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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1 current"><a class="reference internal" href="../../../index.html">Python Tutorials</a><ul class="current"> |
| <li class="toctree-l2"><a class="reference internal" href="../../../getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../getting-started/crash-course/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../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="../../../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="../../../getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../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="../../../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="../../../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="../../../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="../../../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="../../../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="../../../getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../packages/autograd/index.html">Automatic Differentiation</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../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="../../../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="../../../packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../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="../../../packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
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| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../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="../../../packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
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| <li class="toctree-l6"><a class="reference internal" href="../../../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="../../../packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../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> |
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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.matrix_power.html">mxnet.np.linalg.matrix_power</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.svd.html">mxnet.np.linalg.svd</a></li> |
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| <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> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../../api/np/routines.math.html">Mathematical functions</a><ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.cos.html">mxnet.np.cos</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> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nansum.html">mxnet.np.nansum</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.ediff1d.html">mxnet.np.ediff1d</a></li> |
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| <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> |
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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> |
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| <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> |
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| <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> |
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| <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> |
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| <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> |
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| <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> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../../api/np/routines.sort.html">Sorting, searching, and counting</a><ul> |
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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> |
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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-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> |
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| <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.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> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.median.html">mxnet.np.median</a></li> |
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| <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> |
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| <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> |
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| <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.cpu.html">mxnet.npx.cpu</a></li> |
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| <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> |
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| <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> |
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| <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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| <li class="toctree-l1 current"><a class="reference internal" href="../../../index.html">Python Tutorials</a><ul class="current"> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../getting-started/crash-course/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../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="../../../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="../../../getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../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="../../../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="../../../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="../../../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="../../../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="../../../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="../../../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="../../../getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../getting-started/gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
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| <li class="toctree-l2"><a class="reference internal" href="../../../packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../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="../../../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="../../../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="../../../packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../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="../../../packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
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| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../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="../../../packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</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.matrix_power.html">mxnet.np.linalg.matrix_power</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.svd.html">mxnet.np.linalg.svd</a></li> |
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| <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> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.cond.html">mxnet.np.linalg.cond</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../../api/np/routines.math.html">Mathematical functions</a><ul> |
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| <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> |
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| <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> |
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| <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> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.logistic.html">mxnet.np.random.logistic</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.multinomial.html">mxnet.np.random.multinomial</a></li> |
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| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../api/np/routines.sort.html">Sorting, searching, and counting</a><ul> |
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| <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> |
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| <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.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> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.median.html">mxnet.np.median</a></li> |
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| <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> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.bincount.html">mxnet.np.bincount</a></li> |
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| </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.cpu.html">mxnet.npx.cpu</a></li> |
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| <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> |
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| |
| <!--- Licensed to the Apache Software Foundation (ASF) under one --><!--- or more contributor license agreements. See the NOTICE file --><!--- distributed with this work for additional information --><!--- regarding copyright ownership. The ASF licenses this file --><!--- to you under the Apache License, Version 2.0 (the --><!--- "License"); you may not use this file except in compliance --><!--- with the License. You may obtain a copy of the License at --><!--- http://www.apache.org/licenses/LICENSE-2.0 --><!--- Unless required by applicable law or agreed to in writing, --><!--- software distributed under the License is distributed on an --><!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY --><!--- KIND, either express or implied. See the License for the --><!--- specific language governing permissions and limitations --><!--- under the License. --><div class="section" id="Improving-accuracy-with-Intel®-Neural-Compressor"> |
| <h1>Improving accuracy with Intel® Neural Compressor<a class="headerlink" href="#Improving-accuracy-with-Intel®-Neural-Compressor" title="Permalink to this headline">¶</a></h1> |
| <p>The accuracy of a model can decrease as a result of quantization. When the accuracy drop is significant, we can try to manually find a better quantization configuration (exclude some layers, try different calibration methods, etc.), but for bigger models this might prove to be a difficult and time consuming task. <a class="reference external" href="https://github.com/intel/neural-compressor">Intel® Neural Compressor</a> (INC) tries to automate this process using several tuning heuristics, which aim to find the quantization |
| configuration that satisfies the specified accuracy requirement.</p> |
| <p><strong>NOTE:</strong></p> |
| <p>Most tuning strategies will try different configurations on an evaluation dataset in order to find out how each layer affects the accuracy of the model. This means that for larger models, it may take a long time to find a solution (as the tuning space is usually larger and the evaluation itself takes longer).</p> |
| <div class="section" id="Installation-and-Prerequisites"> |
| <h2>Installation and Prerequisites<a class="headerlink" href="#Installation-and-Prerequisites" title="Permalink to this headline">¶</a></h2> |
| <ul> |
| <li><p>Install MXNet with oneDNN enabled as described in the <a class="reference external" href="https://mxnet.apache.org/versions/master/get_started?platform=linux&language=python&processor=cpu&environ=pip&">Get started</a>. (Until the 2.0 release you can use the nightly build version: <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">--pre</span> <span class="pre">mxnet</span> <span class="pre">-f</span> <span class="pre">https://dist.mxnet.io/python</span></code>)</p></li> |
| <li><p>Install Intel® Neural Compressor:</p> |
| <p>Use one of the commands below to install INC (supported python versions are: 3.6, 3.7, 3.8, 3.9):</p> |
| <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># install stable version from pip</span> |
| pip<span class="w"> </span>install<span class="w"> </span>neural-compressor |
| |
| <span class="c1"># install nightly version from pip</span> |
| pip<span class="w"> </span>install<span class="w"> </span>-i<span class="w"> </span>https://test.pypi.org/simple/<span class="w"> </span>neural-compressor |
| |
| <span class="c1"># install stable version from conda</span> |
| conda<span class="w"> </span>install<span class="w"> </span>neural-compressor<span class="w"> </span>-c<span class="w"> </span>conda-forge<span class="w"> </span>-c<span class="w"> </span>intel |
| </pre></div> |
| </div> |
| <p>If you get into trouble with dependencies on <code class="docutils literal notranslate"><span class="pre">cv2</span></code> library you can run: <code class="docutils literal notranslate"><span class="pre">apt-get</span> <span class="pre">update</span> <span class="pre">&&</span> <span class="pre">apt-get</span> <span class="pre">install</span> <span class="pre">-y</span> <span class="pre">python3-opencv</span></code></p> |
| </li> |
| </ul> |
| </div> |
| <div class="section" id="Configuration-file"> |
| <h2>Configuration file<a class="headerlink" href="#Configuration-file" title="Permalink to this headline">¶</a></h2> |
| <p>Quantization tuning process can be customized in the yaml configuration file. Below is a simple example:</p> |
| <div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><span class="c1"># cnn.yaml</span> |
| |
| <span class="nt">version</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">1.0</span> |
| |
| <span class="nt">model</span><span class="p">:</span> |
| <span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">cnn</span> |
| <span class="w"> </span><span class="nt">framework</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">mxnet</span> |
| |
| <span class="nt">quantization</span><span class="p">:</span> |
| <span class="w"> </span><span class="nt">calibration</span><span class="p">:</span> |
| <span class="w"> </span><span class="nt">sampling_size</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">160</span><span class="w"> </span><span class="c1"># number of samples for calibration</span> |
| |
| <span class="nt">tuning</span><span class="p">:</span> |
| <span class="w"> </span><span class="nt">strategy</span><span class="p">:</span> |
| <span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">basic</span> |
| <span class="w"> </span><span class="nt">accuracy_criterion</span><span class="p">:</span> |
| <span class="w"> </span><span class="nt">relative</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">0.01</span> |
| <span class="w"> </span><span class="nt">exit_policy</span><span class="p">:</span> |
| <span class="w"> </span><span class="nt">timeout</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">0</span> |
| <span class="w"> </span><span class="nt">random_seed</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">9527</span> |
| </pre></div> |
| </div> |
| <p>We are using the <code class="docutils literal notranslate"><span class="pre">basic</span></code> strategy, but you could also try out different ones. <a class="reference external" href="https://github.com/intel/neural-compressor/blob/master/docs/tuning_strategies.md">Here</a> you can find a list of strategies available in INC and details of how they work. You can also add your own strategy if the existing ones do not suit your needs.</p> |
| <p>Since the value of <code class="docutils literal notranslate"><span class="pre">timeout</span></code> in the example above is 0, INC will run until it finds a configuration that satisfies the accuracy criterion and then exit. Depending on the strategy this may not be ideal, as sometimes it would be better to further explore the tuning space to find a superior configuration both in terms of accuracy and speed. To achieve this, we can set a specific <code class="docutils literal notranslate"><span class="pre">timeout</span></code> value, which will tell INC how long (in seconds) it should run.</p> |
| <p>For more information about the configuration file, see the <a class="reference external" href="https://github.com/intel/neural-compressor/blob/master/neural_compressor/template/ptq.yaml">template</a> from the official INC repo. Keep in mind that only the <code class="docutils literal notranslate"><span class="pre">post</span> <span class="pre">training</span> <span class="pre">quantization</span></code> is currently supported for MXNet.</p> |
| </div> |
| <div class="section" id="Model-quantization-and-tuning"> |
| <h2>Model quantization and tuning<a class="headerlink" href="#Model-quantization-and-tuning" title="Permalink to this headline">¶</a></h2> |
| <div class="line-block"> |
| <div class="line">In general, Intel® Neural Compressor requires 4 elements in order to run:</div> |
| <div class="line">1. Configuration file - like the example above</div> |
| <div class="line">2. Model to be quantized</div> |
| <div class="line">3. Calibration dataloader</div> |
| <div class="line">4. Evaluation function - a function that takes a model as an argument and returns the accuracy it achieves on a certain evaluation dataset.</div> |
| </div> |
| <div class="section" id="Quantizing-ResNet"> |
| <h3>Quantizing ResNet<a class="headerlink" href="#Quantizing-ResNet" title="Permalink to this headline">¶</a></h3> |
| <p>The <a class="reference external" href="https://mxnet.apache.org/versions/master/api/python/docs/tutorials/performance/backend/dnnl/dnnl_quantization.html#Quantization">quantization</a> sections described how to quantize ResNet using the native MXNet quantization. This example shows how we can achieve the similar results (with the auto-tuning) using INC.</p> |
| <ol class="arabic simple"> |
| <li><p>Get the model</p></li> |
| </ol> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">logging</span> |
| <span class="kn">import</span> <span class="nn">mxnet</span> <span class="k">as</span> <span class="nn">mx</span> |
| <span class="kn">from</span> <span class="nn">mxnet.gluon.model_zoo</span> <span class="kn">import</span> <span class="n">vision</span> |
| |
| <span class="n">logging</span><span class="o">.</span><span class="n">basicConfig</span><span class="p">()</span> |
| <span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s1">'logger'</span><span class="p">)</span> |
| <span class="n">logger</span><span class="o">.</span><span class="n">setLevel</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">INFO</span><span class="p">)</span> |
| |
| <span class="n">batch_shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">)</span> |
| <span class="n">resnet18</span> <span class="o">=</span> <span class="n">vision</span><span class="o">.</span><span class="n">resnet18_v1</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <ol class="arabic simple" start="2"> |
| <li><p>Prepare the dataset:</p></li> |
| </ol> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">mx</span><span class="o">.</span><span class="n">test_utils</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="s1">'http://data.mxnet.io/data/val_256_q90.rec'</span><span class="p">,</span> <span class="s1">'data/val_256_q90.rec'</span><span class="p">)</span> |
| |
| <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">16</span> |
| <span class="n">mean_std</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'mean_r'</span><span class="p">:</span> <span class="mf">123.68</span><span class="p">,</span> <span class="s1">'mean_g'</span><span class="p">:</span> <span class="mf">116.779</span><span class="p">,</span> <span class="s1">'mean_b'</span><span class="p">:</span> <span class="mf">103.939</span><span class="p">,</span> |
| <span class="s1">'std_r'</span><span class="p">:</span> <span class="mf">58.393</span><span class="p">,</span> <span class="s1">'std_g'</span><span class="p">:</span> <span class="mf">57.12</span><span class="p">,</span> <span class="s1">'std_b'</span><span class="p">:</span> <span class="mf">57.375</span><span class="p">}</span> |
| |
| <span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">ImageRecordIter</span><span class="p">(</span><span class="n">path_imgrec</span><span class="o">=</span><span class="s1">'data/val_256_q90.rec'</span><span class="p">,</span> |
| <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> |
| <span class="n">data_shape</span><span class="o">=</span><span class="n">batch_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:],</span> |
| <span class="n">rand_crop</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">rand_mirror</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="o">**</span><span class="n">mean_std</span><span class="p">)</span> |
| <span class="n">data</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">batch_size</span> |
| </pre></div> |
| </div> |
| <ol class="arabic simple" start="3"> |
| <li><p>Prepare the evaluation function:</p></li> |
| </ol> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">eval_samples</span> <span class="o">=</span> <span class="n">batch_size</span><span class="o">*</span><span class="mi">10</span> |
| |
| <span class="k">def</span> <span class="nf">eval_func</span><span class="p">(</span><span class="n">model</span><span class="p">):</span> |
| <span class="n">data</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span> |
| <span class="n">metric</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">data</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">i</span> <span class="o">*</span> <span class="n">batch_size</span> <span class="o">>=</span> <span class="n">eval_samples</span><span class="p">:</span> |
| <span class="k">break</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">batch</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">as_in_context</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">())</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">batch</span><span class="o">.</span><span class="n">label</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">as_in_context</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">())</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">metric</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">outputs</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">metric</span><span class="o">.</span><span class="n">get</span><span class="p">()[</span><span class="mi">1</span><span class="p">]</span> |
| </pre></div> |
| </div> |
| <ol class="arabic simple" start="4"> |
| <li><p>Run Intel® Neural Compressor:</p></li> |
| </ol> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">neural_compressor.experimental</span> <span class="kn">import</span> <span class="n">Quantization</span> |
| <span class="n">quantizer</span> <span class="o">=</span> <span class="n">Quantization</span><span class="p">(</span><span class="s2">"./cnn.yaml"</span><span class="p">)</span> |
| <span class="n">quantizer</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">resnet18</span> |
| <span class="n">quantizer</span><span class="o">.</span><span class="n">calib_dataloader</span> <span class="o">=</span> <span class="n">data</span> |
| <span class="n">quantizer</span><span class="o">.</span><span class="n">eval_func</span> <span class="o">=</span> <span class="n">eval_func</span> |
| <span class="n">qnet</span> <span class="o">=</span> <span class="n">quantizer</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span><span class="o">.</span><span class="n">model</span> |
| </pre></div> |
| </div> |
| <p>Since this model already achieves good accuracy using native quantization (less than 1% accuracy drop), for the given configuration file, INC will end on the first configuration, quantizing all layers using <code class="docutils literal notranslate"><span class="pre">naive</span></code> calibration mode for each. To see the true potential of INC, we need a model which suffers from a larger accuracy drop after quantization.</p> |
| </div> |
| <div class="section" id="Quantizing-ResNet50v2"> |
| <h3>Quantizing ResNet50v2<a class="headerlink" href="#Quantizing-ResNet50v2" title="Permalink to this headline">¶</a></h3> |
| <p>This example shows how to use INC to quantize ResNet50 v2. In this case, the native MXNet quantization introduce a huge accuracy drop (70% using <code class="docutils literal notranslate"><span class="pre">naive</span></code> calibration mode) and INC allows to automatically find better solution.</p> |
| <p>This is the <a class="reference external" href="https://github.com/apache/incubator-mxnet/blob/master/example/quantization_inc/resnet50v2_mse.yaml">INC configuration file</a> for this example:</p> |
| <div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><span class="nt">version</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">1.0</span> |
| |
| <span class="nt">model</span><span class="p">:</span> |
| <span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">resnet50_v2</span> |
| <span class="w"> </span><span class="nt">framework</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">mxnet</span> |
| |
| <span class="nt">quantization</span><span class="p">:</span> |
| <span class="w"> </span><span class="nt">calibration</span><span class="p">:</span> |
| <span class="w"> </span><span class="nt">sampling_size</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">192</span><span class="w"> </span><span class="c1"># number of samples for calibration</span> |
| |
| <span class="nt">tuning</span><span class="p">:</span> |
| <span class="w"> </span><span class="nt">strategy</span><span class="p">:</span> |
| <span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">mse</span> |
| <span class="w"> </span><span class="nt">accuracy_criterion</span><span class="p">:</span> |
| <span class="w"> </span><span class="nt">relative</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">0.015</span> |
| <span class="w"> </span><span class="nt">exit_policy</span><span class="p">:</span> |
| <span class="w"> </span><span class="nt">timeout</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">0</span> |
| <span class="w"> </span><span class="nt">max_trials</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">500</span> |
| <span class="w"> </span><span class="nt">random_seed</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">9527</span> |
| </pre></div> |
| </div> |
| <p>It could be used with script below (<a class="reference external" href="https://github.com/apache/incubator-mxnet/blob/master/example/quantization_inc/resnet_mse.py">resnet_mse.py</a>) to find operator, which caused the most significant accuracy drop and disable it from quantization. You can find description of MSE strategy <a class="reference external" href="https://github.com/intel/neural-compressor/blob/master/docs/tuning_strategies.md#user-content-mse">here</a>.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mxnet</span> <span class="k">as</span> <span class="nn">mx</span> |
| <span class="kn">from</span> <span class="nn">mxnet.gluon.model_zoo.vision</span> <span class="kn">import</span> <span class="n">resnet50_v2</span> |
| <span class="kn">from</span> <span class="nn">mxnet.gluon.data.vision</span> <span class="kn">import</span> <span class="n">transforms</span> |
| <span class="kn">from</span> <span class="nn">mxnet.contrib.quantization</span> <span class="kn">import</span> <span class="n">quantize_net</span> |
| |
| <span class="c1"># Preparing input data</span> |
| <span class="n">rgb_mean</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">)</span> |
| <span class="n">rgb_std</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">)</span> |
| <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">64</span> |
| <span class="n">num_calib_batches</span> <span class="o">=</span> <span class="mi">9</span> |
| <span class="c1"># set proper path to ImageNet data set below</span> |
| <span class="n">dataset</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">ImageRecordDataset</span><span class="p">(</span><span class="s1">'../imagenet/rec/val.rec'</span><span class="p">)</span> |
| <span class="c1"># Tuning with INC on whole data set takes a lot of time. Therefore, we take only a part of the data set</span> |
| <span class="c1"># as representative part of it:</span> |
| <span class="n">dataset</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="n">num_calib_batches</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">)</span> |
| <span class="n">transformer</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span><span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">256</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="n">rgb_mean</span><span class="p">,</span> <span class="n">std</span><span class="o">=</span><span class="n">rgb_std</span><span class="p">)])</span> |
| <span class="c1"># Note: as input data is used many times during tuning, it is better to have it prepared earlier.</span> |
| <span class="c1"># Therefore, lazy parameter for transform_first is set to False.</span> |
| <span class="n">val_data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span> |
| <span class="n">dataset</span><span class="o">.</span><span class="n">transform_first</span><span class="p">(</span><span class="n">transformer</span><span class="p">,</span> <span class="n">lazy</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="n">val_data</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">batch_size</span> |
| |
| <span class="n">net</span> <span class="o">=</span> <span class="n">resnet50_v2</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">eval_func</span><span class="p">(</span><span class="n">model</span><span class="p">):</span> |
| <span class="n">metric</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">x</span><span class="p">,</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">val_data</span><span class="p">:</span> |
| <span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">metric</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">output</span><span class="p">)</span> |
| <span class="n">accuracy</span> <span class="o">=</span> <span class="n">metric</span><span class="o">.</span><span class="n">get</span><span class="p">()[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="k">return</span> <span class="n">accuracy</span> |
| |
| |
| <span class="kn">from</span> <span class="nn">neural_compressor.experimental</span> <span class="kn">import</span> <span class="n">Quantization</span> |
| <span class="n">quantizer</span> <span class="o">=</span> <span class="n">Quantization</span><span class="p">(</span><span class="s2">"resnet50v2_mse.yaml"</span><span class="p">)</span> |
| <span class="n">quantizer</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">net</span> |
| <span class="n">quantizer</span><span class="o">.</span><span class="n">calib_dataloader</span> <span class="o">=</span> <span class="n">val_data</span> |
| <span class="n">quantizer</span><span class="o">.</span><span class="n">eval_func</span> <span class="o">=</span> <span class="n">eval_func</span> |
| <span class="n">qnet_inc</span> <span class="o">=</span> <span class="n">quantizer</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span><span class="o">.</span><span class="n">model</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"INC finished"</span><span class="p">)</span> |
| <span class="c1"># You can save optimized model for the later use:</span> |
| <span class="n">qnet_inc</span><span class="o">.</span><span class="n">export</span><span class="p">(</span><span class="s2">"__quantized_with_inc"</span><span class="p">)</span> |
| <span class="c1"># You can see which configuration was applied by INC and which nodes were excluded from quantization,</span> |
| <span class="c1"># to achieve given accuracy loss against floating point calculation.</span> |
| <span class="nb">print</span><span class="p">(</span><span class="n">quantizer</span><span class="o">.</span><span class="n">strategy</span><span class="o">.</span><span class="n">best_qmodel</span><span class="o">.</span><span class="n">q_config</span><span class="p">[</span><span class="s1">'quant_cfg'</span><span class="p">])</span> |
| </pre></div> |
| </div> |
| <div class="section" id="Results:"> |
| <h4>Results:<a class="headerlink" href="#Results:" title="Permalink to this headline">¶</a></h4> |
| <div class="line-block"> |
| <div class="line">Resnet50 v2 model could be prepared to achieve better performance with various calibration and tuning methods.</div> |
| <div class="line">It is done by <a class="reference external" href="https://github.com/apache/incubator-mxnet/blob/master/example/quantization_inc/resnet_tuning.py">resnet_tuning.py</a> script on a small part of data set to reduce time required for tuning (9 batches). Later saved models are validated on a whole data set by <a class="reference external" href="https://github.com/apache/incubator-mxnet/blob/master/example/quantization_inc/resnet_measurement.py">resnet_measurement.py</a> script. Accuracy results on the whole validation dataset (782 batches) are shown below.</div> |
| </div> |
| <table class="docutils align-default"> |
| <colgroup> |
| <col style="width: 29%" /> |
| <col style="width: 10%" /> |
| <col style="width: 10%" /> |
| <col style="width: 9%" /> |
| <col style="width: 9%" /> |
| <col style="width: 10%" /> |
| <col style="width: 12%" /> |
| <col style="width: 9%" /> |
| </colgroup> |
| <thead> |
| <tr class="row-odd"><th class="head"><p>Optimization method</p></th> |
| <th class="head"><p>Top 1 accuracy</p></th> |
| <th class="head"><p>Top 5 accuracy</p></th> |
| <th class="head"><p>Top 1 relative accuracy loss [%]</p></th> |
| <th class="head"><p>Top 5 relative accuracy loss [%]</p></th> |
| <th class="head"><p>Cost = one-time optimization on 9 batches [s]</p></th> |
| <th class="head"><p>Validation time [s]</p></th> |
| <th class="head"><p>Speedup</p></th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr class="row-even"><td><p>fp32 no optimization</p></td> |
| <td><p>0.7699</p></td> |
| <td><p>0.9340</p></td> |
| <td><p>0.00</p></td> |
| <td><p>0.00</p></td> |
| <td><p>0.00</p></td> |
| <td><p>316.50</p></td> |
| <td><p>1.0</p></td> |
| </tr> |
| <tr class="row-odd"><td><p>fp32 fused</p></td> |
| <td><p>0.7699</p></td> |
| <td><p>0.9340</p></td> |
| <td><p>0.00</p></td> |
| <td><p>0.00</p></td> |
| <td><p>0.03</p></td> |
| <td><p>147.77</p></td> |
| <td><p>2.1</p></td> |
| </tr> |
| <tr class="row-even"><td><p>int8 full naive</p></td> |
| <td><p>0.2207</p></td> |
| <td><p>0.3912</p></td> |
| <td><p>71.33</p></td> |
| <td><p>58.12</p></td> |
| <td><p>11.29</p></td> |
| <td><p>45.81</p></td> |
| <td><p><strong>6.9</strong></p></td> |
| </tr> |
| <tr class="row-odd"><td><p>int8 full entropy</p></td> |
| <td><p>0.6933</p></td> |
| <td><p>0.8917</p></td> |
| <td><p>9.95</p></td> |
| <td><p>4.53</p></td> |
| <td><p>80.23</p></td> |
| <td><p>46.39</p></td> |
| <td><p>6.8</p></td> |
| </tr> |
| <tr class="row-even"><td><p>int8 smart naive</p></td> |
| <td><p>0.2210</p></td> |
| <td><p>0.3905</p></td> |
| <td><p>71.29</p></td> |
| <td><p>58.19</p></td> |
| <td><p>11.15</p></td> |
| <td><p>46.02</p></td> |
| <td><p>6.9</p></td> |
| </tr> |
| <tr class="row-odd"><td><p>int8 smart entropy</p></td> |
| <td><p>0.6928</p></td> |
| <td><p>0.8910</p></td> |
| <td><p>10.01</p></td> |
| <td><p>4.60</p></td> |
| <td><p>79.75</p></td> |
| <td><p>45.98</p></td> |
| <td><p>6.9</p></td> |
| </tr> |
| <tr class="row-even"><td><p>int8 INC basic</p></td> |
| <td><p>0.7692</p></td> |
| <td><p>0.9331</p></td> |
| <td><p><strong>0.09</strong></p></td> |
| <td><p>0.10</p></td> |
| <td><p>266.50</p></td> |
| <td><p>48.32</p></td> |
| <td><p><strong>6.6</strong></p></td> |
| </tr> |
| <tr class="row-odd"><td><p>int8 INC mse</p></td> |
| <td><p>0.7692</p></td> |
| <td><p>0.9337</p></td> |
| <td><p><strong>0.09</strong></p></td> |
| <td><p>0.03</p></td> |
| <td><p>106.50</p></td> |
| <td><p>49.76</p></td> |
| <td><p><strong>6.4</strong></p></td> |
| </tr> |
| <tr class="row-even"><td><p>int8 INC mycustom</p></td> |
| <td><p>0.7699</p></td> |
| <td><p>0.9338</p></td> |
| <td><p><strong>0.00</strong></p></td> |
| <td><p>0.02</p></td> |
| <td><p>370.29</p></td> |
| <td><p>70.07</p></td> |
| <td><p><strong>4.5</strong></p></td> |
| </tr> |
| </tbody> |
| </table> |
| <div class="line-block"> |
| <div class="line">Environment:</div> |
| <div class="line">- Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz (c6i.16xlarge Amazon EC2 instance)</div> |
| <div class="line">- Ubuntu 20.04.4 LTS (GNU/Linux Ubuntu 20.04.4 LTS 5.15.0-1017-aws ami-0558cee5b20db1f9c)</div> |
| <div class="line">- MXNet 2.0.0b20220902 (commit 3a19f0e50d75fedb05eb558a9c835726b57df4cf)</div> |
| <div class="line">- INC 1.13.1</div> |
| <div class="line">- scripts above were run as parameter for <a class="reference external" href="https://github.com/apache/incubator-mxnet/blob/master/benchmark/python/dnnl/run.sh">run.sh</a> script to properly setup parallel computation parameters.</div> |
| </div> |
| <p>For this model INC <code class="docutils literal notranslate"><span class="pre">basic</span></code>, <code class="docutils literal notranslate"><span class="pre">mse</span></code> and <code class="docutils literal notranslate"><span class="pre">mycustom</span></code> strategies found configurations meeting the 1.5% relative accuracy loss criterion. Only the <code class="docutils literal notranslate"><span class="pre">bayesian</span></code> strategy didn’t find solution within 500 attempts limit. Although these results may suggest that the <code class="docutils literal notranslate"><span class="pre">mse</span></code> strategy is the best compromise between time spent to find the optimized model and final model performance efficiency, different strategies may give better results for specific models and tasks. For example for ALBERT model there |
| is no solution given by build-in INC strategies. For such situation you can create your custom strategy, similar to this one: <a class="reference external" href="https://github.com/apache/incubator-mxnet/blob/master/example/quantization_inc/custom_strategy.py">custom_strategy.py</a>. You can notice, that the most important thing done by INC was to find the operator, which had the most significant impact on the loss of accuracy and disable it from quantization if needed. You can see below which operator was excluded by <code class="docutils literal notranslate"><span class="pre">mse</span></code> |
| strategy in last print given by <a class="reference external" href="https://github.com/apache/incubator-mxnet/blob/master/example/quantization_inc/resnet_mse.py">resnet_mse.py</a> script:</p> |
| <p>{‘excluded_symbols’: [‘<strong>sg_onednn_conv_bn_act_0</strong>’], ‘quantized_dtype’: ‘auto’, ‘quantize_mode’: ‘smart’, ‘quantize_granularity’: ‘tensor-wise’}</p> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="Tips"> |
| <h2>Tips<a class="headerlink" href="#Tips" title="Permalink to this headline">¶</a></h2> |
| <ul> |
| <li><p>In order to get a solution that generalizes well, evaluate the model (in eval_func) on a representative dataset.</p></li> |
| <li><p>With <code class="docutils literal notranslate"><span class="pre">history.snapshot</span></code> file (generated by INC) you can recover any model that was generated during the tuning process:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">neural_compressor.utils.utility</span> <span class="kn">import</span> <span class="n">recover</span> |
| |
| <span class="n">quantized_model</span> <span class="o">=</span> <span class="n">recover</span><span class="p">(</span><span class="n">f32_model</span><span class="p">,</span> <span class="s1">'nc_workspace/<tuning date>/history.snapshot'</span><span class="p">,</span> <span class="n">configuration_idx</span><span class="p">)</span><span class="o">.</span><span class="n">model</span> |
| </pre></div> |
| </div> |
| </li> |
| </ul> |
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| <ul> |
| <li><a class="reference internal" href="#">Improving accuracy with Intel® Neural Compressor</a><ul> |
| <li><a class="reference internal" href="#Installation-and-Prerequisites">Installation and Prerequisites</a></li> |
| <li><a class="reference internal" href="#Configuration-file">Configuration file</a></li> |
| <li><a class="reference internal" href="#Model-quantization-and-tuning">Model quantization and tuning</a><ul> |
| <li><a class="reference internal" href="#Quantizing-ResNet">Quantizing ResNet</a></li> |
| <li><a class="reference internal" href="#Quantizing-ResNet50v2">Quantizing ResNet50v2</a><ul> |
| <li><a class="reference internal" href="#Results:">Results:</a></li> |
| </ul> |
| </li> |
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| <li><a class="reference internal" href="#Tips">Tips</a></li> |
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