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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/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html">Step 5: <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-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-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-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</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/gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/image/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> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../../tutorials/packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../../tutorials/packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
| </ul> |
| </li> |
| <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> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/performance/backend/dnnl/dnnl_quantization_inc.html">Improving accuracy with Intel® Neural Compressor</a></li> |
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| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/add_op_in_backend">New Operator in MXNet Backend</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/using_rtc">Using RTC for CUDA kernels</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.zeros_like.html">mxnet.np.zeros_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.array.html">mxnet.np.array</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.copy.html">mxnet.np.copy</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../../api/np/routines.array-manipulation.html">Array manipulation routines</a><ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.ravel.html">mxnet.np.ravel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.ndarray.flatten.html">mxnet.np.ndarray.flatten</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.swapaxes.html">mxnet.np.swapaxes</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.ndarray.T.html">mxnet.np.ndarray.T</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.transpose.html">mxnet.np.transpose</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.moveaxis.html">mxnet.np.moveaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.rollaxis.html">mxnet.np.rollaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.expand_dims.html">mxnet.np.expand_dims</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.squeeze.html">mxnet.np.squeeze</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.broadcast_to.html">mxnet.np.broadcast_to</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.broadcast_arrays.html">mxnet.np.broadcast_arrays</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.atleast_1d.html">mxnet.np.atleast_1d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.atleast_2d.html">mxnet.np.atleast_2d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.atleast_3d.html">mxnet.np.atleast_3d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.concatenate.html">mxnet.np.concatenate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.stack.html">mxnet.np.stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.dstack.html">mxnet.np.dstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.vstack.html">mxnet.np.vstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.column_stack.html">mxnet.np.column_stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.hstack.html">mxnet.np.hstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.split.html">mxnet.np.split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.hsplit.html">mxnet.np.hsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.vsplit.html">mxnet.np.vsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.array_split.html">mxnet.np.array_split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.dsplit.html">mxnet.np.dsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.tile.html">mxnet.np.tile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.repeat.html">mxnet.np.repeat</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.unique.html">mxnet.np.unique</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.delete.html">mxnet.np.delete</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.insert.html">mxnet.np.insert</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.append.html">mxnet.np.append</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.resize.html">mxnet.np.resize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.trim_zeros.html">mxnet.np.trim_zeros</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.flip.html">mxnet.np.flip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.roll.html">mxnet.np.roll</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.rot90.html">mxnet.np.rot90</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.fliplr.html">mxnet.np.fliplr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.flipud.html">mxnet.np.flipud</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../api/np/routines.io.html">Input and output</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.genfromtxt.html">mxnet.np.genfromtxt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.ndarray.tolist.html">mxnet.np.ndarray.tolist</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.set_printoptions.html">mxnet.np.set_printoptions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../api/np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.dot.html">mxnet.np.dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.vdot.html">mxnet.np.vdot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.inner.html">mxnet.np.inner</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.outer.html">mxnet.np.outer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.tensordot.html">mxnet.np.tensordot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.einsum.html">mxnet.np.einsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.multi_dot.html">mxnet.np.linalg.multi_dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.matmul.html">mxnet.np.matmul</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.matrix_power.html">mxnet.np.linalg.matrix_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.kron.html">mxnet.np.kron</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.svd.html">mxnet.np.linalg.svd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.cholesky.html">mxnet.np.linalg.cholesky</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.qr.html">mxnet.np.linalg.qr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.eig.html">mxnet.np.linalg.eig</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.eigh.html">mxnet.np.linalg.eigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.eigvals.html">mxnet.np.linalg.eigvals</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.eigvalsh.html">mxnet.np.linalg.eigvalsh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.norm.html">mxnet.np.linalg.norm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.trace.html">mxnet.np.trace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.cond.html">mxnet.np.linalg.cond</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.det.html">mxnet.np.linalg.det</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.matrix_rank.html">mxnet.np.linalg.matrix_rank</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.slogdet.html">mxnet.np.linalg.slogdet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.solve.html">mxnet.np.linalg.solve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.tensorsolve.html">mxnet.np.linalg.tensorsolve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.lstsq.html">mxnet.np.linalg.lstsq</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.inv.html">mxnet.np.linalg.inv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.pinv.html">mxnet.np.linalg.pinv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.tensorinv.html">mxnet.np.linalg.tensorinv</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../api/np/routines.math.html">Mathematical functions</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.sin.html">mxnet.np.sin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.cos.html">mxnet.np.cos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.tan.html">mxnet.np.tan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.arcsin.html">mxnet.np.arcsin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.arccos.html">mxnet.np.arccos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.arctan.html">mxnet.np.arctan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.degrees.html">mxnet.np.degrees</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.radians.html">mxnet.np.radians</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.hypot.html">mxnet.np.hypot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.arctan2.html">mxnet.np.arctan2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.deg2rad.html">mxnet.np.deg2rad</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.rad2deg.html">mxnet.np.rad2deg</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.unwrap.html">mxnet.np.unwrap</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.sinh.html">mxnet.np.sinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.cosh.html">mxnet.np.cosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.tanh.html">mxnet.np.tanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.arcsinh.html">mxnet.np.arcsinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.arccosh.html">mxnet.np.arccosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.arctanh.html">mxnet.np.arctanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.rint.html">mxnet.np.rint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.fix.html">mxnet.np.fix</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.floor.html">mxnet.np.floor</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.ceil.html">mxnet.np.ceil</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.trunc.html">mxnet.np.trunc</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.around.html">mxnet.np.around</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.round_.html">mxnet.np.round_</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.sum.html">mxnet.np.sum</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.cumsum.html">mxnet.np.cumsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nanprod.html">mxnet.np.nanprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nansum.html">mxnet.np.nansum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.cumprod.html">mxnet.np.cumprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nancumprod.html">mxnet.np.nancumprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nancumsum.html">mxnet.np.nancumsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.diff.html">mxnet.np.diff</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.ediff1d.html">mxnet.np.ediff1d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.cross.html">mxnet.np.cross</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.trapz.html">mxnet.np.trapz</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.exp.html">mxnet.np.exp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.expm1.html">mxnet.np.expm1</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.log.html">mxnet.np.log</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.log2.html">mxnet.np.log2</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.logaddexp.html">mxnet.np.logaddexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.i0.html">mxnet.np.i0</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.ldexp.html">mxnet.np.ldexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.signbit.html">mxnet.np.signbit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.copysign.html">mxnet.np.copysign</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.frexp.html">mxnet.np.frexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.spacing.html">mxnet.np.spacing</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.lcm.html">mxnet.np.lcm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.gcd.html">mxnet.np.gcd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.add.html">mxnet.np.add</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.divide.html">mxnet.np.divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.power.html">mxnet.np.power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.subtract.html">mxnet.np.subtract</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.mod.html">mxnet.np.mod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.multiply.html">mxnet.np.multiply</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.true_divide.html">mxnet.np.true_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.remainder.html">mxnet.np.remainder</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.positive.html">mxnet.np.positive</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.float_power.html">mxnet.np.float_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.fmod.html">mxnet.np.fmod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.modf.html">mxnet.np.modf</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.divmod.html">mxnet.np.divmod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.floor_divide.html">mxnet.np.floor_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.clip.html">mxnet.np.clip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.sqrt.html">mxnet.np.sqrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.cbrt.html">mxnet.np.cbrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.square.html">mxnet.np.square</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.absolute.html">mxnet.np.absolute</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.sign.html">mxnet.np.sign</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.maximum.html">mxnet.np.maximum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.minimum.html">mxnet.np.minimum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.fabs.html">mxnet.np.fabs</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.heaviside.html">mxnet.np.heaviside</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.fmax.html">mxnet.np.fmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.fmin.html">mxnet.np.fmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nan_to_num.html">mxnet.np.nan_to_num</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.interp.html">mxnet.np.interp</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../../api/np/random/index.html">np.random</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.choice.html">mxnet.np.random.choice</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.shuffle.html">mxnet.np.random.shuffle</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.normal.html">mxnet.np.random.normal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.uniform.html">mxnet.np.random.uniform</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.rand.html">mxnet.np.random.rand</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.chisquare.html">mxnet.np.random.chisquare</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.exponential.html">mxnet.np.random.exponential</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.f.html">mxnet.np.random.f</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.gamma.html">mxnet.np.random.gamma</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.gumbel.html">mxnet.np.random.gumbel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.laplace.html">mxnet.np.random.laplace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.logistic.html">mxnet.np.random.logistic</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.lognormal.html">mxnet.np.random.lognormal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.multinomial.html">mxnet.np.random.multinomial</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.multivariate_normal.html">mxnet.np.random.multivariate_normal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.pareto.html">mxnet.np.random.pareto</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.power.html">mxnet.np.random.power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.rayleigh.html">mxnet.np.random.rayleigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.weibull.html">mxnet.np.random.weibull</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../api/np/routines.sort.html">Sorting, searching, and counting</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.ndarray.sort.html">mxnet.np.ndarray.sort</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.lexsort.html">mxnet.np.lexsort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.argsort.html">mxnet.np.argsort</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.partition.html">mxnet.np.partition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.argpartition.html">mxnet.np.argpartition</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nanargmax.html">mxnet.np.nanargmax</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nonzero.html">mxnet.np.nonzero</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.flatnonzero.html">mxnet.np.flatnonzero</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.where.html">mxnet.np.where</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.searchsorted.html">mxnet.np.searchsorted</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.extract.html">mxnet.np.extract</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.count_nonzero.html">mxnet.np.count_nonzero</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../api/np/routines.statistics.html">Statistics</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.min.html">mxnet.np.min</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.max.html">mxnet.np.max</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nanmax.html">mxnet.np.nanmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.ptp.html">mxnet.np.ptp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.percentile.html">mxnet.np.percentile</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.quantile.html">mxnet.np.quantile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nanquantile.html">mxnet.np.nanquantile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.mean.html">mxnet.np.mean</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.std.html">mxnet.np.std</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.var.html">mxnet.np.var</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.median.html">mxnet.np.median</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.average.html">mxnet.np.average</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nanmedian.html">mxnet.np.nanmedian</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nanstd.html">mxnet.np.nanstd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nanvar.html">mxnet.np.nanvar</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.corrcoef.html">mxnet.np.corrcoef</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.correlate.html">mxnet.np.correlate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.cov.html">mxnet.np.cov</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.histogram.html">mxnet.np.histogram</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.histogram2d.html">mxnet.np.histogram2d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.histogramdd.html">mxnet.np.histogramdd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.bincount.html">mxnet.np.bincount</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.histogram_bin_edges.html">mxnet.np.histogram_bin_edges</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.digitize.html">mxnet.np.digitize</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../api/npx/index.html">NPX: NumPy Neural Network Extension</a><ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.reset_np.html">mxnet.npx.reset_np</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.cpu.html">mxnet.npx.cpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.cpu_pinned.html">mxnet.npx.cpu_pinned</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.gpu.html">mxnet.npx.gpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.gpu_memory_info.html">mxnet.npx.gpu_memory_info</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.current_device.html">mxnet.npx.current_device</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.num_gpus.html">mxnet.npx.num_gpus</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.activation.html">mxnet.npx.activation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.batch_norm.html">mxnet.npx.batch_norm</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.convolution.html">mxnet.npx.convolution</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.dropout.html">mxnet.npx.dropout</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.embedding.html">mxnet.npx.embedding</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.fully_connected.html">mxnet.npx.fully_connected</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.layer_norm.html">mxnet.npx.layer_norm</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.pooling.html">mxnet.npx.pooling</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.rnn.html">mxnet.npx.rnn</a></li> |
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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/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html">Step 5: <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-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-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-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</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/gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/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/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/image/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> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../../api/np/routines.io.html">Input and output</a><ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../../api/np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.solve.html">mxnet.np.linalg.solve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.tensorsolve.html">mxnet.np.linalg.tensorsolve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.lstsq.html">mxnet.np.linalg.lstsq</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.inv.html">mxnet.np.linalg.inv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.pinv.html">mxnet.np.linalg.pinv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.tensorinv.html">mxnet.np.linalg.tensorinv</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../api/np/routines.math.html">Mathematical functions</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.sin.html">mxnet.np.sin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.cos.html">mxnet.np.cos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.tan.html">mxnet.np.tan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.arcsin.html">mxnet.np.arcsin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.arccos.html">mxnet.np.arccos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.arctan.html">mxnet.np.arctan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.degrees.html">mxnet.np.degrees</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.radians.html">mxnet.np.radians</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.hypot.html">mxnet.np.hypot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.arctan2.html">mxnet.np.arctan2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.deg2rad.html">mxnet.np.deg2rad</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.rad2deg.html">mxnet.np.rad2deg</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.unwrap.html">mxnet.np.unwrap</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.sinh.html">mxnet.np.sinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.cosh.html">mxnet.np.cosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.tanh.html">mxnet.np.tanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.arcsinh.html">mxnet.np.arcsinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.arccosh.html">mxnet.np.arccosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.arctanh.html">mxnet.np.arctanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.rint.html">mxnet.np.rint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.fix.html">mxnet.np.fix</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.floor.html">mxnet.np.floor</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.ceil.html">mxnet.np.ceil</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.trunc.html">mxnet.np.trunc</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.around.html">mxnet.np.around</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.round_.html">mxnet.np.round_</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.sum.html">mxnet.np.sum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.prod.html">mxnet.np.prod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.cumsum.html">mxnet.np.cumsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nanprod.html">mxnet.np.nanprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nansum.html">mxnet.np.nansum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.cumprod.html">mxnet.np.cumprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nancumprod.html">mxnet.np.nancumprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nancumsum.html">mxnet.np.nancumsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.diff.html">mxnet.np.diff</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.ediff1d.html">mxnet.np.ediff1d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.cross.html">mxnet.np.cross</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.trapz.html">mxnet.np.trapz</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.exp.html">mxnet.np.exp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.expm1.html">mxnet.np.expm1</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.log.html">mxnet.np.log</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.log10.html">mxnet.np.log10</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.log2.html">mxnet.np.log2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.log1p.html">mxnet.np.log1p</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.logaddexp.html">mxnet.np.logaddexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.i0.html">mxnet.np.i0</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.ldexp.html">mxnet.np.ldexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.signbit.html">mxnet.np.signbit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.copysign.html">mxnet.np.copysign</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.frexp.html">mxnet.np.frexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.spacing.html">mxnet.np.spacing</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.lcm.html">mxnet.np.lcm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.gcd.html">mxnet.np.gcd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.add.html">mxnet.np.add</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.reciprocal.html">mxnet.np.reciprocal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.negative.html">mxnet.np.negative</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.divide.html">mxnet.np.divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.power.html">mxnet.np.power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.subtract.html">mxnet.np.subtract</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.mod.html">mxnet.np.mod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.multiply.html">mxnet.np.multiply</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.true_divide.html">mxnet.np.true_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.remainder.html">mxnet.np.remainder</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.positive.html">mxnet.np.positive</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.float_power.html">mxnet.np.float_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.fmod.html">mxnet.np.fmod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.modf.html">mxnet.np.modf</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.divmod.html">mxnet.np.divmod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.floor_divide.html">mxnet.np.floor_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.clip.html">mxnet.np.clip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.sqrt.html">mxnet.np.sqrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.cbrt.html">mxnet.np.cbrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.square.html">mxnet.np.square</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.absolute.html">mxnet.np.absolute</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.sign.html">mxnet.np.sign</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.maximum.html">mxnet.np.maximum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.minimum.html">mxnet.np.minimum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.fabs.html">mxnet.np.fabs</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.heaviside.html">mxnet.np.heaviside</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.fmax.html">mxnet.np.fmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.fmin.html">mxnet.np.fmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nan_to_num.html">mxnet.np.nan_to_num</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.interp.html">mxnet.np.interp</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../api/np/random/index.html">np.random</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.choice.html">mxnet.np.random.choice</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.shuffle.html">mxnet.np.random.shuffle</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.normal.html">mxnet.np.random.normal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.uniform.html">mxnet.np.random.uniform</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.rand.html">mxnet.np.random.rand</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.randint.html">mxnet.np.random.randint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.beta.html">mxnet.np.random.beta</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.chisquare.html">mxnet.np.random.chisquare</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.exponential.html">mxnet.np.random.exponential</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.f.html">mxnet.np.random.f</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.gamma.html">mxnet.np.random.gamma</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.gumbel.html">mxnet.np.random.gumbel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.laplace.html">mxnet.np.random.laplace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.logistic.html">mxnet.np.random.logistic</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.lognormal.html">mxnet.np.random.lognormal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.multinomial.html">mxnet.np.random.multinomial</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.multivariate_normal.html">mxnet.np.random.multivariate_normal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.pareto.html">mxnet.np.random.pareto</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.power.html">mxnet.np.random.power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.rayleigh.html">mxnet.np.random.rayleigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.weibull.html">mxnet.np.random.weibull</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../api/np/routines.sort.html">Sorting, searching, and counting</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.ndarray.sort.html">mxnet.np.ndarray.sort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.sort.html">mxnet.np.sort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.lexsort.html">mxnet.np.lexsort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.argsort.html">mxnet.np.argsort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.msort.html">mxnet.np.msort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.partition.html">mxnet.np.partition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.argpartition.html">mxnet.np.argpartition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.argmax.html">mxnet.np.argmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.argmin.html">mxnet.np.argmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nanargmax.html">mxnet.np.nanargmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nanargmin.html">mxnet.np.nanargmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.argwhere.html">mxnet.np.argwhere</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nonzero.html">mxnet.np.nonzero</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.flatnonzero.html">mxnet.np.flatnonzero</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.where.html">mxnet.np.where</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.searchsorted.html">mxnet.np.searchsorted</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.extract.html">mxnet.np.extract</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.count_nonzero.html">mxnet.np.count_nonzero</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../api/np/routines.statistics.html">Statistics</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.min.html">mxnet.np.min</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.max.html">mxnet.np.max</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.amin.html">mxnet.np.amin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.amax.html">mxnet.np.amax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nanmin.html">mxnet.np.nanmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nanmax.html">mxnet.np.nanmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.ptp.html">mxnet.np.ptp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.percentile.html">mxnet.np.percentile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nanpercentile.html">mxnet.np.nanpercentile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.quantile.html">mxnet.np.quantile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nanquantile.html">mxnet.np.nanquantile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.mean.html">mxnet.np.mean</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.std.html">mxnet.np.std</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.var.html">mxnet.np.var</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.median.html">mxnet.np.median</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.average.html">mxnet.np.average</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nanmedian.html">mxnet.np.nanmedian</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nanstd.html">mxnet.np.nanstd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nanvar.html">mxnet.np.nanvar</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.corrcoef.html">mxnet.np.corrcoef</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.correlate.html">mxnet.np.correlate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.cov.html">mxnet.np.cov</a></li> |
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| <h1>Source code for mxnet.gluon.nn.basic_layers</h1><div class="highlight"><pre> |
| <span></span><span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span> |
| <span class="c1"># or more contributor license agreements. See the NOTICE file</span> |
| <span class="c1"># distributed with this work for additional information</span> |
| <span class="c1"># regarding copyright ownership. The ASF licenses this file</span> |
| <span class="c1"># to you under the Apache License, Version 2.0 (the</span> |
| <span class="c1"># "License"); you may not use this file except in compliance</span> |
| <span class="c1"># with the License. You may obtain a copy of the License at</span> |
| <span class="c1">#</span> |
| <span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span> |
| <span class="c1">#</span> |
| <span class="c1"># Unless required by applicable law or agreed to in writing,</span> |
| <span class="c1"># software distributed under the License is distributed on an</span> |
| <span class="c1"># "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span> |
| <span class="c1"># KIND, either express or implied. See the License for the</span> |
| <span class="c1"># specific language governing permissions and limitations</span> |
| <span class="c1"># under the License.</span> |
| |
| <span class="c1"># coding: utf-8</span> |
| <span class="c1"># pylint: disable= arguments-differ</span> |
| <span class="sd">"""Basic neural network layers."""</span> |
| <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Sequential'</span><span class="p">,</span> <span class="s1">'HybridSequential'</span><span class="p">,</span> <span class="s1">'Dense'</span><span class="p">,</span> <span class="s1">'Dropout'</span><span class="p">,</span> <span class="s1">'Embedding'</span><span class="p">,</span> |
| <span class="s1">'BatchNorm'</span><span class="p">,</span> <span class="s1">'SyncBatchNorm'</span><span class="p">,</span> <span class="s1">'InstanceNorm'</span><span class="p">,</span> <span class="s1">'LayerNorm'</span><span class="p">,</span> <span class="s1">'GroupNorm'</span><span class="p">,</span> |
| <span class="s1">'Flatten'</span><span class="p">,</span> <span class="s1">'Lambda'</span><span class="p">,</span> <span class="s1">'HybridLambda'</span><span class="p">,</span> <span class="s1">'Concatenate'</span><span class="p">,</span> <span class="s1">'HybridConcatenate'</span><span class="p">,</span> <span class="s1">'Identity'</span><span class="p">]</span> |
| <span class="kn">import</span> <span class="nn">warnings</span> |
| <span class="kn">import</span> <span class="nn">uuid</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">.activations</span> <span class="kn">import</span> <span class="n">Activation</span> |
| <span class="kn">from</span> <span class="nn">..block</span> <span class="kn">import</span> <span class="n">Block</span><span class="p">,</span> <span class="n">HybridBlock</span> |
| <span class="kn">from</span> <span class="nn">..utils</span> <span class="kn">import</span> <span class="n">_indent</span> |
| <span class="kn">from</span> <span class="nn">...</span> <span class="kn">import</span> <span class="n">np</span><span class="p">,</span> <span class="n">npx</span><span class="p">,</span> <span class="n">device</span> <span class="k">as</span> <span class="n">_device</span> |
| <span class="kn">from</span> <span class="nn">...util</span> <span class="kn">import</span> <span class="n">use_np</span> |
| <span class="kn">from</span> <span class="nn">..parameter</span> <span class="kn">import</span> <span class="n">Parameter</span> |
| <span class="kn">from</span> <span class="nn">...ndarray</span> <span class="kn">import</span> <span class="n">get_dtype_name</span> |
| |
| <div class="viewcode-block" id="Sequential"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.Sequential">[docs]</a><span class="k">class</span> <span class="nc">Sequential</span><span class="p">(</span><span class="n">Block</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Stacks Blocks sequentially.</span> |
| |
| <span class="sd"> Example::</span> |
| |
| <span class="sd"> net = nn.Sequential()</span> |
| <span class="sd"> net.add(nn.Dense(10, activation='relu'))</span> |
| <span class="sd"> net.add(nn.Dense(20))</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Sequential</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="bp">self</span><span class="o">.</span><span class="n">_layers</span> <span class="o">=</span> <span class="p">[]</span> |
| |
| <div class="viewcode-block" id="Sequential.add"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.Sequential.add">[docs]</a> <span class="k">def</span> <span class="nf">add</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">blocks</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Adds block on top of the stack."""</span> |
| <span class="k">for</span> <span class="n">block</span> <span class="ow">in</span> <span class="n">blocks</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_layers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">block</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">register_child</span><span class="p">(</span><span class="n">block</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Sequential.forward"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.Sequential.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span> |
| <span class="k">for</span> <span class="n">block</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">block</span><span class="p">()(</span><span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> |
| <span class="n">args</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span> |
| <span class="n">args</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">args</span><span class="p">:</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">([</span><span class="n">x</span><span class="p">]</span> <span class="o">+</span> <span class="nb">list</span><span class="p">(</span><span class="n">args</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">x</span></div> |
| |
| <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">'</span><span class="si">{name}</span><span class="s1">(</span><span class="se">\n</span><span class="si">{modstr}</span><span class="se">\n</span><span class="s1">)'</span> |
| <span class="n">modstr</span> <span class="o">=</span> <span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">' (</span><span class="si">{key}</span><span class="s1">): </span><span class="si">{block}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">key</span><span class="o">=</span><span class="n">key</span><span class="p">,</span> |
| <span class="n">block</span><span class="o">=</span><span class="n">_indent</span><span class="p">(</span><span class="n">block</span><span class="p">()</span><span class="o">.</span><span class="fm">__repr__</span><span class="p">(),</span> <span class="mi">2</span><span class="p">))</span> |
| <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">block</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">items</span><span class="p">()])</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="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="n">modstr</span><span class="o">=</span><span class="n">modstr</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="n">layers</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">())[</span><span class="n">key</span><span class="p">]</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">layers</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span> |
| <span class="n">net</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)()</span> |
| <span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="o">*</span><span class="p">(</span><span class="n">l</span><span class="p">()</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">layers</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">net</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">layers</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="Sequential.hybridize"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.Sequential.hybridize">[docs]</a> <span class="k">def</span> <span class="nf">hybridize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">active</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Activates or deactivates `HybridBlock` s recursively. Has no effect on</span> |
| <span class="sd"> non-hybrid children.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> active : bool, default True</span> |
| <span class="sd"> Whether to turn hybrid on or off.</span> |
| <span class="sd"> **kwargs : string</span> |
| <span class="sd"> Additional flags for hybridized operator.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span> <span class="ow">and</span> <span class="nb">all</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">c</span><span class="p">(),</span> <span class="n">HybridBlock</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</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="sa">f</span><span class="s2">"All children of this Sequential layer '</span><span class="si">{</span><span class="nb">repr</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="si">}</span><span class="s2">'</span><span class="se">\n</span><span class="s2"> are HybridBlocks. Consider "</span> |
| <span class="s2">"using HybridSequential for the best performance."</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="nb">super</span><span class="p">(</span><span class="n">Sequential</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">hybridize</span><span class="p">(</span><span class="n">active</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div></div> |
| |
| |
| <div class="viewcode-block" id="HybridSequential"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.HybridSequential">[docs]</a><span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">HybridSequential</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Stacks HybridBlocks sequentially.</span> |
| |
| <span class="sd"> Example::</span> |
| |
| <span class="sd"> net = nn.HybridSequential()</span> |
| <span class="sd"> net.add(nn.Dense(10, activation='relu'))</span> |
| <span class="sd"> net.add(nn.Dense(20))</span> |
| <span class="sd"> net.hybridize()</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_layers</span> <span class="o">=</span> <span class="p">[]</span> |
| |
| <div class="viewcode-block" id="HybridSequential.add"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.HybridSequential.add">[docs]</a> <span class="k">def</span> <span class="nf">add</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">blocks</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Adds block on top of the stack."""</span> |
| <span class="k">for</span> <span class="n">block</span> <span class="ow">in</span> <span class="n">blocks</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_layers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">block</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">register_child</span><span class="p">(</span><span class="n">block</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="HybridSequential.forward"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.HybridSequential.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span> |
| <span class="k">for</span> <span class="n">block</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">block</span><span class="p">()(</span><span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> |
| <span class="n">args</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span> |
| <span class="n">args</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">args</span><span class="p">:</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">([</span><span class="n">x</span><span class="p">]</span> <span class="o">+</span> <span class="nb">list</span><span class="p">(</span><span class="n">args</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">x</span></div> |
| |
| <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">'</span><span class="si">{name}</span><span class="s1">(</span><span class="se">\n</span><span class="si">{modstr}</span><span class="se">\n</span><span class="s1">)'</span> |
| <span class="n">modstr</span> <span class="o">=</span> <span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">' (</span><span class="si">{key}</span><span class="s1">): </span><span class="si">{block}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">key</span><span class="o">=</span><span class="n">key</span><span class="p">,</span> |
| <span class="n">block</span><span class="o">=</span><span class="n">_indent</span><span class="p">(</span><span class="n">block</span><span class="p">()</span><span class="o">.</span><span class="fm">__repr__</span><span class="p">(),</span> <span class="mi">2</span><span class="p">))</span> |
| <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">block</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">items</span><span class="p">()])</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="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="n">modstr</span><span class="o">=</span><span class="n">modstr</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="n">layers</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">())[</span><span class="n">key</span><span class="p">]</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">layers</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span> |
| <span class="n">net</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)()</span> |
| <span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="o">*</span><span class="p">(</span><span class="n">l</span><span class="p">()</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">layers</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">net</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">layers</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="Dense"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.Dense">[docs]</a><span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">Dense</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""Just your regular densely-connected NN layer.</span> |
| |
| <span class="sd"> `Dense` implements the operation:</span> |
| <span class="sd"> `output = activation(dot(input, weight.T) + bias)`</span> |
| <span class="sd"> where `activation` is the element-wise activation function</span> |
| <span class="sd"> passed as the `activation` argument, `weight` is a weights matrix</span> |
| <span class="sd"> created by the layer, and `bias` is a bias vector created by the layer</span> |
| <span class="sd"> (only applicable if `use_bias` is `True`).</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> units : int</span> |
| <span class="sd"> Dimensionality of the output space.</span> |
| <span class="sd"> activation : str</span> |
| <span class="sd"> Activation function to use. See help on `Activation` layer.</span> |
| <span class="sd"> If you don't specify anything, no activation is applied</span> |
| <span class="sd"> (ie. "linear" activation: `a(x) = x`).</span> |
| <span class="sd"> use_bias : bool, default True</span> |
| <span class="sd"> Whether the layer uses a bias vector.</span> |
| <span class="sd"> flatten: bool, default True</span> |
| <span class="sd"> Whether the input tensor should be flattened.</span> |
| <span class="sd"> If true, all but the first axis of input data are collapsed together.</span> |
| <span class="sd"> If false, all but the last axis of input data are kept the same, and the transformation</span> |
| <span class="sd"> applies on the last axis.</span> |
| <span class="sd"> dtype : str or np.dtype, default 'float32'</span> |
| <span class="sd"> Data type of output embeddings.</span> |
| <span class="sd"> weight_initializer : str or `Initializer`</span> |
| <span class="sd"> Initializer for the `kernel` weights matrix.</span> |
| <span class="sd"> bias_initializer: str or `Initializer`</span> |
| <span class="sd"> Initializer for the bias vector.</span> |
| <span class="sd"> in_units : int, optional</span> |
| <span class="sd"> Size of the input data. If not specified, initialization will be</span> |
| <span class="sd"> deferred to the first time `forward` is called and `in_units`</span> |
| <span class="sd"> will be inferred from the shape of input data.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: if `flatten` is True, `data` should be a tensor with shape</span> |
| <span class="sd"> `(batch_size, x1, x2, ..., xn)`, where x1 * x2 * ... * xn is equal to</span> |
| <span class="sd"> `in_units`. If `flatten` is False, `data` should have shape</span> |
| <span class="sd"> `(x1, x2, ..., xn, in_units)`.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: if `flatten` is True, `out` will be a tensor with shape</span> |
| <span class="sd"> `(batch_size, units)`. If `flatten` is False, `out` will have shape</span> |
| <span class="sd"> `(x1, x2, ..., xn, units)`.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">units</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flatten</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">dtype</span><span class="o">=</span><span class="s1">'float32'</span><span class="p">,</span> <span class="n">weight_initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">bias_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> |
| <span class="n">in_units</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Dense</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="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_flatten</span> <span class="o">=</span> <span class="n">flatten</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_units</span> <span class="o">=</span> <span class="n">units</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_in_units</span> <span class="o">=</span> <span class="n">in_units</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s1">'weight'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">units</span><span class="p">,</span> <span class="n">in_units</span><span class="p">),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">weight_initializer</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">use_bias</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s1">'bias'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">units</span><span class="p">,),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">bias_initializer</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</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">bias</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="k">if</span> <span class="n">activation</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">act</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="n">activation</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">act</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| <div class="viewcode-block" id="Dense.forward"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.Dense.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
| <span class="n">device</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">device</span> |
| <span class="n">act</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">fully_connected</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">no_bias</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">num_hidden</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_units</span><span class="p">,</span> <span class="n">flatten</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_flatten</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'fwd'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">act</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">act</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">act</span><span class="p">(</span><span class="n">act</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">act</span></div> |
| |
| <div class="viewcode-block" id="Dense.infer_shape"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.Dense.infer_shape">[docs]</a> <span class="k">def</span> <span class="nf">infer_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_flatten</span><span class="p">:</span> |
| <span class="n">num_input</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="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">ndim</span><span class="p">):</span> |
| <span class="n">num_input</span> <span class="o">*=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</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">num_input</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">weight</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</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">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">ndim</span> <span class="o">-</span> <span class="mi">1</span><span class="p">])</span></div> |
| |
| <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">'</span><span class="si">{name}</span><span class="s1">(</span><span class="si">{layout}</span><span class="s1">, </span><span class="si">{act}</span><span class="s1">)'</span> |
| <span class="n">shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</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="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> |
| <span class="n">act</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">act</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">act</span> <span class="k">else</span> <span class="s1">'linear'</span><span class="p">,</span> |
| <span class="n">layout</span><span class="o">=</span><span class="s1">'</span><span class="si">{0}</span><span class="s1"> -> </span><span class="si">{1}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">if</span> <span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> <span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span></div> |
| |
| |
| <div class="viewcode-block" id="Dropout"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.Dropout">[docs]</a><span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">Dropout</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Applies Dropout to the input.</span> |
| |
| <span class="sd"> Dropout consists in randomly setting a fraction `rate` of input units</span> |
| <span class="sd"> to 0 at each update during training time, which helps prevent overfitting.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> rate : float</span> |
| <span class="sd"> Fraction of the input units to drop. Must be a number between 0 and 1.</span> |
| <span class="sd"> axes : tuple of int, default ()</span> |
| <span class="sd"> The axes on which dropout mask is shared. If empty, regular dropout is applied.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: input tensor with arbitrary shape.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: output tensor with the same shape as `data`.</span> |
| |
| <span class="sd"> References</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> `Dropout: A Simple Way to Prevent Neural Networks from Overfitting</span> |
| <span class="sd"> <http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf>`_</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">rate</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="p">(),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Dropout</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="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_rate</span> <span class="o">=</span> <span class="n">rate</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_axes</span> <span class="o">=</span> <span class="n">axes</span> |
| |
| <div class="viewcode-block" id="Dropout.forward"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.Dropout.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rate</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">npx</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_rate</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_axes</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'fwd'</span><span class="p">,</span> <span class="n">cudnn_off</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></div> |
| |
| <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">'</span><span class="si">{name}</span><span class="s1">(p = </span><span class="si">{_rate}</span><span class="s1">, axes=</span><span class="si">{_axes}</span><span class="s1">)'</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="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> |
| <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">)</span></div> |
| |
| |
| <span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">_BatchNorm</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Abstract BatchNorm layer (private, used as implementation base).</span> |
| <span class="sd"> Batch normalization layer (Ioffe and Szegedy, 2014).</span> |
| <span class="sd"> Normalizes the input at each batch, i.e. applies a transformation</span> |
| <span class="sd"> that maintains the mean activation close to 0 and the activation</span> |
| <span class="sd"> standard deviation close to 1.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> axis : int, default 1</span> |
| <span class="sd"> The axis that should be normalized. This is typically the channels</span> |
| <span class="sd"> (C) axis. For instance, after a `Conv2D` layer with `layout='NCHW'`,</span> |
| <span class="sd"> set `axis=1` in `BatchNorm`. If `layout='NHWC'`, then set `axis=3`.</span> |
| <span class="sd"> momentum: float, default 0.9</span> |
| <span class="sd"> Momentum for the moving average.</span> |
| <span class="sd"> epsilon: float, default 1e-5</span> |
| <span class="sd"> Small float added to variance to avoid dividing by zero.</span> |
| <span class="sd"> center: bool, default True</span> |
| <span class="sd"> If True, add offset of `beta` to normalized tensor.</span> |
| <span class="sd"> If False, `beta` is ignored.</span> |
| <span class="sd"> scale: bool, default True</span> |
| <span class="sd"> If True, multiply by `gamma`. If False, `gamma` is not used.</span> |
| <span class="sd"> When the next layer is linear (also e.g. `nn.relu`),</span> |
| <span class="sd"> this can be disabled since the scaling</span> |
| <span class="sd"> will be done by the next layer.</span> |
| <span class="sd"> use_global_stats: bool, default False</span> |
| <span class="sd"> If True, use global moving statistics instead of local batch-norm. This will force</span> |
| <span class="sd"> change batch-norm into a scale shift operator.</span> |
| <span class="sd"> If False, use local batch-norm.</span> |
| <span class="sd"> beta_initializer: str or `Initializer`, default 'zeros'</span> |
| <span class="sd"> Initializer for the beta weight.</span> |
| <span class="sd"> gamma_initializer: str or `Initializer`, default 'ones'</span> |
| <span class="sd"> Initializer for the gamma weight.</span> |
| <span class="sd"> running_mean_initializer: str or `Initializer`, default 'zeros'</span> |
| <span class="sd"> Initializer for the running mean.</span> |
| <span class="sd"> running_variance_initializer: str or `Initializer`, default 'ones'</span> |
| <span class="sd"> Initializer for the running variance.</span> |
| <span class="sd"> in_channels : int, default 0</span> |
| <span class="sd"> Number of channels (feature maps) in input data. If not specified,</span> |
| <span class="sd"> initialization will be deferred to the first time `forward` is called</span> |
| <span class="sd"> and `in_channels` will be inferred from the shape of input data.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: input tensor with arbitrary shape.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: output tensor with the same shape as `data`.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">use_global_stats</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">beta_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> <span class="n">gamma_initializer</span><span class="o">=</span><span class="s1">'ones'</span><span class="p">,</span> |
| <span class="n">running_mean_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> <span class="n">running_variance_initializer</span><span class="o">=</span><span class="s1">'ones'</span><span class="p">,</span> |
| <span class="n">in_channels</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">_BatchNorm</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="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'axis'</span><span class="p">:</span> <span class="n">axis</span><span class="p">,</span> <span class="s1">'eps'</span><span class="p">:</span> <span class="n">epsilon</span><span class="p">,</span> <span class="s1">'momentum'</span><span class="p">:</span> <span class="n">momentum</span><span class="p">,</span> |
| <span class="s1">'fix_gamma'</span><span class="p">:</span> <span class="ow">not</span> <span class="n">scale</span><span class="p">,</span> <span class="s1">'use_global_stats'</span><span class="p">:</span> <span class="n">use_global_stats</span><span class="p">}</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_axis</span> <span class="o">=</span> <span class="n">axis</span> |
| <span class="k">if</span> <span class="n">in_channels</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">in_channels</span> <span class="o">=</span> <span class="n">in_channels</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s1">'gamma'</span><span class="p">,</span> <span class="n">grad_req</span><span class="o">=</span><span class="s1">'write'</span> <span class="k">if</span> <span class="n">scale</span> <span class="k">else</span> <span class="s1">'null'</span><span class="p">,</span> |
| <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,),</span> <span class="n">init</span><span class="o">=</span><span class="n">gamma_initializer</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">differentiable</span><span class="o">=</span><span class="n">scale</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">beta</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s1">'beta'</span><span class="p">,</span> <span class="n">grad_req</span><span class="o">=</span><span class="s1">'write'</span> <span class="k">if</span> <span class="n">center</span> <span class="k">else</span> <span class="s1">'null'</span><span class="p">,</span> |
| <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,),</span> <span class="n">init</span><span class="o">=</span><span class="n">beta_initializer</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">differentiable</span><span class="o">=</span><span class="n">center</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">running_mean</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s1">'running_mean'</span><span class="p">,</span> <span class="n">grad_req</span><span class="o">=</span><span class="s1">'null'</span><span class="p">,</span> |
| <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">running_mean_initializer</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">differentiable</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">running_var</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s1">'running_var'</span><span class="p">,</span> <span class="n">grad_req</span><span class="o">=</span><span class="s1">'null'</span><span class="p">,</span> |
| <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">running_variance_initializer</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">differentiable</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| |
| <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="k">if</span> <span class="n">get_dtype_name</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span> <span class="o">==</span> <span class="s1">'float16'</span><span class="p">:</span> |
| <span class="n">dtype</span> <span class="o">=</span> <span class="s1">'float32'</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">_BatchNorm</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</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="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
| <span class="n">device</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">device</span> |
| <span class="k">return</span> <span class="n">npx</span><span class="o">.</span><span class="n">batch_norm</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">running_mean</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">running_var</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> |
| <span class="n">name</span><span class="o">=</span><span class="s1">'fwd'</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">infer_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span> |
| <span class="n">channel_axis</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_axis</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_axis</span> <span class="o">>=</span> <span class="mi">0</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">_axis</span> <span class="o">+</span> <span class="n">x</span><span class="o">.</span><span class="n">ndim</span> |
| <span class="n">channel_count</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">channel_axis</span><span class="p">]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">channel_count</span><span class="p">,)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">beta</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">channel_count</span><span class="p">,)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">running_mean</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">channel_count</span><span class="p">,)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">running_var</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">channel_count</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">'</span><span class="si">{name}</span><span class="s1">(</span><span class="si">{content}</span><span class="s1">'</span> |
| <span class="n">in_channels</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma</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">s</span> <span class="o">+=</span> <span class="s1">', in_channels=</span><span class="si">{0}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">in_channels</span> <span class="k">if</span> <span class="n">in_channels</span> <span class="k">else</span> <span class="kc">None</span><span class="p">)</span> |
| <span class="n">s</span> <span class="o">+=</span> <span class="s1">')'</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="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> |
| <span class="n">content</span><span class="o">=</span><span class="s1">', '</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">'='</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="o">.</span><span class="fm">__repr__</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="bp">self</span><span class="o">.</span><span class="n">_kwargs</span><span class="o">.</span><span class="n">items</span><span class="p">()]))</span> |
| |
| <div class="viewcode-block" id="BatchNorm"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.BatchNorm">[docs]</a><span class="k">class</span> <span class="nc">BatchNorm</span><span class="p">(</span><span class="n">_BatchNorm</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Batch normalization layer (Ioffe and Szegedy, 2014).</span> |
| <span class="sd"> Normalizes the input at each batch, i.e. applies a transformation</span> |
| <span class="sd"> that maintains the mean activation close to 0 and the activation</span> |
| <span class="sd"> standard deviation close to 1.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> axis : int, default 1</span> |
| <span class="sd"> The axis that should be normalized. This is typically the channels</span> |
| <span class="sd"> (C) axis. For instance, after a `Conv2D` layer with `layout='NCHW'`,</span> |
| <span class="sd"> set `axis=1` in `BatchNorm`. If `layout='NHWC'`, then set `axis=3`.</span> |
| <span class="sd"> momentum: float, default 0.9</span> |
| <span class="sd"> Momentum for the moving average.</span> |
| <span class="sd"> epsilon: float, default 1e-5</span> |
| <span class="sd"> Small float added to variance to avoid dividing by zero.</span> |
| <span class="sd"> center: bool, default True</span> |
| <span class="sd"> If True, add offset of `beta` to normalized tensor.</span> |
| <span class="sd"> If False, `beta` is ignored.</span> |
| <span class="sd"> scale: bool, default True</span> |
| <span class="sd"> If True, multiply by `gamma`. If False, `gamma` is not used.</span> |
| <span class="sd"> When the next layer is linear (also e.g. `nn.relu`),</span> |
| <span class="sd"> this can be disabled since the scaling</span> |
| <span class="sd"> will be done by the next layer.</span> |
| <span class="sd"> use_global_stats: bool, default False</span> |
| <span class="sd"> If True, use global moving statistics instead of local batch-norm. This will force</span> |
| <span class="sd"> change batch-norm into a scale shift operator.</span> |
| <span class="sd"> If False, use local batch-norm.</span> |
| <span class="sd"> beta_initializer: str or `Initializer`, default 'zeros'</span> |
| <span class="sd"> Initializer for the beta weight.</span> |
| <span class="sd"> gamma_initializer: str or `Initializer`, default 'ones'</span> |
| <span class="sd"> Initializer for the gamma weight.</span> |
| <span class="sd"> running_mean_initializer: str or `Initializer`, default 'zeros'</span> |
| <span class="sd"> Initializer for the running mean.</span> |
| <span class="sd"> running_variance_initializer: str or `Initializer`, default 'ones'</span> |
| <span class="sd"> Initializer for the running variance.</span> |
| <span class="sd"> in_channels : int, default 0</span> |
| <span class="sd"> Number of channels (feature maps) in input data. If not specified,</span> |
| <span class="sd"> initialization will be deferred to the first time `forward` is called</span> |
| <span class="sd"> and `in_channels` will be inferred from the shape of input data.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: input tensor with arbitrary shape.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: output tensor with the same shape as `data`.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">use_global_stats</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">beta_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> <span class="n">gamma_initializer</span><span class="o">=</span><span class="s1">'ones'</span><span class="p">,</span> |
| <span class="n">running_mean_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> <span class="n">running_variance_initializer</span><span class="o">=</span><span class="s1">'ones'</span><span class="p">,</span> |
| <span class="n">in_channels</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">BatchNorm</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">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="n">momentum</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="n">epsilon</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="n">center</span><span class="p">,</span> |
| <span class="n">scale</span><span class="o">=</span><span class="n">scale</span><span class="p">,</span> |
| <span class="n">use_global_stats</span><span class="o">=</span><span class="n">use_global_stats</span><span class="p">,</span> |
| <span class="n">beta_initializer</span><span class="o">=</span><span class="n">beta_initializer</span><span class="p">,</span> |
| <span class="n">gamma_initializer</span><span class="o">=</span><span class="n">gamma_initializer</span><span class="p">,</span> |
| <span class="n">running_mean_initializer</span><span class="o">=</span><span class="n">running_mean_initializer</span><span class="p">,</span> |
| <span class="n">running_variance_initializer</span><span class="o">=</span><span class="n">running_variance_initializer</span><span class="p">,</span> |
| <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="Embedding"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.Embedding">[docs]</a><span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">Embedding</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""Turns non-negative integers (indexes/tokens) into dense vectors</span> |
| <span class="sd"> of fixed size. eg. [4, 20] -> [[0.25, 0.1], [0.6, -0.2]]</span> |
| |
| <span class="sd"> .. note::</span> |
| <span class="sd"> if `sparse_grad` is set to True, the gradient w.r.t weight will be</span> |
| <span class="sd"> sparse. Only a subset of optimizers support sparse gradients, including SGD,</span> |
| <span class="sd"> AdaGrad and Adam. By default lazy updates is turned on, which may perform</span> |
| <span class="sd"> differently from standard updates. For more details, please check the</span> |
| <span class="sd"> Optimization API at:</span> |
| <span class="sd"> https://mxnet.apache.org/versions/master/api/python/docs/api/optimizer/index.html</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> input_dim : int</span> |
| <span class="sd"> Size of the vocabulary, i.e. maximum integer index + 1.</span> |
| <span class="sd"> output_dim : int</span> |
| <span class="sd"> Dimension of the dense embedding.</span> |
| <span class="sd"> dtype : str or np.dtype, default 'float32'</span> |
| <span class="sd"> Data type of output embeddings.</span> |
| <span class="sd"> weight_initializer : Initializer</span> |
| <span class="sd"> Initializer for the `embeddings` matrix.</span> |
| <span class="sd"> sparse_grad: bool</span> |
| <span class="sd"> If True, gradient w.r.t. weight will be a 'row_sparse' NDArray.</span> |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: (N-1)-D tensor with shape: `(x1, x2, ..., xN-1)`.</span> |
| |
| <span class="sd"> Output:</span> |
| <span class="sd"> - **out**: N-D tensor with shape: `(x1, x2, ..., xN-1, output_dim)`.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_dim</span><span class="p">,</span> <span class="n">output_dim</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'float32'</span><span class="p">,</span> |
| <span class="n">weight_initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sparse_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Embedding</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="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="ow">not</span> <span class="n">sparse_grad</span><span class="p">,</span> <span class="s2">"Currently, sparse feature is not supported in Gluon2.0"</span> |
| <span class="n">grad_stype</span> <span class="o">=</span> <span class="s1">'row_sparse'</span> <span class="k">if</span> <span class="n">sparse_grad</span> <span class="k">else</span> <span class="s1">'default'</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'input_dim'</span><span class="p">:</span> <span class="n">input_dim</span><span class="p">,</span> <span class="s1">'output_dim'</span><span class="p">:</span> <span class="n">output_dim</span><span class="p">,</span> |
| <span class="s1">'dtype'</span><span class="p">:</span> <span class="n">dtype</span><span class="p">,</span> <span class="s1">'sparse_grad'</span><span class="p">:</span> <span class="n">sparse_grad</span><span class="p">}</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s1">'weight'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">input_dim</span><span class="p">,</span> <span class="n">output_dim</span><span class="p">),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">weight_initializer</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">grad_stype</span><span class="o">=</span><span class="n">grad_stype</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="Embedding.forward"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.Embedding.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
| <span class="n">device</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">device</span> |
| <span class="k">return</span> <span class="n">npx</span><span class="o">.</span><span class="n">embedding</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">'fwd'</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span><span class="p">)</span></div> |
| |
| <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">'</span><span class="si">{block_name}</span><span class="s1">(</span><span class="si">{input_dim}</span><span class="s1"> -> </span><span class="si">{output_dim}</span><span class="s1">, </span><span class="si">{dtype}</span><span class="s1">)'</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">block_name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> |
| <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="Flatten"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.Flatten">[docs]</a><span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">Flatten</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""Flattens the input to two dimensional.</span> |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: input tensor with arbitrary shape `(N, x1, x2, ..., xn)`</span> |
| |
| <span class="sd"> Output:</span> |
| <span class="sd"> - **out**: 2D tensor with shape: `(N, x1 \cdot x2 \cdot ... \cdot xn)`</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Flatten</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="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="Flatten.forward"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.Flatten.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">npx</span><span class="o">.</span><span class="n">batch_flatten</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></div> |
| |
| <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="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span></div> |
| |
| |
| <div class="viewcode-block" id="InstanceNorm"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.InstanceNorm">[docs]</a><span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">InstanceNorm</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""</span> |
| <span class="sd"> Applies instance normalization to the n-dimensional input array.</span> |
| <span class="sd"> This operator takes an n-dimensional input array where (n>2) and normalizes</span> |
| <span class="sd"> the input using the following formula:</span> |
| |
| <span class="sd"> .. math::</span> |
| |
| <span class="sd"> \bar{C} = \{i \mid i \neq 0, i \neq axis\}</span> |
| |
| <span class="sd"> out = \frac{x - mean[data, \bar{C}]}{ \sqrt{Var[data, \bar{C}]} + \epsilon}</span> |
| <span class="sd"> * gamma + beta</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> axis : int, default 1</span> |
| <span class="sd"> The axis that will be excluded in the normalization process. This is typically the channels</span> |
| <span class="sd"> (C) axis. For instance, after a `Conv2D` layer with `layout='NCHW'`,</span> |
| <span class="sd"> set `axis=1` in `InstanceNorm`. If `layout='NHWC'`, then set `axis=3`. Data will be</span> |
| <span class="sd"> normalized along axes excluding the first axis and the axis given.</span> |
| <span class="sd"> epsilon: float, default 1e-5</span> |
| <span class="sd"> Small float added to variance to avoid dividing by zero.</span> |
| <span class="sd"> center: bool, default True</span> |
| <span class="sd"> If True, add offset of `beta` to normalized tensor.</span> |
| <span class="sd"> If False, `beta` is ignored.</span> |
| <span class="sd"> scale: bool, default True</span> |
| <span class="sd"> If True, multiply by `gamma`. If False, `gamma` is not used.</span> |
| <span class="sd"> When the next layer is linear (also e.g. `nn.relu`),</span> |
| <span class="sd"> this can be disabled since the scaling</span> |
| <span class="sd"> will be done by the next layer.</span> |
| <span class="sd"> beta_initializer: str or `Initializer`, default 'zeros'</span> |
| <span class="sd"> Initializer for the beta weight.</span> |
| <span class="sd"> gamma_initializer: str or `Initializer`, default 'ones'</span> |
| <span class="sd"> Initializer for the gamma weight.</span> |
| <span class="sd"> in_channels : int, default 0</span> |
| <span class="sd"> Number of channels (feature maps) in input data. If not specified,</span> |
| <span class="sd"> initialization will be deferred to the first time `forward` is called</span> |
| <span class="sd"> and `in_channels` will be inferred from the shape of input data.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: input tensor with arbitrary shape.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: output tensor with the same shape as `data`.</span> |
| |
| <span class="sd"> References</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> `Instance Normalization: The Missing Ingredient for Fast Stylization</span> |
| <span class="sd"> <https://arxiv.org/abs/1607.08022>`_</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> # Input of shape (2,1,2)</span> |
| <span class="sd"> >>> x = mx.np.array([[[ 1.1, 2.2]],</span> |
| <span class="sd"> ... [[ 3.3, 4.4]]])</span> |
| <span class="sd"> >>> # Instance normalization is calculated with the above formula</span> |
| <span class="sd"> >>> layer = InstanceNorm()</span> |
| <span class="sd"> >>> layer.initialize(device=mx.cpu(0))</span> |
| <span class="sd"> >>> layer(x)</span> |
| <span class="sd"> [[[-0.99998355 0.99998331]]</span> |
| <span class="sd"> [[-0.99998319 0.99998361]]]</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">beta_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> <span class="n">gamma_initializer</span><span class="o">=</span><span class="s1">'ones'</span><span class="p">,</span> |
| <span class="n">in_channels</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">InstanceNorm</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="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'eps'</span><span class="p">:</span> <span class="n">epsilon</span><span class="p">,</span> <span class="s1">'axis'</span><span class="p">:</span> <span class="n">axis</span><span class="p">,</span> <span class="s1">'center'</span><span class="p">:</span> <span class="n">center</span><span class="p">,</span> <span class="s1">'scale'</span><span class="p">:</span> <span class="n">scale</span><span class="p">}</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_axis</span> <span class="o">=</span> <span class="n">axis</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_epsilon</span> <span class="o">=</span> <span class="n">epsilon</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s1">'gamma'</span><span class="p">,</span> <span class="n">grad_req</span><span class="o">=</span><span class="s1">'write'</span> <span class="k">if</span> <span class="n">scale</span> <span class="k">else</span> <span class="s1">'null'</span><span class="p">,</span> |
| <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,),</span> <span class="n">init</span><span class="o">=</span><span class="n">gamma_initializer</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">beta</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s1">'beta'</span><span class="p">,</span> <span class="n">grad_req</span><span class="o">=</span><span class="s1">'write'</span> <span class="k">if</span> <span class="n">center</span> <span class="k">else</span> <span class="s1">'null'</span><span class="p">,</span> |
| <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,),</span> <span class="n">init</span><span class="o">=</span><span class="n">beta_initializer</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="InstanceNorm.forward"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.InstanceNorm.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
| <span class="n">device</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">device</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_axis</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">npx</span><span class="o">.</span><span class="n">instance_norm</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> |
| <span class="n">name</span><span class="o">=</span><span class="s1">'fwd'</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_epsilon</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">swapaxes</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_axis</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">npx</span><span class="o">.</span><span class="n">instance_norm</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> |
| <span class="n">name</span><span class="o">=</span><span class="s1">'fwd'</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_epsilon</span><span class="p">)</span><span class="o">.</span><span class="n">swapaxes</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_axis</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="InstanceNorm.infer_shape"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.InstanceNorm.infer_shape">[docs]</a> <span class="k">def</span> <span class="nf">infer_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">beta</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],)</span></div> |
| |
| <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">'</span><span class="si">{name}</span><span class="s1">(</span><span class="si">{content}</span><span class="s1">'</span> |
| <span class="n">in_channels</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma</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">s</span> <span class="o">+=</span> <span class="s1">', in_channels=</span><span class="si">{0}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">in_channels</span><span class="p">)</span> |
| <span class="n">s</span> <span class="o">+=</span> <span class="s1">')'</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="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> |
| <span class="n">content</span><span class="o">=</span><span class="s1">', '</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">'='</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="o">.</span><span class="fm">__repr__</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="bp">self</span><span class="o">.</span><span class="n">_kwargs</span><span class="o">.</span><span class="n">items</span><span class="p">()]))</span></div> |
| |
| |
| <div class="viewcode-block" id="LayerNorm"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.LayerNorm">[docs]</a><span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">LayerNorm</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""</span> |
| <span class="sd"> Applies layer normalization to the n-dimensional input array.</span> |
| <span class="sd"> This operator takes an n-dimensional input array and normalizes</span> |
| <span class="sd"> the input using the given axis:</span> |
| |
| <span class="sd"> .. math::</span> |
| |
| <span class="sd"> out = \frac{x - mean[data, axis]}{ \sqrt{Var[data, axis] + \epsilon}} * gamma + beta</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> axis : int, default -1</span> |
| <span class="sd"> The axis that should be normalized. This is typically the axis of the channels.</span> |
| <span class="sd"> epsilon: float, default 1e-5</span> |
| <span class="sd"> Small float added to variance to avoid dividing by zero.</span> |
| <span class="sd"> center: bool, default True</span> |
| <span class="sd"> If True, add offset of `beta` to normalized tensor.</span> |
| <span class="sd"> If False, `beta` is ignored.</span> |
| <span class="sd"> scale: bool, default True</span> |
| <span class="sd"> If True, multiply by `gamma`. If False, `gamma` is not used.</span> |
| <span class="sd"> beta_initializer: str or `Initializer`, default 'zeros'</span> |
| <span class="sd"> Initializer for the beta weight.</span> |
| <span class="sd"> gamma_initializer: str or `Initializer`, default 'ones'</span> |
| <span class="sd"> Initializer for the gamma weight.</span> |
| <span class="sd"> in_channels : int, default 0</span> |
| <span class="sd"> Number of channels (feature maps) in input data. If not specified,</span> |
| <span class="sd"> initialization will be deferred to the first time `forward` is called</span> |
| <span class="sd"> and `in_channels` will be inferred from the shape of input data.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: input tensor with arbitrary shape.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: output tensor with the same shape as `data`.</span> |
| |
| <span class="sd"> References</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> `Layer Normalization</span> |
| <span class="sd"> <https://arxiv.org/pdf/1607.06450.pdf>`_</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> # Input of shape (2, 5)</span> |
| <span class="sd"> >>> x = mx.np.array([[1, 2, 3, 4, 5], [1, 1, 2, 2, 2]])</span> |
| <span class="sd"> >>> # Layer normalization is calculated with the above formula</span> |
| <span class="sd"> >>> layer = LayerNorm()</span> |
| <span class="sd"> >>> layer.initialize(device=mx.cpu(0))</span> |
| <span class="sd"> >>> layer(x)</span> |
| <span class="sd"> [[-1.41421 -0.707105 0. 0.707105 1.41421 ]</span> |
| <span class="sd"> [-1.2247195 -1.2247195 0.81647956 0.81647956 0.81647956]]</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">beta_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> <span class="n">gamma_initializer</span><span class="o">=</span><span class="s1">'ones'</span><span class="p">,</span> |
| <span class="n">in_channels</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">LayerNorm</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="bp">self</span><span class="o">.</span><span class="n">_kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'eps'</span><span class="p">:</span> <span class="n">epsilon</span><span class="p">,</span> <span class="s1">'axis'</span><span class="p">:</span> <span class="n">axis</span><span class="p">,</span> <span class="s1">'center'</span><span class="p">:</span> <span class="n">center</span><span class="p">,</span> <span class="s1">'scale'</span><span class="p">:</span> <span class="n">scale</span><span class="p">}</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_axis</span> <span class="o">=</span> <span class="n">axis</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_epsilon</span> <span class="o">=</span> <span class="n">epsilon</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_center</span> <span class="o">=</span> <span class="n">center</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_scale</span> <span class="o">=</span> <span class="n">scale</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s1">'gamma'</span><span class="p">,</span> <span class="n">grad_req</span><span class="o">=</span><span class="s1">'write'</span> <span class="k">if</span> <span class="n">scale</span> <span class="k">else</span> <span class="s1">'null'</span><span class="p">,</span> |
| <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,),</span> <span class="n">init</span><span class="o">=</span><span class="n">gamma_initializer</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">beta</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s1">'beta'</span><span class="p">,</span> <span class="n">grad_req</span><span class="o">=</span><span class="s1">'write'</span> <span class="k">if</span> <span class="n">center</span> <span class="k">else</span> <span class="s1">'null'</span><span class="p">,</span> |
| <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,),</span> <span class="n">init</span><span class="o">=</span><span class="n">beta_initializer</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="LayerNorm.forward"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.LayerNorm.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</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">device</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">device</span> |
| <span class="k">return</span> <span class="n">npx</span><span class="o">.</span><span class="n">layer_norm</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">gamma</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> |
| <span class="n">beta</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">beta</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_axis</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_epsilon</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="LayerNorm.infer_shape"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.LayerNorm.infer_shape">[docs]</a> <span class="k">def</span> <span class="nf">infer_shape</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="o">*</span><span class="n">args</span><span class="p">):</span> |
| <span class="n">channel_axis</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_axis</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_axis</span> <span class="o">>=</span> <span class="mi">0</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">_axis</span> <span class="o">+</span> <span class="n">data</span><span class="o">.</span><span class="n">ndim</span> |
| <span class="n">channel_count</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">channel_axis</span><span class="p">]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">channel_count</span><span class="p">,)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">beta</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">channel_count</span><span class="p">,)</span></div> |
| |
| <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">'</span><span class="si">{name}</span><span class="s1">(</span><span class="si">{content}</span><span class="s1">'</span> |
| <span class="n">in_channels</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma</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">s</span> <span class="o">+=</span> <span class="s1">', in_channels=</span><span class="si">{0}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">in_channels</span><span class="p">)</span> |
| <span class="n">s</span> <span class="o">+=</span> <span class="s1">')'</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="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> |
| <span class="n">content</span><span class="o">=</span><span class="s1">', '</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">'='</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="o">.</span><span class="fm">__repr__</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="bp">self</span><span class="o">.</span><span class="n">_kwargs</span><span class="o">.</span><span class="n">items</span><span class="p">()]))</span></div> |
| |
| |
| <div class="viewcode-block" id="GroupNorm"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.GroupNorm">[docs]</a><span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">GroupNorm</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""</span> |
| <span class="sd"> Applies group normalization to the n-dimensional input array.</span> |
| <span class="sd"> This operator takes an n-dimensional input array where the leftmost 2 axis are</span> |
| <span class="sd"> `batch` and `channel` respectively:</span> |
| |
| <span class="sd"> .. math::</span> |
| |
| <span class="sd"> x = x.reshape((N, num_groups, C // num_groups, ...))</span> |
| <span class="sd"> axis = (2, ...)</span> |
| <span class="sd"> out = \frac{x - mean[x, axis]}{ \sqrt{Var[x, axis] + \epsilon}} * gamma + beta</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> num_groups: int, default 1</span> |
| <span class="sd"> Number of groups to separate the channel axis into.</span> |
| <span class="sd"> epsilon: float, default 1e-5</span> |
| <span class="sd"> Small float added to variance to avoid dividing by zero.</span> |
| <span class="sd"> center: bool, default True</span> |
| <span class="sd"> If True, add offset of `beta` to normalized tensor.</span> |
| <span class="sd"> If False, `beta` is ignored.</span> |
| <span class="sd"> scale: bool, default True</span> |
| <span class="sd"> If True, multiply by `gamma`. If False, `gamma` is not used.</span> |
| <span class="sd"> beta_initializer: str or `Initializer`, default 'zeros'</span> |
| <span class="sd"> Initializer for the beta weight.</span> |
| <span class="sd"> gamma_initializer: str or `Initializer`, default 'ones'</span> |
| <span class="sd"> Initializer for the gamma weight.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: input tensor with shape (N, C, ...).</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: output tensor with the same shape as `data`.</span> |
| |
| <span class="sd"> References</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> `Group Normalization</span> |
| <span class="sd"> <https://arxiv.org/pdf/1803.08494.pdf>`_</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> # Input of shape (2, 3, 4)</span> |
| <span class="sd"> >>> x = mx.np.array([[[ 0, 1, 2, 3],</span> |
| <span class="sd"> [ 4, 5, 6, 7],</span> |
| <span class="sd"> [ 8, 9, 10, 11]],</span> |
| <span class="sd"> [[12, 13, 14, 15],</span> |
| <span class="sd"> [16, 17, 18, 19],</span> |
| <span class="sd"> [20, 21, 22, 23]]])</span> |
| <span class="sd"> >>> # Group normalization is calculated with the above formula</span> |
| <span class="sd"> >>> layer = GroupNorm()</span> |
| <span class="sd"> >>> layer.initialize(device=mx.cpu(0))</span> |
| <span class="sd"> >>> layer(x)</span> |
| <span class="sd"> [[[-1.5932543 -1.3035717 -1.0138891 -0.7242065]</span> |
| <span class="sd"> [-0.4345239 -0.1448413 0.1448413 0.4345239]</span> |
| <span class="sd"> [ 0.7242065 1.0138891 1.3035717 1.5932543]]</span> |
| <span class="sd"> [[-1.5932543 -1.3035717 -1.0138891 -0.7242065]</span> |
| <span class="sd"> [-0.4345239 -0.1448413 0.1448413 0.4345239]</span> |
| <span class="sd"> [ 0.7242065 1.0138891 1.3035717 1.5932543]]]</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_groups</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">beta_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> <span class="n">gamma_initializer</span><span class="o">=</span><span class="s1">'ones'</span><span class="p">,</span> |
| <span class="n">in_channels</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">GroupNorm</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="bp">self</span><span class="o">.</span><span class="n">_kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'eps'</span><span class="p">:</span> <span class="n">epsilon</span><span class="p">,</span> <span class="s1">'num_groups'</span><span class="p">:</span> <span class="n">num_groups</span><span class="p">,</span> <span class="s1">'center'</span><span class="p">:</span> <span class="n">center</span><span class="p">,</span> <span class="s1">'scale'</span><span class="p">:</span> <span class="n">scale</span><span class="p">}</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_num_groups</span> <span class="o">=</span> <span class="n">num_groups</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_epsilon</span> <span class="o">=</span> <span class="n">epsilon</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_center</span> <span class="o">=</span> <span class="n">center</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_scale</span> <span class="o">=</span> <span class="n">scale</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s1">'gamma'</span><span class="p">,</span> <span class="n">grad_req</span><span class="o">=</span><span class="s1">'write'</span> <span class="k">if</span> <span class="n">scale</span> <span class="k">else</span> <span class="s1">'null'</span><span class="p">,</span> |
| <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,),</span> <span class="n">init</span><span class="o">=</span><span class="n">gamma_initializer</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">beta</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s1">'beta'</span><span class="p">,</span> <span class="n">grad_req</span><span class="o">=</span><span class="s1">'write'</span> <span class="k">if</span> <span class="n">center</span> <span class="k">else</span> <span class="s1">'null'</span><span class="p">,</span> |
| <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,),</span> <span class="n">init</span><span class="o">=</span><span class="n">beta_initializer</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="GroupNorm.forward"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.GroupNorm.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</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">device</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">device</span> |
| <span class="n">norm_data</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">group_norm</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">gamma</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="n">beta</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">beta</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> |
| <span class="n">num_groups</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_groups</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_epsilon</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">norm_data</span></div> |
| |
| <div class="viewcode-block" id="GroupNorm.infer_shape"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.GroupNorm.infer_shape">[docs]</a> <span class="k">def</span> <span class="nf">infer_shape</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="o">*</span><span class="n">args</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</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="mi">1</span><span class="p">],)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">beta</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</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="mi">1</span><span class="p">],)</span></div> |
| |
| <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">'</span><span class="si">{name}</span><span class="s1">(</span><span class="si">{content}</span><span class="s1">'</span> |
| <span class="n">in_channels</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma</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">s</span> <span class="o">+=</span> <span class="s1">', in_channels=</span><span class="si">{0}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">in_channels</span><span class="p">)</span> |
| <span class="n">s</span> <span class="o">+=</span> <span class="s1">')'</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="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> |
| <span class="n">content</span><span class="o">=</span><span class="s1">', '</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">'='</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="o">.</span><span class="fm">__repr__</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="bp">self</span><span class="o">.</span><span class="n">_kwargs</span><span class="o">.</span><span class="n">items</span><span class="p">()]))</span></div> |
| |
| |
| <div class="viewcode-block" id="Lambda"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.Lambda">[docs]</a><span class="k">class</span> <span class="nc">Lambda</span><span class="p">(</span><span class="n">Block</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""Wraps an operator or an expression as a Block object.</span> |
| |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> function : str or function</span> |
| <span class="sd"> Function used in lambda must be one of the following:</span> |
| <span class="sd"> 1) the name of an operator that is available in ndarray. For example::</span> |
| |
| <span class="sd"> block = Lambda('tanh')</span> |
| |
| <span class="sd"> 2) a function that conforms to ``def function(*args)``. For example::</span> |
| |
| <span class="sd"> block = Lambda(lambda x: npx.leaky_relu(x, slope=0.1))</span> |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - ** *args **: one or more input data. Their shapes depend on the function.</span> |
| |
| <span class="sd"> Output:</span> |
| <span class="sd"> - ** *outputs **: one or more output data. Their shapes depend on the function.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">function</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Lambda</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="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">function</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">np</span><span class="p">,</span> <span class="n">function</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_func_impl</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">np</span><span class="p">,</span> <span class="n">function</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">npx</span><span class="p">,</span> <span class="n">function</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_func_impl</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">npx</span><span class="p">,</span> <span class="n">function</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Function name </span><span class="si">{</span><span class="n">function</span><span class="si">}</span><span class="s2"> is not found in np/npx."</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_func_name</span> <span class="o">=</span> <span class="n">function</span> |
| <span class="k">elif</span> <span class="n">callable</span><span class="p">(</span><span class="n">function</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_func_impl</span> <span class="o">=</span> <span class="n">function</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">"Unrecognized function in lambda: </span><span class="si">{}</span><span class="s2"> of type </span><span class="si">{}</span><span class="s2">"</span> |
| <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">function</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">function</span><span class="p">)))</span> |
| |
| <div class="viewcode-block" id="Lambda.forward"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.Lambda.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_func_impl</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span></div> |
| |
| <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="k">return</span> <span class="s1">'</span><span class="si">{name}</span><span class="s1">(</span><span class="si">{function}</span><span class="s1">)'</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="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> |
| <span class="n">function</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_func_impl</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="HybridLambda"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.HybridLambda">[docs]</a><span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">HybridLambda</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""Wraps an operator or an expression as a HybridBlock object.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> function : str or function</span> |
| <span class="sd"> Function used in lambda must be one of the following:</span> |
| <span class="sd"> 1) The name of an operator that is available in both symbol and ndarray. For example::</span> |
| |
| <span class="sd"> block = HybridLambda('tanh')</span> |
| |
| <span class="sd"> 2) A function that conforms to ``def function(F, data, *args)``. For example::</span> |
| |
| <span class="sd"> block = HybridLambda(lambda F, x: F.LeakyReLU(x, slope=0.1))</span> |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - ** *args **: one or more input data. First argument must be symbol or ndarray. Their \</span> |
| <span class="sd"> shapes depend on the function.</span> |
| |
| <span class="sd"> Output:</span> |
| <span class="sd"> - ** *outputs **: one or more output data. Their shapes depend on the function.</span> |
| |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">function</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">HybridLambda</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="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">function</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">np</span><span class="p">,</span> <span class="n">function</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_func</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">np</span><span class="p">,</span> <span class="n">function</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">npx</span><span class="p">,</span> <span class="n">function</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_func</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">npx</span><span class="p">,</span> <span class="n">function</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Function name </span><span class="si">{</span><span class="n">function</span><span class="si">}</span><span class="s2"> is not found in np/npx."</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_func_name</span> <span class="o">=</span> <span class="n">function</span> |
| <span class="k">elif</span> <span class="n">callable</span><span class="p">(</span><span class="n">function</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_func</span> <span class="o">=</span> <span class="n">function</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_func_name</span> <span class="o">=</span> <span class="n">function</span><span class="o">.</span><span class="vm">__name__</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">"Unrecognized function in lambda: </span><span class="si">{}</span><span class="s2"> of type </span><span class="si">{}</span><span class="s2">"</span> |
| <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">function</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">function</span><span class="p">)))</span> |
| |
| <div class="viewcode-block" id="HybridLambda.forward"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.HybridLambda.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_func</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span></div> |
| |
| <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="k">return</span> <span class="s1">'</span><span class="si">{name}</span><span class="s1">(</span><span class="si">{function}</span><span class="s1">)'</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="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> |
| <span class="n">function</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_func_name</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="Concatenate"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.Concatenate">[docs]</a><span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">Concatenate</span><span class="p">(</span><span class="n">Sequential</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Lays `Block` s concurrently.</span> |
| |
| <span class="sd"> This block feeds its input to all children blocks, and</span> |
| <span class="sd"> produce the output by concatenating all the children blocks' outputs</span> |
| <span class="sd"> on the specified axis.</span> |
| |
| <span class="sd"> Example::</span> |
| |
| <span class="sd"> net = Concatenate()</span> |
| <span class="sd"> net.add(nn.Dense(10, activation='relu'))</span> |
| <span class="sd"> net.add(nn.Dense(20))</span> |
| <span class="sd"> net.add(Identity())</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> axis : int, default -1</span> |
| <span class="sd"> The axis on which to concatenate the outputs.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Concatenate</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="bp">self</span><span class="o">.</span><span class="n">axis</span> <span class="o">=</span> <span class="n">axis</span> |
| |
| <div class="viewcode-block" id="Concatenate.forward"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.Concatenate.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">block</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">block</span><span class="p">()(</span><span class="n">x</span><span class="p">))</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">axis</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">out</span></div></div> |
| |
| |
| <div class="viewcode-block" id="HybridConcatenate"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.HybridConcatenate">[docs]</a><span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">HybridConcatenate</span><span class="p">(</span><span class="n">HybridSequential</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Lays `HybridBlock` s concurrently.</span> |
| |
| <span class="sd"> This block feeds its input to all children blocks, and</span> |
| <span class="sd"> produce the output by concatenating all the children blocks' outputs</span> |
| <span class="sd"> on the specified axis.</span> |
| |
| <span class="sd"> Example::</span> |
| |
| <span class="sd"> net = HybridConcatenate()</span> |
| <span class="sd"> net.add(nn.Dense(10, activation='relu'))</span> |
| <span class="sd"> net.add(nn.Dense(20))</span> |
| <span class="sd"> net.add(Identity())</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> axis : int, default -1</span> |
| <span class="sd"> The axis on which to concatenate the outputs.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">axis</span> <span class="o">=</span> <span class="n">axis</span> |
| |
| <div class="viewcode-block" id="HybridConcatenate.forward"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.HybridConcatenate.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">block</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">block</span><span class="p">()(</span><span class="n">x</span><span class="p">))</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">axis</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">out</span></div></div> |
| |
| |
| <div class="viewcode-block" id="Identity"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.Identity">[docs]</a><span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">Identity</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Block that passes through the input directly.</span> |
| |
| <span class="sd"> This block can be used in conjunction with HybridConcatenate</span> |
| <span class="sd"> block for residual connection.</span> |
| |
| <span class="sd"> Example::</span> |
| |
| <span class="sd"> net = HybridConcatenate()</span> |
| <span class="sd"> net.add(nn.Dense(10, activation='relu'))</span> |
| <span class="sd"> net.add(nn.Dense(20))</span> |
| <span class="sd"> net.add(Identity())</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Identity</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> |
| |
| <div class="viewcode-block" id="Identity.forward"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.Identity.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">x</span></div></div> |
| |
| |
| <div class="viewcode-block" id="SyncBatchNorm"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.SyncBatchNorm">[docs]</a><span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">SyncBatchNorm</span><span class="p">(</span><span class="n">BatchNorm</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Cross-GPU Synchronized Batch normalization (SyncBN)</span> |
| |
| <span class="sd"> Standard BN [1]_ implementation only normalize the data within each device.</span> |
| <span class="sd"> SyncBN normalizes the input within the whole mini-batch.</span> |
| <span class="sd"> We follow the implementation described in the paper [2]_.</span> |
| |
| <span class="sd"> Note: Current implementation of SyncBN does not support FP16 training.</span> |
| <span class="sd"> For FP16 inference, use standard nn.BatchNorm instead of SyncBN.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> in_channels : int, default 0</span> |
| <span class="sd"> Number of channels (feature maps) in input data. If not specified,</span> |
| <span class="sd"> initialization will be deferred to the first time `forward` is called</span> |
| <span class="sd"> and `in_channels` will be inferred from the shape of input data.</span> |
| <span class="sd"> num_devices : int, default number of visible GPUs</span> |
| <span class="sd"> momentum: float, default 0.9</span> |
| <span class="sd"> Momentum for the moving average.</span> |
| <span class="sd"> epsilon: float, default 1e-5</span> |
| <span class="sd"> Small float added to variance to avoid dividing by zero.</span> |
| <span class="sd"> center: bool, default True</span> |
| <span class="sd"> If True, add offset of `beta` to normalized tensor.</span> |
| <span class="sd"> If False, `beta` is ignored.</span> |
| <span class="sd"> scale: bool, default True</span> |
| <span class="sd"> If True, multiply by `gamma`. If False, `gamma` is not used.</span> |
| <span class="sd"> When the next layer is linear (also e.g. `nn.relu`),</span> |
| <span class="sd"> this can be disabled since the scaling</span> |
| <span class="sd"> will be done by the next layer.</span> |
| <span class="sd"> use_global_stats: bool, default False</span> |
| <span class="sd"> If True, use global moving statistics instead of local batch-norm. This will force</span> |
| <span class="sd"> change batch-norm into a scale shift operator.</span> |
| <span class="sd"> If False, use local batch-norm.</span> |
| <span class="sd"> beta_initializer: str or `Initializer`, default 'zeros'</span> |
| <span class="sd"> Initializer for the beta weight.</span> |
| <span class="sd"> gamma_initializer: str or `Initializer`, default 'ones'</span> |
| <span class="sd"> Initializer for the gamma weight.</span> |
| <span class="sd"> running_mean_initializer: str or `Initializer`, default 'zeros'</span> |
| <span class="sd"> Initializer for the running mean.</span> |
| <span class="sd"> running_variance_initializer: str or `Initializer`, default 'ones'</span> |
| <span class="sd"> Initializer for the running variance.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: input tensor with arbitrary shape.</span> |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: output tensor with the same shape as `data`.</span> |
| |
| <span class="sd"> Reference:</span> |
| <span class="sd"> .. [1] Ioffe, Sergey, and Christian Szegedy. "Batch normalization: Accelerating \</span> |
| <span class="sd"> deep network training by reducing internal covariate shift." *ICML 2015*</span> |
| <span class="sd"> .. [2] Hang Zhang, Kristin Dana, Jianping Shi, Zhongyue Zhang, Xiaogang Wang, \</span> |
| <span class="sd"> Ambrish Tyagi, and Amit Agrawal. "Context Encoding for Semantic Segmentation." *CVPR 2018*</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_devices</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> |
| <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">use_global_stats</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">beta_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> |
| <span class="n">gamma_initializer</span><span class="o">=</span><span class="s1">'ones'</span><span class="p">,</span> <span class="n">running_mean_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> |
| <span class="n">running_variance_initializer</span><span class="o">=</span><span class="s1">'ones'</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">SyncBatchNorm</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">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="n">momentum</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="n">epsilon</span><span class="p">,</span> |
| <span class="n">center</span><span class="o">=</span><span class="n">center</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="n">scale</span><span class="p">,</span> |
| <span class="n">use_global_stats</span><span class="o">=</span><span class="n">use_global_stats</span><span class="p">,</span> |
| <span class="n">beta_initializer</span><span class="o">=</span><span class="n">beta_initializer</span><span class="p">,</span> |
| <span class="n">gamma_initializer</span><span class="o">=</span><span class="n">gamma_initializer</span><span class="p">,</span> |
| <span class="n">running_mean_initializer</span><span class="o">=</span><span class="n">running_mean_initializer</span><span class="p">,</span> |
| <span class="n">running_variance_initializer</span><span class="o">=</span><span class="n">running_variance_initializer</span><span class="p">,</span> |
| <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="n">num_devices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_num_devices</span><span class="p">()</span> <span class="k">if</span> <span class="n">num_devices</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">num_devices</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'eps'</span><span class="p">:</span> <span class="n">epsilon</span><span class="p">,</span> <span class="s1">'momentum'</span><span class="p">:</span> <span class="n">momentum</span><span class="p">,</span> |
| <span class="s1">'fix_gamma'</span><span class="p">:</span> <span class="ow">not</span> <span class="n">scale</span><span class="p">,</span> <span class="s1">'use_global_stats'</span><span class="p">:</span> <span class="n">use_global_stats</span><span class="p">,</span> |
| <span class="s1">'ndev'</span><span class="p">:</span> <span class="n">num_devices</span><span class="p">,</span> <span class="s1">'key'</span><span class="p">:</span> <span class="n">uuid</span><span class="o">.</span><span class="n">uuid4</span><span class="p">()}</span> |
| |
| <span class="k">def</span> <span class="nf">_get_num_devices</span><span class="p">(</span><span class="bp">self</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">"Caution using SyncBatchNorm: "</span> |
| <span class="s2">"if not using all the GPUs, please mannually set num_devices"</span><span class="p">,</span> |
| <span class="ne">UserWarning</span><span class="p">)</span> |
| <span class="n">num_devices</span> <span class="o">=</span> <span class="n">_device</span><span class="o">.</span><span class="n">num_gpus</span><span class="p">()</span> |
| <span class="n">num_devices</span> <span class="o">=</span> <span class="n">num_devices</span> <span class="k">if</span> <span class="n">num_devices</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">1</span> |
| <span class="k">return</span> <span class="n">num_devices</span> |
| |
| <div class="viewcode-block" id="SyncBatchNorm.forward"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.SyncBatchNorm.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
| <span class="n">device</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">device</span> |
| <span class="k">return</span> <span class="n">npx</span><span class="o">.</span><span class="n">sync_batch_norm</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">running_mean</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">running_var</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> |
| <span class="n">name</span><span class="o">=</span><span class="s1">'fwd'</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span><span class="p">)</span></div></div> |
| </pre></div> |
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