it was &quot;</span> \
<span class="s2">&quot;previous initialized on </span><span class="si">%s</span><span class="s2">.&quot;</span><span class="o">%</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">ctx</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">list_ctx</span><span class="p">()))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">set_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span> <span class="o">=</span> <span class="p">()</span>
<span class="k">def</span> <span class="nf">_finish_deferred_init</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Finishes deferred initialization.&quot;&quot;&quot;</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span><span class="p">:</span>
<span class="k">return</span>
<span class="n">init</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">default_init</span><span class="p">,</span> <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span> <span class="o">=</span> <span class="p">()</span>
<span class="k">assert</span> <span class="n">shape_is_known</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> \
<span class="s2">&quot;Cannot initialize Parameter &#39;</span><span class="si">%s</span><span class="s2">&#39; because it has &quot;</span> \
<span class="s2">&quot;invalid shape: </span><span class="si">%s</span><span class="s2">. Please specify in_units, &quot;</span> \
<span class="s2">&quot;in_channels, etc for `Block`s.&quot;</span><span class="o">%</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
<span class="k">with</span> <span class="n">autograd</span><span class="o">.</span><span class="n">pause</span><span class="p">():</span>
<span class="k">if</span> <span class="n">data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;shape&#39;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="s1">&#39;dtype&#39;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="s1">&#39;ctx&#39;</span><span class="p">:</span> <span class="n">context</span><span class="o">.</span><span class="n">cpu</span><span class="p">()}</span>
<span class="k">if</span> <span class="n">is_np_array</span><span class="p">():</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span> <span class="o">!=</span> <span class="s1">&#39;default&#39;</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;mxnet.numpy.zeros does not support stype = </span><span class="si">{}</span><span class="s2">&quot;</span>
<span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_stype</span><span class="p">))</span>
<span class="n">zeros_fn</span> <span class="o">=</span> <span class="n">_mx_np</span><span class="o">.</span><span class="n">zeros</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;stype&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span>
<span class="n">zeros_fn</span> <span class="o">=</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">zeros</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">zeros_fn</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="n">initializer</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">default_init</span><span class="p">)(</span>
<span class="n">initializer</span><span class="o">.</span><span class="n">InitDesc</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="p">{</span><span class="s1">&#39;__init__&#39;</span><span class="p">:</span> <span class="n">init</span><span class="p">}),</span> <span class="n">data</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_init_impl</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">ctx</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_init_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">ctx_list</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Sets data and grad.&quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_ctx_list</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">ctx_list</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_ctx_map</span> <span class="o">=</span> <span class="p">[[],</span> <span class="p">[]]</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">ctx</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_ctx_list</span><span class="p">):</span>
<span class="n">dev_list</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ctx_map</span><span class="p">[</span><span class="n">ctx</span><span class="o">.</span><span class="n">device_typeid</span><span class="o">&amp;</span><span class="mi">1</span><span class="p">]</span>
<span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">dev_list</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">device_id</span><span class="p">:</span>
<span class="n">dev_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
<span class="n">dev_list</span><span class="p">[</span><span class="n">ctx</span><span class="o">.</span><span class="n">device_id</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="o">=</span> <span class="p">[</span><span class="n">data</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span> <span class="k">for</span> <span class="n">ctx</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ctx_list</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_init_grad</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">_init_grad</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Initialize grad buffers.&quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_req</span> <span class="o">==</span> <span class="s1">&#39;null&#39;</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_grad</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">return</span>
<span class="k">if</span> <span class="n">is_np_array</span><span class="p">():</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad_stype</span> <span class="o">!=</span> <span class="s1">&#39;default&#39;</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;mxnet.numpy.zeros does not support stype = </span><span class="si">{}</span><span class="s2">&quot;</span>
<span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_grad_stype</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_grad</span> <span class="o">=</span> <span class="p">[</span><span class="n">_mx_np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">ctx</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_grad</span> <span class="o">=</span> <span class="p">[</span><span class="n">ndarray</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">ctx</span><span class="p">,</span>
<span class="n">stype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_grad_stype</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">]</span>
<span class="n">autograd</span><span class="o">.</span><span class="n">mark_variables</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_check_and_get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">,</span> <span class="nb">list</span><span class="p">),</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_grad</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_req</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_reduce</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Reduce data from multiple context to cpu.&quot;&quot;&quot;</span>
<span class="n">ctx</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span> <span class="o">==</span> <span class="s1">&#39;default&#39;</span><span class="p">:</span>
<span class="n">block</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">list_data</span><span class="p">()</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">block</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
<span class="k">if</span> <span class="n">is_np_array</span><span class="p">():</span>
<span class="n">data</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([</span><span class="n">w</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">block</span><span class="p">])</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">block</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">add_n</span><span class="p">(</span><span class="o">*</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">block</span><span class="p">))</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">block</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">()</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># fetch all rows for &#39;row_sparse&#39; param</span>
<span class="n">all_row_ids</span> <span class="o">=</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;int64&#39;</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">)</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">stype</span><span class="o">=</span><span class="s1">&#39;row_sparse&#39;</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_trainer</span><span class="o">.</span><span class="n">_row_sparse_pull</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">all_row_ids</span><span class="p">,</span> <span class="n">full_idx</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">return</span> <span class="n">data</span>
<div class="viewcode-block" id="Parameter.initialize"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter.initialize">[docs]</a> <span class="k">def</span> <span class="nf">initialize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">init</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">default_init</span><span class="o">=</span><span class="n">initializer</span><span class="o">.</span><span class="n">Uniform</span><span class="p">(),</span>
<span class="n">force_reinit</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Initializes parameter and gradient arrays. Only used for :py:class:`NDArray` API.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> init : Initializer</span>
<span class="sd"> The initializer to use. Overrides :py:meth:`Parameter.init` and default_init.</span>
<span class="sd"> ctx : Context or list of Context, defaults to :py:meth:`context.current_context()`.</span>
<span class="sd"> Initialize Parameter on given context. If ctx is a list of Context, a</span>
<span class="sd"> copy will be made for each context.</span>
<span class="sd"> .. note::</span>
<span class="sd"> Copies are independent arrays. User is responsible for keeping</span>
<span class="sd"> their values consistent when updating.</span>
<span class="sd"> Normally :py:class:`gluon.Trainer` does this for you.</span>
<span class="sd"> default_init : Initializer</span>
<span class="sd"> Default initializer is used when both :py:func:`init`</span>
<span class="sd"> and :py:meth:`Parameter.init` are ``None``.</span>
<span class="sd"> force_reinit : bool, default False</span>
<span class="sd"> Whether to force re-initialization if parameter is already initialized.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; weight = mx.gluon.Parameter(&#39;weight&#39;, shape=(2, 2))</span>
<span class="sd"> &gt;&gt;&gt; weight.initialize(ctx=mx.cpu(0))</span>
<span class="sd"> &gt;&gt;&gt; weight.data()</span>
<span class="sd"> [[-0.01068833 0.01729892]</span>
<span class="sd"> [ 0.02042518 -0.01618656]]</span>
<span class="sd"> &lt;NDArray 2x2 @cpu(0)&gt;</span>
<span class="sd"> &gt;&gt;&gt; weight.grad()</span>
<span class="sd"> [[ 0. 0.]</span>
<span class="sd"> [ 0. 0.]]</span>
<span class="sd"> &lt;NDArray 2x2 @cpu(0)&gt;</span>
<span class="sd"> &gt;&gt;&gt; weight.initialize(ctx=[mx.gpu(0), mx.gpu(1)])</span>
<span class="sd"> &gt;&gt;&gt; weight.data(mx.gpu(0))</span>
<span class="sd"> [[-0.00873779 -0.02834515]</span>
<span class="sd"> [ 0.05484822 -0.06206018]]</span>
<span class="sd"> &lt;NDArray 2x2 @gpu(0)&gt;</span>
<span class="sd"> &gt;&gt;&gt; weight.data(mx.gpu(1))</span>
<span class="sd"> [[-0.00873779 -0.02834515]</span>
<span class="sd"> [ 0.05484822 -0.06206018]]</span>
<span class="sd"> &lt;NDArray 2x2 @gpu(1)&gt;</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">force_reinit</span><span class="p">:</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Parameter &#39;</span><span class="si">%s</span><span class="s2">&#39; is already initialized, ignoring. &quot;</span> \
<span class="s2">&quot;Set force_reinit=True to re-initialize.&quot;</span><span class="o">%</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
<span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="k">return</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">if</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">ctx</span> <span class="o">=</span> <span class="p">[</span><span class="n">context</span><span class="o">.</span><span class="n">current_context</span><span class="p">()]</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">Context</span><span class="p">):</span>
<span class="n">ctx</span> <span class="o">=</span> <span class="p">[</span><span class="n">ctx</span><span class="p">]</span>
<span class="k">if</span> <span class="n">init</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">init</span> <span class="o">=</span> <span class="n">default_init</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">init</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">init</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">shape_is_known</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_allow_deferred_init</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span> <span class="o">=</span> <span class="p">(</span><span class="n">init</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">default_init</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="k">return</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Cannot initialize Parameter &#39;</span><span class="si">%s</span><span class="s2">&#39; because it has &quot;</span> \
<span class="s2">&quot;invalid shape: </span><span class="si">%s</span><span class="s2">.&quot;</span><span class="o">%</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span> <span class="o">=</span> <span class="p">(</span><span class="n">init</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">default_init</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_finish_deferred_init</span><span class="p">()</span></div>
<div class="viewcode-block" id="Parameter.reset_ctx"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter.reset_ctx">[docs]</a> <span class="k">def</span> <span class="nf">reset_ctx</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ctx</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Re-assign Parameter to other contexts.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> ctx : Context or list of Context, default ``context.current_context()``.</span>
<span class="sd"> Assign Parameter to given context. If ctx is a list of Context, a</span>
<span class="sd"> copy will be made for each context.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">ctx</span> <span class="o">=</span> <span class="p">[</span><span class="n">context</span><span class="o">.</span><span class="n">current_context</span><span class="p">()]</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">Context</span><span class="p">):</span>
<span class="n">ctx</span> <span class="o">=</span> <span class="p">[</span><span class="n">ctx</span><span class="p">]</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">:</span>
<span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reduce</span><span class="p">()</span>
<span class="k">with</span> <span class="n">autograd</span><span class="o">.</span><span class="n">pause</span><span class="p">():</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_init_impl</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">ctx</span><span class="p">)</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span><span class="p">:</span>
<span class="n">init</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">default_init</span><span class="p">,</span> <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span> <span class="o">=</span> <span class="p">(</span><span class="n">init</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">default_init</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Cannot reset context for Parameter &#39;</span><span class="si">%s</span><span class="s2">&#39; because it &quot;</span>
<span class="s2">&quot;has not been initialized.&quot;</span><span class="o">%</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">)</span></div>
<div class="viewcode-block" id="Parameter.set_data"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter.set_data">[docs]</a> <span class="k">def</span> <span class="nf">set_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Sets this parameter&#39;s value on all contexts.&quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span><span class="p">,</span> \
<span class="s2">&quot;Parameter &#39;</span><span class="si">%s</span><span class="s2">&#39; has not been initialized&quot;</span><span class="o">%</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="n">data</span><span class="p">,)</span>
<span class="k">return</span>
<span class="c1"># if update_on_kvstore, we need to make sure the copy stored in kvstore is in sync</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trainer</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trainer</span><span class="o">.</span><span class="n">_kv_initialized</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trainer</span><span class="o">.</span><span class="n">_update_on_kvstore</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trainer</span><span class="o">.</span><span class="n">_params_to_init</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_trainer</span><span class="o">.</span><span class="n">_reset_kvstore</span><span class="p">()</span>
<span class="k">for</span> <span class="n">arr</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_and_get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
<span class="n">arr</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">data</span></div>
<div class="viewcode-block" id="Parameter.row_sparse_data"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter.row_sparse_data">[docs]</a> <span class="k">def</span> <span class="nf">row_sparse_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">row_id</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Returns a copy of the &#39;row_sparse&#39; parameter on the same context as row_id&#39;s.</span>
<span class="sd"> The copy only retains rows whose ids occur in provided row ids.</span>
<span class="sd"> The parameter must have been initialized on this context before.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> row_id: NDArray</span>
<span class="sd"> Row ids to retain for the &#39;row_sparse&#39; parameter.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> NDArray on row_id&#39;s context</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span> <span class="o">!=</span> <span class="s1">&#39;row_sparse&#39;</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Cannot return a copy of Parameter </span><span class="si">%s</span><span class="s2"> via row_sparse_data() &quot;</span> \
<span class="s2">&quot;because its storage type is </span><span class="si">%s</span><span class="s2">. Please use data() instead.&quot;</span> \
<span class="o">%</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span><span class="p">))</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_row_sparse</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">,</span> <span class="n">row_id</span><span class="o">.</span><span class="n">ctx</span><span class="p">,</span> <span class="n">row_id</span><span class="p">)</span></div>
<div class="viewcode-block" id="Parameter.list_row_sparse_data"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter.list_row_sparse_data">[docs]</a> <span class="k">def</span> <span class="nf">list_row_sparse_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">row_id</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Returns copies of the &#39;row_sparse&#39; parameter on all contexts, in the same order</span>
<span class="sd"> as creation. The copy only retains rows whose ids occur in provided row ids.</span>
<span class="sd"> The parameter must have been initialized before.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> row_id: NDArray</span>
<span class="sd"> Row ids to retain for the &#39;row_sparse&#39; parameter.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> list of NDArrays</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span> <span class="o">!=</span> <span class="s1">&#39;row_sparse&#39;</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Cannot return copies of Parameter &#39;</span><span class="si">%s</span><span class="s2">&#39; on all contexts via &quot;</span> \
<span class="s2">&quot;list_row_sparse_data() because its storage type is </span><span class="si">%s</span><span class="s2">. Please &quot;</span> \
<span class="s2">&quot;use data() instead.&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span><span class="p">))</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_row_sparse</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">,</span> <span class="nb">list</span><span class="p">,</span> <span class="n">row_id</span><span class="p">)</span></div>
<div class="viewcode-block" id="Parameter.data"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter.data">[docs]</a> <span class="k">def</span> <span class="nf">data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Returns a copy of this parameter on one context. Must have been</span>
<span class="sd"> initialized on this context before. For sparse parameters, use</span>
<span class="sd"> :py:meth:`Parameter.row_sparse_data` instead.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> ctx : Context</span>
<span class="sd"> Desired context.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> NDArray on ctx</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span> <span class="o">!=</span> <span class="s1">&#39;default&#39;</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Cannot return a copy of Parameter &#39;</span><span class="si">%s</span><span class="s2">&#39; on ctx </span><span class="si">%s</span><span class="s2"> via data() &quot;</span> \
<span class="s2">&quot;because its storage type is </span><span class="si">%s</span><span class="s2">. Please use row_sparse_data() &quot;</span> \
<span class="s2">&quot;instead.&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">ctx</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span><span class="p">))</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_and_get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">,</span> <span class="n">ctx</span><span class="p">)</span></div>
<div class="viewcode-block" id="Parameter.list_data"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter.list_data">[docs]</a> <span class="k">def</span> <span class="nf">list_data</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Returns copies of this parameter on all contexts, in the same order</span>
<span class="sd"> as creation. For sparse parameters, use :py:meth:`Parameter.list_row_sparse_data`</span>
<span class="sd"> instead.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> list of NDArrays</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span> <span class="o">!=</span> <span class="s1">&#39;default&#39;</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Cannot return copies of Parameter &#39;</span><span class="si">%s</span><span class="s2">&#39; on all contexts via &quot;</span> \
<span class="s2">&quot;list_data() because its storage type is </span><span class="si">%s</span><span class="s2">. Please use &quot;</span> \
<span class="s2">&quot;row_sparse_data() instead.&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span><span class="p">))</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_and_get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span></div>
<div class="viewcode-block" id="Parameter.grad"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter.grad">[docs]</a> <span class="k">def</span> <span class="nf">grad</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Returns a gradient buffer for this parameter on one context.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> ctx : Context</span>
<span class="sd"> Desired context.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
<span class="s2">&quot;Cannot get gradient array for Parameter &#39;</span><span class="si">%s</span><span class="s2">&#39; &quot;</span> \
<span class="s2">&quot;because grad_req=&#39;null&#39;&quot;</span><span class="o">%</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">))</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_and_get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_grad</span><span class="p">,</span> <span class="n">ctx</span><span class="p">)</span></div>
<div class="viewcode-block" id="Parameter.list_grad"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter.list_grad">[docs]</a> <span class="k">def</span> <span class="nf">list_grad</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Returns gradient buffers on all contexts, in the same order</span>
<span class="sd"> as :py:meth:`values`.&quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
<span class="s2">&quot;Cannot get gradient array for Parameter &#39;</span><span class="si">%s</span><span class="s2">&#39; &quot;</span> \
<span class="s2">&quot;because grad_req=&#39;null&#39;&quot;</span><span class="o">%</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">))</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_and_get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_grad</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span></div>
<div class="viewcode-block" id="Parameter.list_ctx"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter.list_ctx">[docs]</a> <span class="k">def</span> <span class="nf">list_ctx</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Returns a list of contexts this parameter is initialized on.&quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_init</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Parameter &#39;</span><span class="si">%s</span><span class="s2">&#39; has not been initialized&quot;</span><span class="o">%</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ctx_list</span></div>
<div class="viewcode-block" id="Parameter.zero_grad"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter.zero_grad">[docs]</a> <span class="k">def</span> <span class="nf">zero_grad</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Sets gradient buffer on all contexts to 0. No action is taken if</span>
<span class="sd"> parameter is uninitialized or doesn&#39;t require gradient.&quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad</span><span class="p">:</span>
<span class="n">ndarray</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">i</span><span class="p">)</span></div>
<div class="viewcode-block" id="Parameter.var"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter.var">[docs]</a> <span class="k">def</span> <span class="nf">var</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Returns a symbol representing this parameter.&quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_var</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_var</span> <span class="o">=</span> <span class="n">symbol</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span>
<span class="n">lr_mult</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lr_mult</span><span class="p">,</span> <span class="n">wd_mult</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">wd_mult</span><span class="p">,</span>
<span class="n">init</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">init</span><span class="p">,</span> <span class="n">stype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_stype</span><span class="p">)</span>
<span class="k">if</span> <span class="n">is_np_array</span><span class="p">():</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_var</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_var</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_var</span></div>
<div class="viewcode-block" id="Parameter.cast"><a class="viewcode-back" href="../../../api/gluon/parameter.html#mxnet.gluon.Parameter.cast">[docs]</a> <span class="k">def</span> <span class="nf">cast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dtype</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Cast data and gradient of this Parameter to a new data type.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> dtype : str or numpy.dtype</span>
<span class="sd"> The new data type.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_dtype</span> <span class="o">=</span> <span class="n">dtype</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span>
<span class="k">with</span> <span class="n">autograd</span><span class="o">.</span><span class="n">pause</span><span class="p">():</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">]</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_grad</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad</span><span class="p">]</span>
<span class="n">autograd</span><span class="o">.</span><span class="n">mark_variables</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_req</span><span class="p">)</span></div></div>
<span class="k">class</span> <span class="nc">Constant</span><span class="p">(</span><span class="n">Parameter</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;A constant parameter for holding immutable tensors.</span>
<span class="sd"> `Constant`s are ignored by `autograd` and `Trainer`, thus their values</span>
<span class="sd"> will not change during training. But you can still update their values</span>
<span class="sd"> manually with the `set_data` method.</span>
<span class="sd"> `Constant` s can be created with either::</span>
<span class="sd"> const = mx.gluon.Constant(&#39;const&#39;, [[1,2],[3,4]])</span>
<span class="sd"> or::</span>
<span class="sd"> class Block(gluon.Block):</span>
<span class="sd"> def __init__(self, **kwargs):</span>
<span class="sd"> super(Block, self).__init__(**kwargs)</span>
<span class="sd"> self.const = self.params.get_constant(&#39;const&#39;, [[1,2],[3,4]])</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> name : str</span>
<span class="sd"> Name of the parameter.</span>
<span class="sd"> value : array-like</span>
<span class="sd"> Initial value for the constant.</span>
<span class="sd"> &quot;&quot;&quot;</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">name</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span>
<span class="n">array_fn</span> <span class="o">=</span> <span class="n">_mx_np</span><span class="o">.</span><span class="n">array</span> <span class="k">if</span> <span class="n">is_np_array</span><span class="p">()</span> <span class="k">else</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">array</span>
<span class="n">value</span> <span class="o">=</span> <span class="n">array_fn</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">value</span> <span class="o">=</span> <span class="n">value</span>
<span class="k">class</span> <span class="nc">Init</span><span class="p">(</span><span class="n">initializer</span><span class="o">.</span><span class="n">Initializer</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">_init_weight</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">arr</span><span class="p">):</span>
<span class="n">value</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span>
<span class="n">init_name</span> <span class="o">=</span> <span class="s1">&#39;Constant_</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="nb">id</span><span class="p">(</span><span class="bp">self</span><span class="p">))</span>
<span class="n">initializer</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="n">init_name</span><span class="p">)(</span><span class="n">Init</span><span class="p">)</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Constant</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
<span class="n">name</span><span class="p">,</span> <span class="n">grad_req</span><span class="o">=</span><span class="s1">&#39;null&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">value</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">value</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span>
<span class="n">init</span><span class="o">=</span><span class="n">init_name</span><span class="p">)</span>
<span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;Constant </span><span class="si">{name}</span><span class="s1"> (shape=</span><span class="si">{shape}</span><span class="s1">, dtype=</span><span class="si">{dtype}</span><span class="s1">)&#39;</span>
<span class="k">return</span> <span class="n">s</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">grad_req</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="s1">&#39;null&#39;</span>
<span class="nd">@grad_req</span><span class="o">.</span><span class="n">setter</span>
<span class="k">def</span> <span class="nf">grad_req</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">req</span><span class="p">):</span>
<span class="k">if</span> <span class="n">req</span> <span class="o">!=</span> <span class="s1">&#39;null&#39;</span><span class="p">:</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;Constant parameter &quot;</span><span class="si">{}</span><span class="s1">&quot; does not support &#39;</span>
<span class="s1">&#39;grad_req other than &quot;null&quot;, and new value &quot;</span><span class="si">{}</span><span class="s1">&quot; &#39;</span>
<span class="s1">&#39;is ignored.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">req</span><span class="p">))</span>
<span class="k">class</span> <span class="nc">ParameterDict</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;A dictionary managing a set of parameters.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> prefix : str, default ``&#39;&#39;``</span>
<span class="sd"> The prefix to be prepended to all Parameters&#39; names created by this dict.</span>
<span class="sd"> shared : ParameterDict or None</span>
<span class="sd"> If not ``None``, when this dict&#39;s :py:meth:`get` method creates a new parameter, will</span>
<span class="sd"> first try to retrieve it from &quot;shared&quot; dict. Usually used for sharing</span>
<span class="sd"> parameters with another Block.</span>
<span class="sd"> &quot;&quot;&quot;</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">prefix</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">shared</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_prefix</span> <span class="o">=</span> <span class="n">prefix</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_params</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_shared</span> <span class="o">=</span> <span class="n">shared</span>
<span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{name}</span><span class="s1">(</span><span class="se">\n</span><span class="si">{content}</span><span class="se">\n</span><span class="s1">)&#39;</span>
<span class="n">name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prefix</span><span class="o">+</span><span class="s1">&#39; &#39;</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prefix</span> <span class="k">else</span> <span class="s1">&#39;&#39;</span>
<span class="k">return</span> <span class="n">s</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span>
<span class="n">content</span><span class="o">=</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">_indent</span><span class="p">(</span><span class="s1">&#39; </span><span class="si">{0}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">v</span><span class="p">),</span> <span class="mi">2</span><span class="p">)</span>
<span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">()]))</span>
<span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_params</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
<span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">iter</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_params</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">items</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_params</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">keys</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_params</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">values</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_params</span><span class="o">.</span><span class="n">values</span><span class="p">()</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">prefix</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Prefix of this dict. It will be prepended to :py:class:`Parameter`s&#39; name created</span>
<span class="sd"> with :py:func:`get`.&quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prefix</span>
<span class="k">def</span> <span class="nf">_get_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
<span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_params</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_params</span><span class="p">[</span><span class="n">name</span><span class="p">]</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shared</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shared</span><span class="o">.</span><span class="n">_params</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_params</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shared</span><span class="o">.</span><span class="n">_params</span><span class="p">[</span><span class="n">name</span><span class="p">]</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shared</span><span class="o">.</span><span class="n">_params</span><span class="p">[</span><span class="n">name</span><span class="p">]</span>
<span class="k">return</span> <span class="kc">None</span>
<span class="k">def</span> <span class="nf">get</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Retrieves a :py:class:`Parameter` with name ``self.prefix+name``. If not found,</span>
<span class="sd"> :py:func:`get` will first try to retrieve it from &quot;shared&quot; dict. If still not</span>
<span class="sd"> found, :py:func:`get` will create a new :py:class:`Parameter` with key-word arguments and</span>
<span class="sd"> insert it to self.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> name : str</span>
<span class="sd"> Name of the desired Parameter. It will be prepended with this dictionary&#39;s</span>
<span class="sd"> prefix.</span>
<span class="sd"> **kwargs : dict</span>
<span class="sd"> The rest of key-word arguments for the created :py:class:`Parameter`.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> Parameter</span>
<span class="sd"> The created or retrieved :py:class:`Parameter`.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">prefix</span> <span class="o">+</span> <span class="n">name</span>
<span class="n">param</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_impl</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
<span class="k">if</span> <span class="n">param</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="c1"># pylint: disable=too-many-nested-blocks</span>
<span class="n">param</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_params</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">param</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">param</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">param</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">existing</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">param</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span>
<span class="k">if</span> <span class="n">k</span> <span class="o">==</span> <span class="s1">&#39;shape&#39;</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">existing</span><span class="p">):</span>
<span class="n">inferred_shape</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">matched</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">for</span> <span class="n">dim1</span><span class="p">,</span> <span class="n">dim2</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">existing</span><span class="p">):</span>
<span class="k">if</span> <span class="n">dim1</span> <span class="o">!=</span> <span class="n">dim2</span> <span class="ow">and</span> <span class="n">dim1</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">dim2</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">matched</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">break</span>
<span class="k">elif</span> <span class="n">dim1</span> <span class="o">==</span> <span class="n">dim2</span><span class="p">:</span>
<span class="n">inferred_shape</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dim1</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">dim1</span> <span class="ow">in</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">):</span> <span class="c1"># -1 means unknown dim size in np_shape mode</span>
<span class="n">inferred_shape</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dim2</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">inferred_shape</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dim1</span><span class="p">)</span>
<span class="k">if</span> <span class="n">matched</span><span class="p">:</span>
<span class="n">param</span><span class="o">.</span><span class="n">_shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">inferred_shape</span><span class="p">)</span>
<span class="k">continue</span>
<span class="k">elif</span> <span class="n">k</span> <span class="o">==</span> <span class="s1">&#39;dtype&#39;</span> <span class="ow">and</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="n">existing</span><span class="p">):</span>
<span class="k">continue</span>
<span class="k">assert</span> <span class="n">v</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">v</span> <span class="o">==</span> <span class="n">existing</span><span class="p">,</span> \
<span class="s2">&quot;Cannot retrieve Parameter &#39;</span><span class="si">%s</span><span class="s2">&#39; because desired attribute &quot;</span> \
<span class="s2">&quot;does not match with stored for attribute &#39;</span><span class="si">%s</span><span class="s2">&#39;: &quot;</span> \
<span class="s2">&quot;desired &#39;</span><span class="si">%s</span><span class="s2">&#39; vs stored &#39;</span><span class="si">%s</span><span class="s2">&#39;.&quot;</span><span class="o">%</span><span class="p">(</span>
<span class="n">name</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="nb">getattr</span><span class="p">(</span><span class="n">param</span><span class="p">,</span> <span class="n">k</span><span class="p">)))</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">setattr</span><span class="p">(</span><span class="n">param</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span>
<span class="k">return</span> <span class="n">param</span>
<span class="k">def</span> <span class="nf">get_constant</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Retrieves a :py:class:`.Constant` with name ``self.prefix+name``. If not found,</span>
<span class="sd"> :py:func:`get` will first try to retrieve it from &quot;shared&quot; dict. If still not</span>
<span class="sd"> found, :py:func:`get` will create a new :py:class:`.Constant` with key-word</span>
<span class="sd"> arguments and insert it to self.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> name : str</span>
<span class="sd"> Name of the desired Constant. It will be prepended with this dictionary&#39;s</span>
<span class="sd"> prefix.</span>
<span class="sd"> value : array-like</span>
<span class="sd"> Initial value of constant.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :py:class:`.Constant`</span>
<span class="sd"> The created or retrieved :py:class:`.Constant`.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">prefix</span> <span class="o">+</span> <span class="n">name</span>
<span class="n">param</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_impl</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
<span class="k">if</span> <span class="n">param</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">if</span> <span class="n">value</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span><span class="s2">&quot;No constant named &#39;</span><span class="si">{}</span><span class="s2">&#39;. Please specify value &quot;</span> \
<span class="s2">&quot;if you want to create a new constant.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">name</span><span class="p">))</span>
<span class="n">param</span> <span class="o">=</span> <span class="n">Constant</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_params</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">param</span>
<span class="k">elif</span> <span class="n">value</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">param</span><span class="p">,</span> <span class="n">Constant</span><span class="p">),</span> \
<span class="s2">&quot;Parameter &#39;</span><span class="si">{}</span><span class="s2">&#39; already exists but it is not a constant.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">name</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span>
<span class="n">value</span> <span class="o">=</span> <span class="n">value</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
<span class="k">assert</span> <span class="n">param</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">value</span><span class="o">.</span><span class="n">shape</span> <span class="ow">and</span> \
<span class="p">(</span><span class="n">param</span><span class="o">.</span><span class="n">value</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> <span class="o">==</span> <span class="n">value</span><span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">(),</span> \
<span class="s2">&quot;Constant &#39;</span><span class="si">{}</span><span class="s2">&#39; already exists but it&#39;s value doesn&#39;t match new &quot;</span> \
<span class="s2">&quot;value&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
<span class="k">return</span> <span class="n">param</span>
<span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Copies all Parameters in ``other`` to self.&quot;&quot;&quot;</span>
<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">other</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_params</span><span class="p">:</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_params</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="ow">is</span> <span class="n">v</span><span class="p">,</span> \
<span class="s2">&quot;Cannot update self with other because they have different &quot;</span> \
<span class="s2">&quot;Parameters with the same name &#39;</span><span class="si">%s</span><span class="s2">&#39;&quot;</span><span class="o">%</span><span class="n">k</span>
<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">other</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_params</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
<span class="k">def</span> <span class="nf">initialize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">init</span><span class="o">=</span><span class="n">initializer</span><span class="o">.</span><span class="n">Uniform</span><span class="p">(),</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">force_reinit</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Initializes all Parameters managed by this dictionary to be used for :py:class:`NDArray`</span>
<span class="sd"> API. It has no effect when using :py:class:`Symbol` API.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> init : Initializer</span>
<span class="sd"> Global default Initializer to be used when :py:meth:`Parameter.init` is ``None``.</span>
<span class="sd"> Otherwise, :py:meth:`Parameter.init` takes precedence.</span>
<span class="sd"> ctx : Context or list of Context</span>
<span class="sd"> Keeps a copy of Parameters on one or many context(s).</span>
<span class="sd"> verbose : bool, default False</span>
<span class="sd"> Whether to verbosely print out details on initialization.</span>
<span class="sd"> force_reinit : bool, default False</span>
<span class="sd"> Whether to force re-initialization if parameter is already initialized.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
<span class="n">init</span><span class="o">.</span><span class="n">set_verbosity</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span>
<span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">v</span><span class="o">.</span><span class="n">initialize</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">init</span><span class="p">,</span> <span class="n">force_reinit</span><span class="o">=</span><span class="n">force_reinit</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">zero_grad</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Sets all Parameters&#39; gradient buffer to 0.&quot;&quot;&quot;</span>
<span class="c1"># collect gradient arrays for each ctx</span>
<span class="n">arrays</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
<span class="k">if</span> <span class="n">p</span><span class="o">.</span><span class="n">grad_req</span> <span class="o">==</span> <span class="s1">&#39;null&#39;</span> <span class="ow">or</span> <span class="n">p</span><span class="o">.</span><span class="n">_grad</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">continue</span>
<span class="k">for</span> <span class="n">g</span> <span class="ow">in</span> <span class="n">p</span><span class="o">.</span><span class="n">list_grad</span><span class="p">():</span>
<span class="k">if</span> <span class="n">g</span><span class="o">.</span><span class="n">stype</span> <span class="o">==</span> <span class="s1">&#39;row_sparse&#39;</span><span class="p">:</span>
<span class="n">ndarray</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">g</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">g</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">arrays</span><span class="p">[</span><span class="n">g</span><span class="o">.</span><span class="n">ctx</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">g</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">arrays</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span>
<span class="k">if</span> <span class="n">is_np_array</span><span class="p">():</span>
<span class="k">for</span> <span class="n">arr</span> <span class="ow">in</span> <span class="n">arrays</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
<span class="k">for</span> <span class="n">ele</span> <span class="ow">in</span> <span class="n">arr</span><span class="p">:</span>
<span class="n">ele</span><span class="p">[()]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">for</span> <span class="n">arr</span> <span class="ow">in</span> <span class="n">arrays</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
<span class="n">ndarray</span><span class="o">.</span><span class="n">reset_arrays</span><span class="p">(</span><span class="o">*</span><span class="n">arr</span><span class="p">,</span> <span class="n">num_arrays</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">arr</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">reset_ctx</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ctx</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Re-assign all Parameters to other contexts.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> ctx : Context or list of Context, default :py:meth:`context.current_context()`.</span>
<span class="sd"> Assign Parameter to given context. If ctx is a list of Context, a</span>
<span class="sd"> copy will be made for each context.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
<span class="n">i</span><span class="o">.</span><span class="n">reset_ctx</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">list_ctx</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Returns a list of all the contexts on which the underlying Parameters</span>
<span class="sd"> are initialized.&quot;&quot;&quot;</span>
<span class="n">s</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
<span class="n">s</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">list_ctx</span><span class="p">())</span>
<span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">setattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Set an attribute to a new value for all Parameters.</span>
<span class="sd"> For example, set grad_req to null if you don&#39;t need gradient w.r.t a</span>
<span class="sd"> model&#39;s Parameters::</span>
<span class="sd"> model.collect_params().setattr(&#39;grad_req&#39;, &#39;null&#39;)</span>
<span class="sd"> or change the learning rate multiplier::</span>
<span class="sd"> model.collect_params().setattr(&#39;lr_mult&#39;, 0.5)</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> name : str</span>
<span class="sd"> Name of the attribute.</span>
<span class="sd"> value : valid type for attribute name</span>
<span class="sd"> The new value for the attribute.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
<span class="nb">setattr</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">save</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">filename</span><span class="p">,</span> <span class="n">strip_prefix</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Save parameters to file.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> filename : str</span>
<span class="sd"> Path to parameter file.</span>
<span class="sd"> strip_prefix : str, default &#39;&#39;</span>
<span class="sd"> Strip prefix from parameter names before saving.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">arg_dict</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
<span class="n">weight</span> <span class="o">=</span> <span class="n">param</span><span class="o">.</span><span class="n">_reduce</span><span class="p">()</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">param</span><span class="o">.</span><span class="n">name</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="n">strip_prefix</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s2">&quot;Prefix &#39;</span><span class="si">%s</span><span class="s2">&#39; is to be striped before saving, but Parameter&#39;s &quot;</span>
<span class="s2">&quot;name &#39;</span><span class="si">%s</span><span class="s2">&#39; does not start with &#39;</span><span class="si">%s</span><span class="s2">&#39;. &quot;</span>
<span class="s2">&quot;this may be due to your Block shares parameters from other &quot;</span>
<span class="s2">&quot;Blocks or you forgot to use &#39;with name_scope()&#39; when creating &quot;</span>
<span class="s2">&quot;child blocks. For more info on naming, please see &quot;</span>
<span class="s2">&quot;https://mxnet.io/api/python/docs/tutorials/packages/gluon/blocks/naming.html&quot;</span><span class="o">%</span><span class="p">(</span>
<span class="n">strip_prefix</span><span class="p">,</span> <span class="n">param</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">strip_prefix</span><span class="p">))</span>
<span class="n">arg_dict</span><span class="p">[</span><span class="n">param</span><span class="o">.</span><span class="n">name</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">strip_prefix</span><span class="p">):]]</span> <span class="o">=</span> <span class="n">weight</span>
<span class="n">ndarray</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">arg_dict</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">filename</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">allow_missing</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">ignore_extra</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">restore_prefix</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">cast_dtype</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">dtype_source</span><span class="o">=</span><span class="s2">&quot;current&quot;</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Load parameters from file.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> filename : str</span>
<span class="sd"> Path to parameter file.</span>
<span class="sd"> ctx : Context or list of Context</span>
<span class="sd"> Context(s) initialize loaded parameters on.</span>
<span class="sd"> allow_missing : bool, default False</span>
<span class="sd"> Whether to silently skip loading parameters not represents in the file.</span>
<span class="sd"> ignore_extra : bool, default False</span>
<span class="sd"> Whether to silently ignore parameters from the file that are not</span>
<span class="sd"> present in this ParameterDict.</span>
<span class="sd"> restore_prefix : str, default &#39;&#39;</span>
<span class="sd"> prepend prefix to names of stored parameters before loading.</span>
<span class="sd"> cast_dtype : bool, default False</span>
<span class="sd"> Cast the data type of the parameter</span>
<span class="sd"> dtype_source : str, default &#39;current&#39;</span>
<span class="sd"> must be in {&#39;current&#39;, &#39;saved&#39;}</span>
<span class="sd"> Only valid if cast_dtype=True, specify the source of the dtype for casting</span>
<span class="sd"> the parameters</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">restore_prefix</span><span class="p">:</span>
<span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
<span class="k">assert</span> <span class="n">name</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="n">restore_prefix</span><span class="p">),</span> \
<span class="s2">&quot;restore_prefix is &#39;</span><span class="si">%s</span><span class="s2">&#39; but Parameters name &#39;</span><span class="si">%s</span><span class="s2">&#39; does not start &quot;</span> \
<span class="s2">&quot;with &#39;</span><span class="si">%s</span><span class="s2">&#39;. For more info on naming, please see &quot;</span> \
<span class="s2">&quot;https://mxnet.io/api/python/docs/tutorials/packages/gluon/blocks/naming.html&quot;</span><span class="o">%</span><span class="p">(</span>
<span class="n">restore_prefix</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">restore_prefix</span><span class="p">)</span>
<span class="n">ndarray_load</span> <span class="o">=</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">load_dict</span><span class="p">(</span><span class="n">ndarray_load</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">allow_missing</span><span class="p">,</span>
<span class="n">ignore_extra</span><span class="p">,</span> <span class="n">restore_prefix</span><span class="p">,</span> <span class="n">filename</span><span class="p">,</span> <span class="n">cast_dtype</span><span class="p">,</span> <span class="n">dtype_source</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">load_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">param_dict</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">allow_missing</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">ignore_extra</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">restore_prefix</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cast_dtype</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">dtype_source</span><span class="o">=</span><span class="s2">&quot;current&quot;</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Load parameters from dict</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> param_dict : dict</span>
<span class="sd"> Dictionary containing model parameters, preprended with arg: and aux: names</span>
<span class="sd"> ctx : Context or list of Context</span>
<span class="sd"> Context(s) initialize loaded parameters on.</span>
<span class="sd"> allow_missing : bool, default False</span>
<span class="sd"> Whether to silently skip loading parameters not represented in the file.</span>
<span class="sd"> ignore_extra : bool, default False</span>
<span class="sd"> Whether to silently ignore parameters from the file that are not</span>
<span class="sd"> present in this ParameterDict.</span>
<span class="sd"> restore_prefix : str, default &#39;&#39;</span>
<span class="sd"> prepend prefix to names of stored parameters before loading</span>
<span class="sd"> filename : str, default None</span>
<span class="sd"> cast_dtype : bool, default False</span>
<span class="sd"> Cast the data type of the NDArray loaded from the checkpoint to the dtype</span>
<span class="sd"> provided by the Parameter if any</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">lprefix</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">restore_prefix</span><span class="p">)</span>
<span class="n">loaded</span> <span class="o">=</span> <span class="p">[(</span><span class="n">k</span><span class="p">[</span><span class="mi">4</span><span class="p">:]</span> <span class="k">if</span> <span class="n">k</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">&#39;arg:&#39;</span><span class="p">)</span> <span class="ow">or</span> <span class="n">k</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">&#39;aux:&#39;</span><span class="p">)</span> <span class="k">else</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span> \
<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">param_dict</span><span class="o">.</span><span class="n">items</span><span class="p">()]</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">param_dict</span><span class="p">,</span> <span class="nb">dict</span><span class="p">)</span> <span class="k">else</span> <span class="n">param_dict</span>
<span class="n">arg_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">restore_prefix</span><span class="o">+</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">loaded</span><span class="p">}</span>
<span class="n">error_str</span> <span class="o">=</span> <span class="s2">&quot;file: </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">filename</span><span class="p">)</span> <span class="k">if</span> <span class="n">filename</span> <span class="k">else</span> <span class="s2">&quot;param_dict&quot;</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">allow_missing</span><span class="p">:</span>
<span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
<span class="k">assert</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">arg_dict</span><span class="p">,</span> \
<span class="s2">&quot;Parameter &#39;</span><span class="si">%s</span><span class="s2">&#39; is missing in </span><span class="si">%s</span><span class="s2">, which contains parameters: </span><span class="si">%s</span><span class="s2">. &quot;</span> \
<span class="s2">&quot;Please make sure source and target networks have the same prefix.&quot;</span> \
<span class="s2">&quot;For more info on naming, please see &quot;</span> \
<span class="s2">&quot;https://mxnet.io/api/python/docs/tutorials/packages/gluon/blocks/naming.html&quot;</span><span class="o">%</span><span class="p">(</span>
<span class="n">name</span><span class="p">[</span><span class="n">lprefix</span><span class="p">:],</span> <span class="n">error_str</span><span class="p">,</span> <span class="n">_brief_print_list</span><span class="p">(</span><span class="n">arg_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
<span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">arg_dict</span><span class="p">:</span>
<span class="k">if</span> <span class="n">name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_params</span><span class="p">:</span>
<span class="k">assert</span> <span class="n">ignore_extra</span><span class="p">,</span> \
<span class="s2">&quot;Parameter &#39;</span><span class="si">%s</span><span class="s2">&#39; loaded from </span><span class="si">%s</span><span class="s2"> is not present in ParameterDict, &quot;</span> \
<span class="s2">&quot;choices are: </span><span class="si">%s</span><span class="s2">. Set ignore_extra to True to ignore. &quot;</span> \
<span class="s2">&quot;Please make sure source and target networks have the same prefix.&quot;</span> \
<span class="s2">&quot;For more info on naming, please see &quot;</span> \
<span class="s2">&quot;https://mxnet.io/api/python/docs/tutorials/packages/gluon/blocks/naming.html&quot;</span><span class="o">%</span><span class="p">(</span>
<span class="n">name</span><span class="p">[</span><span class="n">lprefix</span><span class="p">:],</span> <span class="n">error_str</span><span class="p">,</span> <span class="n">_brief_print_list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_params</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
<span class="k">continue</span>
<span class="bp">self</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">_load_init</span><span class="p">(</span><span class="n">arg_dict</span><span class="p">[</span><span class="n">name</span><span class="p">],</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">cast_dtype</span><span class="o">=</span><span class="n">cast_dtype</span><span class="p">,</span>
<span class="n">dtype_source</span><span class="o">=</span><span class="n">dtype_source</span><span class="p">)</span>
</pre></div>
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