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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> |
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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> |
| </ul> |
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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="../../autograd/index.html">Automatic Differentiation</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../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> |
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| <li class="toctree-l5"><a class="reference internal" href="../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="../image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../image/mnist.html">Handwritten Digit Recognition</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../loss/custom-loss.html">Custom Loss Blocks</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../loss/loss.html">Loss functions</a></li> |
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| <li class="toctree-l4 current"><a class="reference internal" href="index.html">Text Tutorials</a><ul class="current"> |
| <li class="toctree-l5"><a class="reference internal" href="gnmt.html">Google Neural Machine Translation</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../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="../../kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../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="../../legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../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="../../legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../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="../../np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../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="../../onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../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 external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../performance/compression/int8.html">Deploy with int-8</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-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.ndarray.tolist.html">mxnet.np.ndarray.tolist</a></li> |
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| </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> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.dot.html">mxnet.np.dot</a></li> |
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| <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> |
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| <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> |
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| <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> |
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| <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.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.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> |
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| <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.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> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.modf.html">mxnet.np.modf</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> |
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| <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> |
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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> |
| <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.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> |
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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.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> |
| <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> |
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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> |
| <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"> |
| <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> |
| </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 current"><a class="reference internal" href="../../index.html">Packages</a><ul class="current"> |
| <li class="toctree-l3"><a class="reference internal" href="../../autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3 current"><a class="reference internal" href="../index.html">Gluon</a><ul class="current"> |
| <li class="toctree-l4"><a class="reference internal" href="../blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../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="../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="../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="../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="../image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4 current"><a class="reference internal" href="index.html">Text Tutorials</a><ul class="current"> |
| <li class="toctree-l5"><a class="reference internal" href="gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5 current"><a class="current reference internal" href="#">Machine Translation with Transformer</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../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="../training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../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="../../legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
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| </li> |
| </ul> |
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| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
| </ul> |
| </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.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.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.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.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> |
| <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> |
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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> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.randint.html">mxnet.np.random.randint</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.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> |
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| <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> |
| <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> |
| <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> |
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| |
| <div class="section" id="machine-translation-with-transformer"> |
| <h1>Machine Translation with Transformer<a class="headerlink" href="#machine-translation-with-transformer" title="Permalink to this headline">¶</a></h1> |
| <p>In this notebook, we will show how to train Transformer introduced in |
| [1] and evaluate the pretrained model using GluonNLP. The model is both |
| more accurate and lighter to train than previous seq2seq models. We will |
| together go through:</p> |
| <ol class="arabic simple"> |
| <li><p>Use the state-of-the-art pretrained Transformer model: we will |
| evaluate the pretrained SOTA Transformer model and translate a few |
| sentences ourselves with the <code class="docutils literal notranslate"><span class="pre">BeamSearchTranslator</span></code> using the SOTA |
| model;</p></li> |
| <li><p>Train the Transformer yourself: including loading and processing |
| dataset, define the Transformer model, write train script and |
| evaluate the trained model. Note that in order to obtain the |
| state-of-the-art results on WMT 2014 English-German dataset, it will |
| take around 1 day to have the model. In order to let you run through |
| the Transformer quickly, we suggest you to start with the <code class="docutils literal notranslate"><span class="pre">TOY</span></code> |
| dataset sampled from the WMT dataset (by default in this notebook).</p></li> |
| </ol> |
| <div class="section" id="preparation"> |
| <h2>Preparation<a class="headerlink" href="#preparation" title="Permalink to this headline">¶</a></h2> |
| <div class="section" id="load-mxnet-and-gluonnlp"> |
| <h3>Load MXNet and GluonNLP<a class="headerlink" href="#load-mxnet-and-gluonnlp" title="Permalink to this headline">¶</a></h3> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">warnings</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s1">'ignore'</span><span class="p">)</span> |
| |
| <span class="kn">import</span> <span class="nn">random</span> |
| <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</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</span> <span class="kn">import</span> <span class="n">gluon</span> |
| <span class="kn">import</span> <span class="nn">gluonnlp</span> <span class="k">as</span> <span class="nn">nlp</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="set-environment"> |
| <h3>Set Environment<a class="headerlink" href="#set-environment" title="Permalink to this headline">¶</a></h3> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span> |
| <span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span> |
| <span class="n">mx</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">10000</span><span class="p">)</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="use-the-sota-pretrained-transformer-model"> |
| <h2>Use the SOTA Pretrained Transformer model<a class="headerlink" href="#use-the-sota-pretrained-transformer-model" title="Permalink to this headline">¶</a></h2> |
| <p>In this subsection, we first load the SOTA Transformer model in GluonNLP |
| model zoo; and secondly we load the full WMT 2014 English-German test |
| dataset; and finally evaluate the model.</p> |
| <div class="section" id="get-the-sota-transformer"> |
| <h3>Get the SOTA Transformer<a class="headerlink" href="#get-the-sota-transformer" title="Permalink to this headline">¶</a></h3> |
| <p>Next, we load the pretrained SOTA Transformer using the model API in |
| GluonNLP. In this way, we can easily get access to the SOTA machine |
| translation model and use it in your own application.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">nmt</span> |
| |
| <span class="n">wmt_model_name</span> <span class="o">=</span> <span class="s1">'transformer_en_de_512'</span> |
| |
| <span class="n">wmt_transformer_model</span><span class="p">,</span> <span class="n">wmt_src_vocab</span><span class="p">,</span> <span class="n">wmt_tgt_vocab</span> <span class="o">=</span> \ |
| <span class="n">nmt</span><span class="o">.</span><span class="n">transformer</span><span class="o">.</span><span class="n">get_model</span><span class="p">(</span><span class="n">wmt_model_name</span><span class="p">,</span> |
| <span class="n">dataset_name</span><span class="o">=</span><span class="s1">'WMT2014'</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="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">)</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="n">wmt_src_vocab</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="n">wmt_tgt_vocab</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>The Transformer model architecture is shown as below:</p> |
| <div style="width: 500px;"><p><img alt="transformer" src="../../../../_images/transformer.png" /></p> |
| </div><div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">wmt_transformer_model</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="load-and-preprocess-wmt-2014-dataset"> |
| <h3>Load and Preprocess WMT 2014 Dataset<a class="headerlink" href="#load-and-preprocess-wmt-2014-dataset" title="Permalink to this headline">¶</a></h3> |
| <p>We then load the WMT 2014 English-German test dataset for evaluation |
| purpose.</p> |
| <p>The following shows how to process the dataset and cache the processed |
| dataset for the future use. The processing steps include:</p> |
| <ul class="simple"> |
| <li><ol class="arabic simple"> |
| <li><p>clip the source and target sequences</p></li> |
| </ol> |
| </li> |
| <li><ol class="arabic simple" start="2"> |
| <li><p>split the string input to a list of tokens</p></li> |
| </ol> |
| </li> |
| <li><ol class="arabic simple" start="3"> |
| <li><p>map the string token into its index in the vocabulary</p></li> |
| </ol> |
| </li> |
| <li><ol class="arabic simple" start="4"> |
| <li><p>append EOS token to source sentence and add BOS and EOS tokens to |
| target sentence.</p></li> |
| </ol> |
| </li> |
| </ul> |
| <p>Let’s first look at the WMT 2014 corpus.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">hyperparameters</span> <span class="k">as</span> <span class="nn">hparams</span> |
| |
| <span class="n">wmt_data_test</span> <span class="o">=</span> <span class="n">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">WMT2014BPE</span><span class="p">(</span><span class="s1">'newstest2014'</span><span class="p">,</span> |
| <span class="n">src_lang</span><span class="o">=</span><span class="n">hparams</span><span class="o">.</span><span class="n">src_lang</span><span class="p">,</span> |
| <span class="n">tgt_lang</span><span class="o">=</span><span class="n">hparams</span><span class="o">.</span><span class="n">tgt_lang</span><span class="p">,</span> |
| <span class="n">full</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Source language </span><span class="si">%s</span><span class="s1">, Target language </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="n">hparams</span><span class="o">.</span><span class="n">src_lang</span><span class="p">,</span> <span class="n">hparams</span><span class="o">.</span><span class="n">tgt_lang</span><span class="p">))</span> |
| |
| <span class="n">wmt_data_test</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| </pre></div> |
| </div> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">wmt_test_text</span> <span class="o">=</span> <span class="n">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">WMT2014</span><span class="p">(</span><span class="s1">'newstest2014'</span><span class="p">,</span> |
| <span class="n">src_lang</span><span class="o">=</span><span class="n">hparams</span><span class="o">.</span><span class="n">src_lang</span><span class="p">,</span> |
| <span class="n">tgt_lang</span><span class="o">=</span><span class="n">hparams</span><span class="o">.</span><span class="n">tgt_lang</span><span class="p">,</span> |
| <span class="n">full</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="n">wmt_test_text</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| </pre></div> |
| </div> |
| <p>We then generate the target gold translations.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">wmt_test_tgt_sentences</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">wmt_test_text</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="k">lambda</span> <span class="n">src</span><span class="p">,</span> <span class="n">tgt</span><span class="p">:</span> <span class="n">tgt</span><span class="p">))</span> |
| <span class="n">wmt_test_tgt_sentences</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| </pre></div> |
| </div> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">dataprocessor</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="n">dataprocessor</span><span class="o">.</span><span class="n">TrainValDataTransform</span><span class="o">.</span><span class="vm">__doc__</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">wmt_transform_fn</span> <span class="o">=</span> <span class="n">dataprocessor</span><span class="o">.</span><span class="n">TrainValDataTransform</span><span class="p">(</span><span class="n">wmt_src_vocab</span><span class="p">,</span> <span class="n">wmt_tgt_vocab</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> |
| <span class="n">wmt_dataset_processed</span> <span class="o">=</span> <span class="n">wmt_data_test</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">wmt_transform_fn</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="nb">print</span><span class="p">(</span><span class="o">*</span><span class="n">wmt_dataset_processed</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">sep</span><span class="o">=</span><span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="create-sampler-and-dataloader-for-wmt-2014-dataset"> |
| <h3>Create Sampler and DataLoader for WMT 2014 Dataset<a class="headerlink" href="#create-sampler-and-dataloader-for-wmt-2014-dataset" title="Permalink to this headline">¶</a></h3> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">wmt_data_test_with_len</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">SimpleDataset</span><span class="p">([(</span><span class="n">ele</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ele</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="nb">len</span><span class="p">(</span> |
| <span class="n">ele</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="nb">len</span><span class="p">(</span><span class="n">ele</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">ele</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">wmt_dataset_processed</span><span class="p">)])</span> |
| </pre></div> |
| </div> |
| <p>Now, we have obtained data_train, data_val, and data_test. The next |
| step is to construct sampler and DataLoader. The first step is to |
| construct batchify function, which pads and stacks sequences to form |
| mini-batch.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">wmt_test_batchify_fn</span> <span class="o">=</span> <span class="n">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">batchify</span><span class="o">.</span><span class="n">Tuple</span><span class="p">(</span> |
| <span class="n">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">batchify</span><span class="o">.</span><span class="n">Pad</span><span class="p">(),</span> |
| <span class="n">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">batchify</span><span class="o">.</span><span class="n">Pad</span><span class="p">(),</span> |
| <span class="n">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">batchify</span><span class="o">.</span><span class="n">Stack</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">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">batchify</span><span class="o">.</span><span class="n">Stack</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">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">batchify</span><span class="o">.</span><span class="n">Stack</span><span class="p">())</span> |
| </pre></div> |
| </div> |
| <p>We can then construct bucketing samplers, which generate batches by |
| grouping sequences with similar lengths.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">wmt_bucket_scheme</span> <span class="o">=</span> <span class="n">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">ExpWidthBucket</span><span class="p">(</span><span class="n">bucket_len_step</span><span class="o">=</span><span class="mf">1.2</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">wmt_test_batch_sampler</span> <span class="o">=</span> <span class="n">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">FixedBucketSampler</span><span class="p">(</span> |
| <span class="n">lengths</span><span class="o">=</span><span class="n">wmt_dataset_processed</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="k">lambda</span> <span class="n">src</span><span class="p">,</span> <span class="n">tgt</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">tgt</span><span class="p">)),</span> |
| <span class="n">use_average_length</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">bucket_scheme</span><span class="o">=</span><span class="n">wmt_bucket_scheme</span><span class="p">,</span> |
| <span class="n">batch_size</span><span class="o">=</span><span class="mi">256</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="n">wmt_test_batch_sampler</span><span class="o">.</span><span class="n">stats</span><span class="p">())</span> |
| </pre></div> |
| </div> |
| <p>Given the samplers, we can create DataLoader, which is iterable.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">wmt_test_data_loader</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">wmt_data_test_with_len</span><span class="p">,</span> |
| <span class="n">batch_sampler</span><span class="o">=</span><span class="n">wmt_test_batch_sampler</span><span class="p">,</span> |
| <span class="n">batchify_fn</span><span class="o">=</span><span class="n">wmt_test_batchify_fn</span><span class="p">,</span> |
| <span class="n">num_workers</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span> |
| <span class="nb">len</span><span class="p">(</span><span class="n">wmt_test_data_loader</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="evaluate-transformer"> |
| <h3>Evaluate Transformer<a class="headerlink" href="#evaluate-transformer" title="Permalink to this headline">¶</a></h3> |
| <p>Next, we generate the SOTA results on the WMT test dataset. As we can |
| see from the result, we are able to achieve the SOTA number 27.35 as the |
| BLEU score.</p> |
| <p>We first define the <code class="docutils literal notranslate"><span class="pre">BeamSearchTranslator</span></code> to generate the actual |
| translations.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">wmt_translator</span> <span class="o">=</span> <span class="n">nmt</span><span class="o">.</span><span class="n">translation</span><span class="o">.</span><span class="n">BeamSearchTranslator</span><span class="p">(</span> |
| <span class="n">model</span><span class="o">=</span><span class="n">wmt_transformer_model</span><span class="p">,</span> |
| <span class="n">beam_size</span><span class="o">=</span><span class="n">hparams</span><span class="o">.</span><span class="n">beam_size</span><span class="p">,</span> |
| <span class="n">scorer</span><span class="o">=</span><span class="n">nlp</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">BeamSearchScorer</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="n">hparams</span><span class="o">.</span><span class="n">lp_alpha</span><span class="p">,</span> <span class="n">K</span><span class="o">=</span><span class="n">hparams</span><span class="o">.</span><span class="n">lp_k</span><span class="p">),</span> |
| <span class="n">max_length</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Then we caculate the <code class="docutils literal notranslate"><span class="pre">loss</span></code> as well as the <code class="docutils literal notranslate"><span class="pre">bleu</span></code> score on the WMT |
| 2014 English-German test dataset. Note that the following evalution |
| process will take ~13 mins to complete.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">time</span> |
| <span class="kn">import</span> <span class="nn">utils</span> |
| |
| <span class="n">eval_start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> |
| |
| <span class="n">wmt_test_loss_function</span> <span class="o">=</span> <span class="n">nmt</span><span class="o">.</span><span class="n">loss</span><span class="o">.</span><span class="n">SoftmaxCEMaskedLoss</span><span class="p">()</span> |
| <span class="n">wmt_test_loss_function</span><span class="o">.</span><span class="n">hybridize</span><span class="p">()</span> |
| |
| <span class="n">wmt_detokenizer</span> <span class="o">=</span> <span class="n">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">SacreMosesDetokenizer</span><span class="p">()</span> |
| |
| <span class="n">wmt_test_loss</span><span class="p">,</span> <span class="n">wmt_test_translation_out</span> <span class="o">=</span> <span class="n">utils</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">wmt_transformer_model</span><span class="p">,</span> |
| <span class="n">wmt_test_data_loader</span><span class="p">,</span> |
| <span class="n">wmt_test_loss_function</span><span class="p">,</span> |
| <span class="n">wmt_translator</span><span class="p">,</span> |
| <span class="n">wmt_tgt_vocab</span><span class="p">,</span> |
| <span class="n">wmt_detokenizer</span><span class="p">,</span> |
| <span class="n">ctx</span><span class="p">)</span> |
| |
| <span class="n">wmt_test_bleu_score</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">nmt</span><span class="o">.</span><span class="n">bleu</span><span class="o">.</span><span class="n">compute_bleu</span><span class="p">([</span><span class="n">wmt_test_tgt_sentences</span><span class="p">],</span> |
| <span class="n">wmt_test_translation_out</span><span class="p">,</span> |
| <span class="n">tokenized</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">tokenizer</span><span class="o">=</span><span class="n">hparams</span><span class="o">.</span><span class="n">bleu</span><span class="p">,</span> |
| <span class="n">split_compound_word</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">bpe</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'WMT14 EN-DE SOTA model test loss: </span><span class="si">%.2f</span><span class="s1">; test bleu score: </span><span class="si">%.2f</span><span class="s1">; time cost </span><span class="si">%.2f</span><span class="s1">s'</span> |
| <span class="o">%</span><span class="p">(</span><span class="n">wmt_test_loss</span><span class="p">,</span> <span class="n">wmt_test_bleu_score</span> <span class="o">*</span> <span class="mi">100</span><span class="p">,</span> <span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">eval_start_time</span><span class="p">)))</span> |
| </pre></div> |
| </div> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">'Sample translations:'</span><span class="p">)</span> |
| <span class="n">num_pairs</span> <span class="o">=</span> <span class="mi">3</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="n">num_pairs</span><span class="p">):</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'EN:'</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="n">wmt_test_text</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'DE-Candidate:'</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="n">wmt_test_translation_out</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'DE-Reference:'</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="n">wmt_test_tgt_sentences</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'========'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="translation-inference"> |
| <h3>Translation Inference<a class="headerlink" href="#translation-inference" title="Permalink to this headline">¶</a></h3> |
| <p>We herein show the actual translation example (EN-DE) when given a |
| source language using the SOTA Transformer model.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">utils</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Translate the following English sentence into German:'</span><span class="p">)</span> |
| |
| <span class="n">sample_src_seq</span> <span class="o">=</span> <span class="s1">'We love each other'</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'[</span><span class="se">\'</span><span class="s1">'</span> <span class="o">+</span> <span class="n">sample_src_seq</span> <span class="o">+</span> <span class="s1">'</span><span class="se">\'</span><span class="s1">]'</span><span class="p">)</span> |
| |
| <span class="n">sample_tgt_seq</span> <span class="o">=</span> <span class="n">utils</span><span class="o">.</span><span class="n">translate</span><span class="p">(</span><span class="n">wmt_translator</span><span class="p">,</span> |
| <span class="n">sample_src_seq</span><span class="p">,</span> |
| <span class="n">wmt_src_vocab</span><span class="p">,</span> |
| <span class="n">wmt_tgt_vocab</span><span class="p">,</span> |
| <span class="n">wmt_detokenizer</span><span class="p">,</span> |
| <span class="n">ctx</span><span class="p">)</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'The German translation is:'</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="n">sample_tgt_seq</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="train-your-own-transformer"> |
| <h2>Train Your Own Transformer<a class="headerlink" href="#train-your-own-transformer" title="Permalink to this headline">¶</a></h2> |
| <p>In this subsection, we will go though the whole process about loading |
| translation dataset in a more unified way, and create data sampler and |
| loader, as well as define the Transformer model, finally writing |
| training script to train the model yourself.</p> |
| <div class="section" id="load-and-preprocess-toy-dataset"> |
| <h3>Load and Preprocess TOY Dataset<a class="headerlink" href="#load-and-preprocess-toy-dataset" title="Permalink to this headline">¶</a></h3> |
| <p>Note that we use demo mode (<code class="docutils literal notranslate"><span class="pre">TOY</span></code> dataset) by default, since loading |
| the whole WMT 2014 English-German dataset <code class="docutils literal notranslate"><span class="pre">WMT2014BPE</span></code> for the later |
| training will be slow (~1 day). But if you really want to train to have |
| the SOTA result, please set <code class="docutils literal notranslate"><span class="pre">demo</span> <span class="pre">=</span> <span class="pre">False</span></code>. In order to make the data |
| processing blocks execute in a more efficient way, we package them in |
| the <code class="docutils literal notranslate"><span class="pre">load_translation_data</span></code> (<code class="docutils literal notranslate"><span class="pre">transform</span></code> etc.) function used as |
| below. The function also returns the gold target sentences as well as |
| the vocabularies.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">demo</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="k">if</span> <span class="n">demo</span><span class="p">:</span> |
| <span class="n">dataset</span> <span class="o">=</span> <span class="s1">'TOY'</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">dataset</span> <span class="o">=</span> <span class="s1">'WMT2014BPE'</span> |
| |
| <span class="n">data_train</span><span class="p">,</span> <span class="n">data_val</span><span class="p">,</span> <span class="n">data_test</span><span class="p">,</span> <span class="n">val_tgt_sentences</span><span class="p">,</span> <span class="n">test_tgt_sentences</span><span class="p">,</span> <span class="n">src_vocab</span><span class="p">,</span> <span class="n">tgt_vocab</span> <span class="o">=</span> \ |
| <span class="n">dataprocessor</span><span class="o">.</span><span class="n">load_translation_data</span><span class="p">(</span> |
| <span class="n">dataset</span><span class="o">=</span><span class="n">dataset</span><span class="p">,</span> |
| <span class="n">src_lang</span><span class="o">=</span><span class="n">hparams</span><span class="o">.</span><span class="n">src_lang</span><span class="p">,</span> |
| <span class="n">tgt_lang</span><span class="o">=</span><span class="n">hparams</span><span class="o">.</span><span class="n">tgt_lang</span><span class="p">)</span> |
| |
| <span class="n">data_train_lengths</span> <span class="o">=</span> <span class="n">dataprocessor</span><span class="o">.</span><span class="n">get_data_lengths</span><span class="p">(</span><span class="n">data_train</span><span class="p">)</span> |
| <span class="n">data_val_lengths</span> <span class="o">=</span> <span class="n">dataprocessor</span><span class="o">.</span><span class="n">get_data_lengths</span><span class="p">(</span><span class="n">data_val</span><span class="p">)</span> |
| <span class="n">data_test_lengths</span> <span class="o">=</span> <span class="n">dataprocessor</span><span class="o">.</span><span class="n">get_data_lengths</span><span class="p">(</span><span class="n">data_test</span><span class="p">)</span> |
| |
| <span class="n">data_train</span> <span class="o">=</span> <span class="n">data_train</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="k">lambda</span> <span class="n">src</span><span class="p">,</span> <span class="n">tgt</span><span class="p">:</span> <span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">tgt</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">src</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">tgt</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">data_val</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">SimpleDataset</span><span class="p">([(</span><span class="n">ele</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ele</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="nb">len</span><span class="p">(</span><span class="n">ele</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="nb">len</span><span class="p">(</span><span class="n">ele</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">i</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">ele</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">data_val</span><span class="p">)])</span> |
| <span class="n">data_test</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">SimpleDataset</span><span class="p">([(</span><span class="n">ele</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ele</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="nb">len</span><span class="p">(</span><span class="n">ele</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="nb">len</span><span class="p">(</span><span class="n">ele</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">i</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">ele</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">data_test</span><span class="p">)])</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="create-sampler-and-dataloader-for-toy-dataset"> |
| <h3>Create Sampler and DataLoader for TOY Dataset<a class="headerlink" href="#create-sampler-and-dataloader-for-toy-dataset" title="Permalink to this headline">¶</a></h3> |
| <p>Now, we have obtained <code class="docutils literal notranslate"><span class="pre">data_train</span></code>, <code class="docutils literal notranslate"><span class="pre">data_val</span></code>, and <code class="docutils literal notranslate"><span class="pre">data_test</span></code>. |
| The next step is to construct sampler and DataLoader. The first step is |
| to construct batchify function, which pads and stacks sequences to form |
| mini-batch.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">train_batchify_fn</span> <span class="o">=</span> <span class="n">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">batchify</span><span class="o">.</span><span class="n">Tuple</span><span class="p">(</span> |
| <span class="n">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">batchify</span><span class="o">.</span><span class="n">Pad</span><span class="p">(),</span> |
| <span class="n">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">batchify</span><span class="o">.</span><span class="n">Pad</span><span class="p">(),</span> |
| <span class="n">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">batchify</span><span class="o">.</span><span class="n">Stack</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">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">batchify</span><span class="o">.</span><span class="n">Stack</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">test_batchify_fn</span> <span class="o">=</span> <span class="n">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">batchify</span><span class="o">.</span><span class="n">Tuple</span><span class="p">(</span> |
| <span class="n">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">batchify</span><span class="o">.</span><span class="n">Pad</span><span class="p">(),</span> |
| <span class="n">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">batchify</span><span class="o">.</span><span class="n">Pad</span><span class="p">(),</span> |
| <span class="n">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">batchify</span><span class="o">.</span><span class="n">Stack</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">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">batchify</span><span class="o">.</span><span class="n">Stack</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">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">batchify</span><span class="o">.</span><span class="n">Stack</span><span class="p">())</span> |
| |
| <span class="n">target_val_lengths</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">data_val_lengths</span><span class="p">))</span> |
| <span class="n">target_test_lengths</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">data_test_lengths</span><span class="p">))</span> |
| </pre></div> |
| </div> |
| <p>We can then construct bucketing samplers, which generate batches by |
| grouping sequences with similar lengths.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">bucket_scheme</span> <span class="o">=</span> <span class="n">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">ExpWidthBucket</span><span class="p">(</span><span class="n">bucket_len_step</span><span class="o">=</span><span class="mf">1.2</span><span class="p">)</span> |
| <span class="n">train_batch_sampler</span> <span class="o">=</span> <span class="n">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">FixedBucketSampler</span><span class="p">(</span><span class="n">lengths</span><span class="o">=</span><span class="n">data_train_lengths</span><span class="p">,</span> |
| <span class="n">batch_size</span><span class="o">=</span><span class="n">hparams</span><span class="o">.</span><span class="n">batch_size</span><span class="p">,</span> |
| <span class="n">num_buckets</span><span class="o">=</span><span class="n">hparams</span><span class="o">.</span><span class="n">num_buckets</span><span class="p">,</span> |
| <span class="n">ratio</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> |
| <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">use_average_length</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">num_shards</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> |
| <span class="n">bucket_scheme</span><span class="o">=</span><span class="n">bucket_scheme</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Train Batch Sampler:'</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="n">train_batch_sampler</span><span class="o">.</span><span class="n">stats</span><span class="p">())</span> |
| |
| |
| <span class="n">val_batch_sampler</span> <span class="o">=</span> <span class="n">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">FixedBucketSampler</span><span class="p">(</span><span class="n">lengths</span><span class="o">=</span><span class="n">target_val_lengths</span><span class="p">,</span> |
| <span class="n">batch_size</span><span class="o">=</span><span class="n">hparams</span><span class="o">.</span><span class="n">test_batch_size</span><span class="p">,</span> |
| <span class="n">num_buckets</span><span class="o">=</span><span class="n">hparams</span><span class="o">.</span><span class="n">num_buckets</span><span class="p">,</span> |
| <span class="n">ratio</span><span class="o">=</span><span class="mf">0.0</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">use_average_length</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">bucket_scheme</span><span class="o">=</span><span class="n">bucket_scheme</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Validation Batch Sampler:'</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="n">val_batch_sampler</span><span class="o">.</span><span class="n">stats</span><span class="p">())</span> |
| |
| <span class="n">test_batch_sampler</span> <span class="o">=</span> <span class="n">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">FixedBucketSampler</span><span class="p">(</span><span class="n">lengths</span><span class="o">=</span><span class="n">target_test_lengths</span><span class="p">,</span> |
| <span class="n">batch_size</span><span class="o">=</span><span class="n">hparams</span><span class="o">.</span><span class="n">test_batch_size</span><span class="p">,</span> |
| <span class="n">num_buckets</span><span class="o">=</span><span class="n">hparams</span><span class="o">.</span><span class="n">num_buckets</span><span class="p">,</span> |
| <span class="n">ratio</span><span class="o">=</span><span class="mf">0.0</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">use_average_length</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">bucket_scheme</span><span class="o">=</span><span class="n">bucket_scheme</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Test Batch Sampler:'</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="n">test_batch_sampler</span><span class="o">.</span><span class="n">stats</span><span class="p">())</span> |
| </pre></div> |
| </div> |
| <p>Given the samplers, we can create DataLoader, which is iterable. Note |
| that the data loader of validation and test dataset share the same |
| batchifying function <code class="docutils literal notranslate"><span class="pre">test_batchify_fn</span></code>.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">train_data_loader</span> <span class="o">=</span> <span class="n">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">ShardedDataLoader</span><span class="p">(</span><span class="n">data_train</span><span class="p">,</span> |
| <span class="n">batch_sampler</span><span class="o">=</span><span class="n">train_batch_sampler</span><span class="p">,</span> |
| <span class="n">batchify_fn</span><span class="o">=</span><span class="n">train_batchify_fn</span><span class="p">,</span> |
| <span class="n">num_workers</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Length of train_data_loader: </span><span class="si">%d</span><span class="s1">'</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_data_loader</span><span class="p">))</span> |
| <span class="n">val_data_loader</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">data_val</span><span class="p">,</span> |
| <span class="n">batch_sampler</span><span class="o">=</span><span class="n">val_batch_sampler</span><span class="p">,</span> |
| <span class="n">batchify_fn</span><span class="o">=</span><span class="n">test_batchify_fn</span><span class="p">,</span> |
| <span class="n">num_workers</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Length of val_data_loader: </span><span class="si">%d</span><span class="s1">'</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">val_data_loader</span><span class="p">))</span> |
| <span class="n">test_data_loader</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">data_test</span><span class="p">,</span> |
| <span class="n">batch_sampler</span><span class="o">=</span><span class="n">test_batch_sampler</span><span class="p">,</span> |
| <span class="n">batchify_fn</span><span class="o">=</span><span class="n">test_batchify_fn</span><span class="p">,</span> |
| <span class="n">num_workers</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Length of test_data_loader: </span><span class="si">%d</span><span class="s1">'</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_data_loader</span><span class="p">))</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="define-transformer-model"> |
| <h3>Define Transformer Model<a class="headerlink" href="#define-transformer-model" title="Permalink to this headline">¶</a></h3> |
| <p>After obtaining DataLoader, we then start to define the Transformer. The |
| encoder and decoder of the Transformer can be easily obtained by calling |
| <code class="docutils literal notranslate"><span class="pre">get_transformer_encoder_decoder</span></code> function. Then, we use the encoder |
| and decoder in <code class="docutils literal notranslate"><span class="pre">NMTModel</span></code> to construct the Transformer model. |
| <code class="docutils literal notranslate"><span class="pre">model.hybridize</span></code> allows computation to be done using symbolic |
| backend. We also use <code class="docutils literal notranslate"><span class="pre">label_smoothing</span></code>.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">encoder</span><span class="p">,</span> <span class="n">decoder</span> <span class="o">=</span> <span class="n">nmt</span><span class="o">.</span><span class="n">transformer</span><span class="o">.</span><span class="n">get_transformer_encoder_decoder</span><span class="p">(</span><span class="n">units</span><span class="o">=</span><span class="n">hparams</span><span class="o">.</span><span class="n">num_units</span><span class="p">,</span> |
| <span class="n">hidden_size</span><span class="o">=</span><span class="n">hparams</span><span class="o">.</span><span class="n">hidden_size</span><span class="p">,</span> |
| <span class="n">dropout</span><span class="o">=</span><span class="n">hparams</span><span class="o">.</span><span class="n">dropout</span><span class="p">,</span> |
| <span class="n">num_layers</span><span class="o">=</span><span class="n">hparams</span><span class="o">.</span><span class="n">num_layers</span><span class="p">,</span> |
| <span class="n">num_heads</span><span class="o">=</span><span class="n">hparams</span><span class="o">.</span><span class="n">num_heads</span><span class="p">,</span> |
| <span class="n">max_src_length</span><span class="o">=</span><span class="mi">530</span><span class="p">,</span> |
| <span class="n">max_tgt_length</span><span class="o">=</span><span class="mi">549</span><span class="p">,</span> |
| <span class="n">scaled</span><span class="o">=</span><span class="n">hparams</span><span class="o">.</span><span class="n">scaled</span><span class="p">)</span> |
| <span class="n">model</span> <span class="o">=</span> <span class="n">nmt</span><span class="o">.</span><span class="n">translation</span><span class="o">.</span><span class="n">NMTModel</span><span class="p">(</span><span class="n">src_vocab</span><span class="o">=</span><span class="n">src_vocab</span><span class="p">,</span> <span class="n">tgt_vocab</span><span class="o">=</span><span class="n">tgt_vocab</span><span class="p">,</span> <span class="n">encoder</span><span class="o">=</span><span class="n">encoder</span><span class="p">,</span> <span class="n">decoder</span><span class="o">=</span><span class="n">decoder</span><span class="p">,</span> |
| <span class="n">share_embed</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">embed_size</span><span class="o">=</span><span class="n">hparams</span><span class="o">.</span><span class="n">num_units</span><span class="p">,</span> <span class="n">tie_weights</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">embed_initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="s1">'transformer_'</span><span class="p">)</span> |
| <span class="n">model</span><span class="o">.</span><span class="n">initialize</span><span class="p">(</span><span class="n">init</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">Xavier</span><span class="p">(</span><span class="n">magnitude</span><span class="o">=</span><span class="mf">3.0</span><span class="p">),</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">)</span> |
| <span class="n">model</span><span class="o">.</span><span class="n">hybridize</span><span class="p">()</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="p">)</span> |
| |
| <span class="n">label_smoothing</span> <span class="o">=</span> <span class="n">nmt</span><span class="o">.</span><span class="n">loss</span><span class="o">.</span><span class="n">LabelSmoothing</span><span class="p">(</span><span class="n">epsilon</span><span class="o">=</span><span class="n">hparams</span><span class="o">.</span><span class="n">epsilon</span><span class="p">,</span> <span class="n">units</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">tgt_vocab</span><span class="p">))</span> |
| <span class="n">label_smoothing</span><span class="o">.</span><span class="n">hybridize</span><span class="p">()</span> |
| |
| <span class="n">loss_function</span> <span class="o">=</span> <span class="n">nmt</span><span class="o">.</span><span class="n">loss</span><span class="o">.</span><span class="n">SoftmaxCEMaskedLoss</span><span class="p">(</span><span class="n">sparse_label</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="n">loss_function</span><span class="o">.</span><span class="n">hybridize</span><span class="p">()</span> |
| |
| <span class="n">test_loss_function</span> <span class="o">=</span> <span class="n">nmt</span><span class="o">.</span><span class="n">loss</span><span class="o">.</span><span class="n">SoftmaxCEMaskedLoss</span><span class="p">()</span> |
| <span class="n">test_loss_function</span><span class="o">.</span><span class="n">hybridize</span><span class="p">()</span> |
| |
| <span class="n">detokenizer</span> <span class="o">=</span> <span class="n">nlp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">SacreMosesDetokenizer</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| <p>Here, we build the translator using the beam search</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">translator</span> <span class="o">=</span> <span class="n">nmt</span><span class="o">.</span><span class="n">translation</span><span class="o">.</span><span class="n">BeamSearchTranslator</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span> |
| <span class="n">beam_size</span><span class="o">=</span><span class="n">hparams</span><span class="o">.</span><span class="n">beam_size</span><span class="p">,</span> |
| <span class="n">scorer</span><span class="o">=</span><span class="n">nlp</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">BeamSearchScorer</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="n">hparams</span><span class="o">.</span><span class="n">lp_alpha</span><span class="p">,</span> |
| <span class="n">K</span><span class="o">=</span><span class="n">hparams</span><span class="o">.</span><span class="n">lp_k</span><span class="p">),</span> |
| <span class="n">max_length</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Use beam_size=</span><span class="si">%d</span><span class="s1">, alpha=</span><span class="si">%.2f</span><span class="s1">, K=</span><span class="si">%d</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="n">hparams</span><span class="o">.</span><span class="n">beam_size</span><span class="p">,</span> <span class="n">hparams</span><span class="o">.</span><span class="n">lp_alpha</span><span class="p">,</span> <span class="n">hparams</span><span class="o">.</span><span class="n">lp_k</span><span class="p">))</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="training-loop"> |
| <h3>Training Loop<a class="headerlink" href="#training-loop" title="Permalink to this headline">¶</a></h3> |
| <p>Before conducting training, we need to create trainer for updating the |
| parameter. In the following example, we create a trainer that uses ADAM |
| optimzier.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">trainer</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">Trainer</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(),</span> <span class="n">hparams</span><span class="o">.</span><span class="n">optimizer</span><span class="p">,</span> |
| <span class="p">{</span><span class="s1">'learning_rate'</span><span class="p">:</span> <span class="n">hparams</span><span class="o">.</span><span class="n">lr</span><span class="p">,</span> <span class="s1">'beta2'</span><span class="p">:</span> <span class="mf">0.98</span><span class="p">,</span> <span class="s1">'epsilon'</span><span class="p">:</span> <span class="mf">1e-9</span><span class="p">})</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Use learning_rate=</span><span class="si">%.2f</span><span class="s1">'</span> |
| <span class="o">%</span> <span class="p">(</span><span class="n">trainer</span><span class="o">.</span><span class="n">learning_rate</span><span class="p">))</span> |
| </pre></div> |
| </div> |
| <p>We can then write the training loop. During the training, we perform the |
| evaluation on validation and testing dataset every epoch, and record the |
| parameters that give the hightest BLEU score on validation dataset. |
| Before performing forward and backward, we first use <code class="docutils literal notranslate"><span class="pre">as_in_context</span></code> |
| function to copy the mini-batch to GPU. The statement |
| <code class="docutils literal notranslate"><span class="pre">with</span> <span class="pre">mx.autograd.record()</span></code> will locate Gluon backend to compute the |
| gradients for the part inside the block. For ease of observing the |
| convergence of the update of the <code class="docutils literal notranslate"><span class="pre">Loss</span></code> in a quick fashion, we set the |
| <code class="docutils literal notranslate"><span class="pre">epochs</span> <span class="pre">=</span> <span class="pre">3</span></code>. Notice that, in order to obtain the best BLEU score, we |
| will need more epochs and large warmup steps following the original |
| paper as you can find the SOTA results in the first subsection. Besides, |
| we use Averaging SGD [2] to update the parameters, since it is more |
| robust for the machine translation task.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">best_valid_loss</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="s1">'Inf'</span><span class="p">)</span> |
| <span class="n">step_num</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="c1">#We use warmup steps as introduced in [1].</span> |
| <span class="n">warmup_steps</span> <span class="o">=</span> <span class="n">hparams</span><span class="o">.</span><span class="n">warmup_steps</span> |
| <span class="n">grad_interval</span> <span class="o">=</span> <span class="n">hparams</span><span class="o">.</span><span class="n">num_accumulated</span> |
| <span class="n">model</span><span class="o">.</span><span class="n">setattr</span><span class="p">(</span><span class="s1">'grad_req'</span><span class="p">,</span> <span class="s1">'add'</span><span class="p">)</span> |
| <span class="c1">#We use Averaging SGD [2] to update the parameters.</span> |
| <span class="n">average_start</span> <span class="o">=</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">train_data_loader</span><span class="p">)</span> <span class="o">//</span> <span class="n">grad_interval</span><span class="p">)</span> <span class="o">*</span> \ |
| <span class="p">(</span><span class="n">hparams</span><span class="o">.</span><span class="n">epochs</span> <span class="o">-</span> <span class="n">hparams</span><span class="o">.</span><span class="n">average_start</span><span class="p">)</span> |
| <span class="n">average_param_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">])</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> |
| <span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">()</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span> |
| <span class="n">update_average_param_dict</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="n">model</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">epoch_id</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">hparams</span><span class="o">.</span><span class="n">epochs</span><span class="p">):</span> |
| <span class="n">utils</span><span class="o">.</span><span class="n">train_one_epoch</span><span class="p">(</span><span class="n">epoch_id</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">train_data_loader</span><span class="p">,</span> <span class="n">trainer</span><span class="p">,</span> |
| <span class="n">label_smoothing</span><span class="p">,</span> <span class="n">loss_function</span><span class="p">,</span> <span class="n">grad_interval</span><span class="p">,</span> |
| <span class="n">average_param_dict</span><span class="p">,</span> <span class="n">update_average_param_dict</span><span class="p">,</span> |
| <span class="n">step_num</span><span class="p">,</span> <span class="n">ctx</span><span class="p">)</span> |
| <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">waitall</span><span class="p">()</span> |
| <span class="c1"># We define evaluation function as follows. The `evaluate` function use beam search translator</span> |
| <span class="c1"># to generate outputs for the validation and testing datasets.</span> |
| <span class="n">valid_loss</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">utils</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">val_data_loader</span><span class="p">,</span> |
| <span class="n">test_loss_function</span><span class="p">,</span> <span class="n">translator</span><span class="p">,</span> |
| <span class="n">tgt_vocab</span><span class="p">,</span> <span class="n">detokenizer</span><span class="p">,</span> <span class="n">ctx</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Epoch </span><span class="si">%d</span><span class="s1">, valid Loss=</span><span class="si">%.4f</span><span class="s1">, valid ppl=</span><span class="si">%.4f</span><span class="s1">'</span> |
| <span class="o">%</span> <span class="p">(</span><span class="n">epoch_id</span><span class="p">,</span> <span class="n">valid_loss</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">valid_loss</span><span class="p">)))</span> |
| <span class="n">test_loss</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">utils</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">test_data_loader</span><span class="p">,</span> |
| <span class="n">test_loss_function</span><span class="p">,</span> <span class="n">translator</span><span class="p">,</span> |
| <span class="n">tgt_vocab</span><span class="p">,</span> <span class="n">detokenizer</span><span class="p">,</span> <span class="n">ctx</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Epoch </span><span class="si">%d</span><span class="s1">, test Loss=</span><span class="si">%.4f</span><span class="s1">, test ppl=</span><span class="si">%.4f</span><span class="s1">'</span> |
| <span class="o">%</span> <span class="p">(</span><span class="n">epoch_id</span><span class="p">,</span> <span class="n">test_loss</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">test_loss</span><span class="p">)))</span> |
| <span class="k">if</span> <span class="n">valid_loss</span> <span class="o"><</span> <span class="n">best_valid_loss</span><span class="p">:</span> |
| <span class="n">best_valid_loss</span> <span class="o">=</span> <span class="n">valid_loss</span> |
| <span class="n">model</span><span class="o">.</span><span class="n">save_parameters</span><span class="p">(</span><span class="s1">'</span><span class="si">{}</span><span class="s1">.</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">hparams</span><span class="o">.</span><span class="n">save_dir</span><span class="p">,</span> <span class="s1">'valid_best.params'</span><span class="p">))</span> |
| <span class="n">model</span><span class="o">.</span><span class="n">save_parameters</span><span class="p">(</span><span class="s1">'</span><span class="si">{}</span><span class="s1">.epoch</span><span class="si">{:d}</span><span class="s1">.params'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">hparams</span><span class="o">.</span><span class="n">save_dir</span><span class="p">,</span> <span class="n">epoch_id</span><span class="p">))</span> |
| <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'</span><span class="si">{}</span><span class="s1">.</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">hparams</span><span class="o">.</span><span class="n">save_dir</span><span class="p">,</span> <span class="s1">'average.params'</span><span class="p">),</span> <span class="n">average_param_dict</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="n">hparams</span><span class="o">.</span><span class="n">average_start</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">()</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="n">v</span><span class="o">.</span><span class="n">set_data</span><span class="p">(</span><span class="n">average_param_dict</span><span class="p">[</span><span class="n">k</span><span class="p">])</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">model</span><span class="o">.</span><span class="n">load_parameters</span><span class="p">(</span><span class="s1">'</span><span class="si">{}</span><span class="s1">.</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">hparams</span><span class="o">.</span><span class="n">save_dir</span><span class="p">,</span> <span class="s1">'valid_best.params'</span><span class="p">),</span> <span class="n">ctx</span><span class="p">)</span> |
| <span class="n">valid_loss</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">utils</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">val_data_loader</span><span class="p">,</span> |
| <span class="n">test_loss_function</span><span class="p">,</span> <span class="n">translator</span><span class="p">,</span> |
| <span class="n">tgt_vocab</span><span class="p">,</span> <span class="n">detokenizer</span><span class="p">,</span> <span class="n">ctx</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Best model valid Loss=</span><span class="si">%.4f</span><span class="s1">, valid ppl=</span><span class="si">%.4f</span><span class="s1">'</span> |
| <span class="o">%</span> <span class="p">(</span><span class="n">valid_loss</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">valid_loss</span><span class="p">)))</span> |
| <span class="n">test_loss</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">utils</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">test_data_loader</span><span class="p">,</span> |
| <span class="n">test_loss_function</span><span class="p">,</span> <span class="n">translator</span><span class="p">,</span> |
| <span class="n">tgt_vocab</span><span class="p">,</span> <span class="n">detokenizer</span><span class="p">,</span> <span class="n">ctx</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Best model test Loss=</span><span class="si">%.4f</span><span class="s1">, test ppl=</span><span class="si">%.4f</span><span class="s1">'</span> |
| <span class="o">%</span> <span class="p">(</span><span class="n">test_loss</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">test_loss</span><span class="p">)))</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="conclusion"> |
| <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">¶</a></h2> |
| <ul class="simple"> |
| <li><p>Showcase with Transformer, we are able to support the deep neural |
| networks for seq2seq task. We have already achieved SOTA results on |
| the WMT 2014 English-German task.</p></li> |
| <li><p>Gluon NLP Toolkit provides high-level APIs that could drastically |
| simplify the development process of modeling for NLP tasks sharing |
| the encoder-decoder structure.</p></li> |
| <li><p>Low-level APIs in NLP Toolkit enables easy customization.</p></li> |
| </ul> |
| <p>Documentation can be found at <a class="reference external" href="https://gluon-nlp.mxnet.io/index.html">https://gluon-nlp.mxnet.io/index.html</a></p> |
| <p>Code is here <a class="reference external" href="https://github.com/dmlc/gluon-nlp">https://github.com/dmlc/gluon-nlp</a></p> |
| </div> |
| <div class="section" id="references"> |
| <h2>References<a class="headerlink" href="#references" title="Permalink to this headline">¶</a></h2> |
| <p>[1] Vaswani, Ashish, et al. “Attention is all you need.” Advances in |
| Neural Information Processing Systems. 2017.</p> |
| <p>[2] Polyak, Boris T, and Anatoli B. Juditsky. “Acceleration of |
| stochastic approximation by averaging.” SIAM Journal on Control and |
| Optimization. 1992.</p> |
| </div> |
| </div> |
| |
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| <div class="side-doc-outline"> |
| <div class="side-doc-outline--content"> |
| <div class="localtoc"> |
| <p class="caption"> |
| <span class="caption-text">Table Of Contents</span> |
| </p> |
| <ul> |
| <li><a class="reference internal" href="#">Machine Translation with Transformer</a><ul> |
| <li><a class="reference internal" href="#preparation">Preparation</a><ul> |
| <li><a class="reference internal" href="#load-mxnet-and-gluonnlp">Load MXNet and GluonNLP</a></li> |
| <li><a class="reference internal" href="#set-environment">Set Environment</a></li> |
| </ul> |
| </li> |
| <li><a class="reference internal" href="#use-the-sota-pretrained-transformer-model">Use the SOTA Pretrained Transformer model</a><ul> |
| <li><a class="reference internal" href="#get-the-sota-transformer">Get the SOTA Transformer</a></li> |
| <li><a class="reference internal" href="#load-and-preprocess-wmt-2014-dataset">Load and Preprocess WMT 2014 Dataset</a></li> |
| <li><a class="reference internal" href="#create-sampler-and-dataloader-for-wmt-2014-dataset">Create Sampler and DataLoader for WMT 2014 Dataset</a></li> |
| <li><a class="reference internal" href="#evaluate-transformer">Evaluate Transformer</a></li> |
| <li><a class="reference internal" href="#translation-inference">Translation Inference</a></li> |
| </ul> |
| </li> |
| <li><a class="reference internal" href="#train-your-own-transformer">Train Your Own Transformer</a><ul> |
| <li><a class="reference internal" href="#load-and-preprocess-toy-dataset">Load and Preprocess TOY Dataset</a></li> |
| <li><a class="reference internal" href="#create-sampler-and-dataloader-for-toy-dataset">Create Sampler and DataLoader for TOY Dataset</a></li> |
| <li><a class="reference internal" href="#define-transformer-model">Define Transformer Model</a></li> |
| <li><a class="reference internal" href="#training-loop">Training Loop</a></li> |
| </ul> |
| </li> |
| <li><a class="reference internal" href="#conclusion">Conclusion</a></li> |
| <li><a class="reference internal" href="#references">References</a></li> |
| </ul> |
| </li> |
| </ul> |
| |
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