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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-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/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 current"><a class="current reference internal" href="#">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> |
| </li> |
| <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 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> |
| </li> |
| <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 current"><a class="current reference internal" href="#">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
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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> |
| </li> |
| <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> |
| </li> |
| </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="../../onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../../api/np/routines.io.html">Input and output</a><ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../../api/np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.linalg.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="google-neural-machine-translation"> |
| <h1>Google Neural Machine Translation<a class="headerlink" href="#google-neural-machine-translation" title="Permalink to this headline">¶</a></h1> |
| <p>In this notebook, we are going to train Google NMT on IWSLT 2015 |
| English-Vietnamese Dataset. The building process includes four steps: 1) |
| load and process dataset, 2) create sampler and DataLoader, 3) build |
| model, and 4) write training epochs.</p> |
| <div class="section" id="load-mxnet-and-gluon"> |
| <h2>Load MXNET and Gluon<a class="headerlink" href="#load-mxnet-and-gluon" title="Permalink to this headline">¶</a></h2> |
| <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">argparse</span> |
| <span class="kn">import</span> <span class="nn">time</span> |
| <span class="kn">import</span> <span class="nn">random</span> |
| <span class="kn">import</span> <span class="nn">os</span> |
| <span class="kn">import</span> <span class="nn">logging</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> |
| <span class="kn">import</span> <span class="nn">nmt</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="hyper-parameters"> |
| <h2>Hyper-parameters<a class="headerlink" href="#hyper-parameters" title="Permalink to this headline">¶</a></h2> |
| <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">device</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> |
| |
| <span class="c1"># parameters for dataset</span> |
| <span class="n">dataset</span> <span class="o">=</span> <span class="s1">'IWSLT2015'</span> |
| <span class="n">src_lang</span><span class="p">,</span> <span class="n">tgt_lang</span> <span class="o">=</span> <span class="s1">'en'</span><span class="p">,</span> <span class="s1">'vi'</span> |
| <span class="n">src_max_len</span><span class="p">,</span> <span class="n">tgt_max_len</span> <span class="o">=</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">50</span> |
| |
| <span class="c1"># parameters for model</span> |
| <span class="n">num_hidden</span> <span class="o">=</span> <span class="mi">512</span> |
| <span class="n">num_layers</span> <span class="o">=</span> <span class="mi">2</span> |
| <span class="n">num_bi_layers</span> <span class="o">=</span> <span class="mi">1</span> |
| <span class="n">dropout</span> <span class="o">=</span> <span class="mf">0.2</span> |
| |
| <span class="c1"># parameters for training</span> |
| <span class="n">batch_size</span><span class="p">,</span> <span class="n">test_batch_size</span> <span class="o">=</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">32</span> |
| <span class="n">num_buckets</span> <span class="o">=</span> <span class="mi">5</span> |
| <span class="n">epochs</span> <span class="o">=</span> <span class="mi">1</span> |
| <span class="n">clip</span> <span class="o">=</span> <span class="mi">5</span> |
| <span class="n">lr</span> <span class="o">=</span> <span class="mf">0.001</span> |
| <span class="n">lr_update_factor</span> <span class="o">=</span> <span class="mf">0.5</span> |
| <span class="n">log_interval</span> <span class="o">=</span> <span class="mi">10</span> |
| <span class="n">save_dir</span> <span class="o">=</span> <span class="s1">'gnmt_en_vi_u512'</span> |
| |
| <span class="c1">#parameters for testing</span> |
| <span class="n">beam_size</span> <span class="o">=</span> <span class="mi">10</span> |
| <span class="n">lp_alpha</span> <span class="o">=</span> <span class="mf">1.0</span> |
| <span class="n">lp_k</span> <span class="o">=</span> <span class="mi">5</span> |
| |
| <span class="n">nmt</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">logging_config</span><span class="p">(</span><span class="n">save_dir</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="load-and-preprocess-dataset"> |
| <h2>Load and Preprocess Dataset<a class="headerlink" href="#load-and-preprocess-dataset" title="Permalink to this headline">¶</a></h2> |
| <p>The following shows how to process the dataset and cache the processed |
| dataset for future use. The processing steps include: 1) clip the source |
| and target sequences, 2) split the string input to a list of tokens, 3) |
| map the string token into its integer index in the vocabulary, and 4) |
| append end-of-sentence (EOS) token to source sentence and add BOS and |
| EOS tokens to target sentence.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">cache_dataset</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">prefix</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Cache the processed npy dataset the dataset into a npz</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> dataset : gluon.data.SimpleDataset</span> |
| <span class="sd"> file_path : str</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">nmt</span><span class="o">.</span><span class="n">_constants</span><span class="o">.</span><span class="n">CACHE_PATH</span><span class="p">):</span> |
| <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">nmt</span><span class="o">.</span><span class="n">_constants</span><span class="o">.</span><span class="n">CACHE_PATH</span><span class="p">)</span> |
| <span class="n">src_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</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="k">for</span> <span class="n">ele</span> <span class="ow">in</span> <span class="n">dataset</span><span class="p">])</span> |
| <span class="n">tgt_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</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="k">for</span> <span class="n">ele</span> <span class="ow">in</span> <span class="n">dataset</span><span class="p">])</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">savez</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">nmt</span><span class="o">.</span><span class="n">_constants</span><span class="o">.</span><span class="n">CACHE_PATH</span><span class="p">,</span> <span class="n">prefix</span> <span class="o">+</span> <span class="s1">'.npz'</span><span class="p">),</span> <span class="n">src_data</span><span class="o">=</span><span class="n">src_data</span><span class="p">,</span> <span class="n">tgt_data</span><span class="o">=</span><span class="n">tgt_data</span><span class="p">)</span> |
| |
| |
| <span class="k">def</span> <span class="nf">load_cached_dataset</span><span class="p">(</span><span class="n">prefix</span><span class="p">):</span> |
| <span class="n">cached_file_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">nmt</span><span class="o">.</span><span class="n">_constants</span><span class="o">.</span><span class="n">CACHE_PATH</span><span class="p">,</span> <span class="n">prefix</span> <span class="o">+</span> <span class="s1">'.npz'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">cached_file_path</span><span class="p">):</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Load cached data from </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">cached_file_path</span><span class="p">))</span> |
| <span class="n">dat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">cached_file_path</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">ArrayDataset</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">dat</span><span class="p">[</span><span class="s1">'src_data'</span><span class="p">]),</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">dat</span><span class="p">[</span><span class="s1">'tgt_data'</span><span class="p">]))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="kc">None</span> |
| |
| |
| <span class="k">class</span> <span class="nc">TrainValDataTransform</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Transform the machine translation dataset.</span> |
| |
| <span class="sd"> Clip source and the target sentences to the maximum length. For the source sentence, append the</span> |
| <span class="sd"> EOS. For the target sentence, append BOS and EOS.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> src_vocab : Vocab</span> |
| <span class="sd"> tgt_vocab : Vocab</span> |
| <span class="sd"> src_max_len : int</span> |
| <span class="sd"> tgt_max_len : int</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">src_vocab</span><span class="p">,</span> <span class="n">tgt_vocab</span><span class="p">,</span> <span class="n">src_max_len</span><span class="p">,</span> <span class="n">tgt_max_len</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_src_vocab</span> <span class="o">=</span> <span class="n">src_vocab</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_tgt_vocab</span> <span class="o">=</span> <span class="n">tgt_vocab</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_src_max_len</span> <span class="o">=</span> <span class="n">src_max_len</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_tgt_max_len</span> <span class="o">=</span> <span class="n">tgt_max_len</span> |
| |
| <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</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="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_src_max_len</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">src_sentence</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_src_vocab</span><span class="p">[</span><span class="n">src</span><span class="o">.</span><span class="n">split</span><span class="p">()[:</span><span class="bp">self</span><span class="o">.</span><span class="n">_src_max_len</span><span class="p">]]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">src_sentence</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_src_vocab</span><span class="p">[</span><span class="n">src</span><span class="o">.</span><span class="n">split</span><span class="p">()]</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_tgt_max_len</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">tgt_sentence</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_tgt_vocab</span><span class="p">[</span><span class="n">tgt</span><span class="o">.</span><span class="n">split</span><span class="p">()[:</span><span class="bp">self</span><span class="o">.</span><span class="n">_tgt_max_len</span><span class="p">]]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">tgt_sentence</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_tgt_vocab</span><span class="p">[</span><span class="n">tgt</span><span class="o">.</span><span class="n">split</span><span class="p">()]</span> |
| <span class="n">src_sentence</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_src_vocab</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_src_vocab</span><span class="o">.</span><span class="n">eos_token</span><span class="p">])</span> |
| <span class="n">tgt_sentence</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_tgt_vocab</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_tgt_vocab</span><span class="o">.</span><span class="n">bos_token</span><span class="p">])</span> |
| <span class="n">tgt_sentence</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_tgt_vocab</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_tgt_vocab</span><span class="o">.</span><span class="n">eos_token</span><span class="p">])</span> |
| <span class="n">src_npy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">src_sentence</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span> |
| <span class="n">tgt_npy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">tgt_sentence</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">src_npy</span><span class="p">,</span> <span class="n">tgt_npy</span> |
| |
| |
| <span class="k">def</span> <span class="nf">process_dataset</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">src_vocab</span><span class="p">,</span> <span class="n">tgt_vocab</span><span class="p">,</span> <span class="n">src_max_len</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">tgt_max_len</span><span class="o">=-</span><span class="mi">1</span><span class="p">):</span> |
| <span class="n">start</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">dataset_processed</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">TrainValDataTransform</span><span class="p">(</span><span class="n">src_vocab</span><span class="p">,</span> <span class="n">tgt_vocab</span><span class="p">,</span> |
| <span class="n">src_max_len</span><span class="p">,</span> |
| <span class="n">tgt_max_len</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">end</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="nb">print</span><span class="p">(</span><span class="s1">'Processing time spent: </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">end</span> <span class="o">-</span> <span class="n">start</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">dataset_processed</span> |
| |
| |
| <span class="k">def</span> <span class="nf">load_translation_data</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">src_lang</span><span class="o">=</span><span class="s1">'en'</span><span class="p">,</span> <span class="n">tgt_lang</span><span class="o">=</span><span class="s1">'vi'</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Load translation dataset</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> dataset : str</span> |
| <span class="sd"> src_lang : str, default 'en'</span> |
| <span class="sd"> tgt_lang : str, default 'vi'</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> data_train_processed : Dataset</span> |
| <span class="sd"> The preprocessed training sentence pairs</span> |
| <span class="sd"> data_val_processed : Dataset</span> |
| <span class="sd"> The preprocessed validation sentence pairs</span> |
| <span class="sd"> data_test_processed : Dataset</span> |
| <span class="sd"> The preprocessed test sentence pairs</span> |
| <span class="sd"> val_tgt_sentences : list</span> |
| <span class="sd"> The target sentences in the validation set</span> |
| <span class="sd"> test_tgt_sentences : list</span> |
| <span class="sd"> The target sentences in the test set</span> |
| <span class="sd"> src_vocab : Vocab</span> |
| <span class="sd"> Vocabulary of the source language</span> |
| <span class="sd"> tgt_vocab : Vocab</span> |
| <span class="sd"> Vocabulary of the target language</span> |
| <span class="sd"> """</span> |
| <span class="n">common_prefix</span> <span class="o">=</span> <span class="s1">'IWSLT2015_</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</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">src_lang</span><span class="p">,</span> <span class="n">tgt_lang</span><span class="p">,</span> |
| <span class="n">src_max_len</span><span class="p">,</span> <span class="n">tgt_max_len</span><span class="p">)</span> |
| <span class="n">data_train</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">IWSLT2015</span><span class="p">(</span><span class="s1">'train'</span><span class="p">,</span> <span class="n">src_lang</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">tgt_lang</span><span class="p">)</span> |
| <span class="n">data_val</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">IWSLT2015</span><span class="p">(</span><span class="s1">'val'</span><span class="p">,</span> <span class="n">src_lang</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">tgt_lang</span><span class="p">)</span> |
| <span class="n">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">IWSLT2015</span><span class="p">(</span><span class="s1">'test'</span><span class="p">,</span> <span class="n">src_lang</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">tgt_lang</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">data_train</span><span class="o">.</span><span class="n">src_vocab</span><span class="p">,</span> <span class="n">data_train</span><span class="o">.</span><span class="n">tgt_vocab</span> |
| <span class="n">data_train_processed</span> <span class="o">=</span> <span class="n">load_cached_dataset</span><span class="p">(</span><span class="n">common_prefix</span> <span class="o">+</span> <span class="s1">'_train'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">data_train_processed</span><span class="p">:</span> |
| <span class="n">data_train_processed</span> <span class="o">=</span> <span class="n">process_dataset</span><span class="p">(</span><span class="n">data_train</span><span class="p">,</span> <span class="n">src_vocab</span><span class="p">,</span> <span class="n">tgt_vocab</span><span class="p">,</span> |
| <span class="n">src_max_len</span><span class="p">,</span> <span class="n">tgt_max_len</span><span class="p">)</span> |
| <span class="n">cache_dataset</span><span class="p">(</span><span class="n">data_train_processed</span><span class="p">,</span> <span class="n">common_prefix</span> <span class="o">+</span> <span class="s1">'_train'</span><span class="p">)</span> |
| <span class="n">data_val_processed</span> <span class="o">=</span> <span class="n">load_cached_dataset</span><span class="p">(</span><span class="n">common_prefix</span> <span class="o">+</span> <span class="s1">'_val'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">data_val_processed</span><span class="p">:</span> |
| <span class="n">data_val_processed</span> <span class="o">=</span> <span class="n">process_dataset</span><span class="p">(</span><span class="n">data_val</span><span class="p">,</span> <span class="n">src_vocab</span><span class="p">,</span> <span class="n">tgt_vocab</span><span class="p">)</span> |
| <span class="n">cache_dataset</span><span class="p">(</span><span class="n">data_val_processed</span><span class="p">,</span> <span class="n">common_prefix</span> <span class="o">+</span> <span class="s1">'_val'</span><span class="p">)</span> |
| <span class="n">data_test_processed</span> <span class="o">=</span> <span class="n">load_cached_dataset</span><span class="p">(</span><span class="n">common_prefix</span> <span class="o">+</span> <span class="s1">'_test'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">data_test_processed</span><span class="p">:</span> |
| <span class="n">data_test_processed</span> <span class="o">=</span> <span class="n">process_dataset</span><span class="p">(</span><span class="n">data_test</span><span class="p">,</span> <span class="n">src_vocab</span><span class="p">,</span> <span class="n">tgt_vocab</span><span class="p">)</span> |
| <span class="n">cache_dataset</span><span class="p">(</span><span class="n">data_test_processed</span><span class="p">,</span> <span class="n">common_prefix</span> <span class="o">+</span> <span class="s1">'_test'</span><span class="p">)</span> |
| <span class="n">fetch_tgt_sentence</span> <span class="o">=</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="o">.</span><span class="n">split</span><span class="p">()</span> |
| <span class="n">val_tgt_sentences</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">data_val</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">fetch_tgt_sentence</span><span class="p">))</span> |
| <span class="n">test_tgt_sentences</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">data_test</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">fetch_tgt_sentence</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">data_train_processed</span><span class="p">,</span> <span class="n">data_val_processed</span><span class="p">,</span> <span class="n">data_test_processed</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="k">def</span> <span class="nf">get_data_lengths</span><span class="p">(</span><span class="n">dataset</span><span class="p">):</span> |
| <span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="k">lambda</span> <span class="n">srg</span><span class="p">,</span> <span class="n">tgt</span><span class="p">:</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">srg</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">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">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">src_lang</span><span class="p">,</span> <span class="n">tgt_lang</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">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">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">get_data_lengths</span><span class="p">(</span><span class="n">data_test</span><span class="p">)</span> |
| |
| <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">save_dir</span><span class="p">,</span> <span class="s1">'val_gt.txt'</span><span class="p">),</span> <span class="s1">'w'</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">'utf-8'</span><span class="p">)</span> <span class="k">as</span> <span class="n">of</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">ele</span> <span class="ow">in</span> <span class="n">val_tgt_sentences</span><span class="p">:</span> |
| <span class="n">of</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">' '</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">ele</span><span class="p">)</span> <span class="o">+</span> <span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="p">)</span> |
| |
| <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">save_dir</span><span class="p">,</span> <span class="s1">'test_gt.txt'</span><span class="p">),</span> <span class="s1">'w'</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">'utf-8'</span><span class="p">)</span> <span class="k">as</span> <span class="n">of</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">ele</span> <span class="ow">in</span> <span class="n">test_tgt_sentences</span><span class="p">:</span> |
| <span class="n">of</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">' '</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">ele</span><span class="p">)</span> <span class="o">+</span> <span class="s1">'</span><span class="se">\n</span><span class="s1">'</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"> |
| <h2>Create Sampler and DataLoader<a class="headerlink" href="#create-sampler-and-dataloader" title="Permalink to this headline">¶</a></h2> |
| <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> |
| </pre></div> |
| </div> |
| <p>We can then construct bucketing samplers, which generate batches by |
| grouping sequences with similar lengths. Here, the bucketing scheme is |
| empirically determined.</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">batch_size</span><span class="p">,</span> |
| <span class="n">num_buckets</span><span class="o">=</span><span class="n">num_buckets</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">bucket_scheme</span><span class="o">=</span><span class="n">bucket_scheme</span><span class="p">)</span> |
| <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">'Train Batch Sampler:</span><span class="se">\n</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">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">data_val_lengths</span><span class="p">,</span> |
| <span class="n">batch_size</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">num_buckets</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">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">'Valid Batch Sampler:</span><span class="se">\n</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">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">data_test_lengths</span><span class="p">,</span> |
| <span class="n">batch_size</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">num_buckets</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">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">'Test Batch Sampler:</span><span class="se">\n</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">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">train_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_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">4</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">4</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">4</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="build-gnmt-model"> |
| <h2>Build GNMT Model<a class="headerlink" href="#build-gnmt-model" title="Permalink to this headline">¶</a></h2> |
| <p>After obtaining DataLoader, we can build the model. The GNMT encoder and |
| decoder can be easily constructed by calling |
| <code class="docutils literal notranslate"><span class="pre">get_gnmt_encoder_decoder</span></code> function. Then, we feed the encoder and |
| decoder to <code class="docutils literal notranslate"><span class="pre">NMTModel</span></code> to construct the GNMT model. <code class="docutils literal notranslate"><span class="pre">model.hybridize</span></code> |
| allows computation to be done using the symbolic backend.</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">gnmt</span><span class="o">.</span><span class="n">get_gnmt_encoder_decoder</span><span class="p">(</span><span class="n">hidden_size</span><span class="o">=</span><span class="n">num_hidden</span><span class="p">,</span> |
| <span class="n">dropout</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">num_layers</span><span class="p">,</span> |
| <span class="n">num_bi_layers</span><span class="o">=</span><span class="n">num_bi_layers</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">embed_size</span><span class="o">=</span><span class="n">num_hidden</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="s1">'gnmt_'</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">Uniform</span><span class="p">(</span><span class="mf">0.1</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span> |
| <span class="n">static_alloc</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="n">model</span><span class="o">.</span><span class="n">hybridize</span><span class="p">(</span><span class="n">static_alloc</span><span class="o">=</span><span class="n">static_alloc</span><span class="p">)</span> |
| <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="n">model</span><span class="p">)</span> |
| |
| <span class="c1"># Due to the paddings, we need to mask out the losses corresponding to padding tokens.</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">loss_function</span><span class="o">.</span><span class="n">hybridize</span><span class="p">(</span><span class="n">static_alloc</span><span class="o">=</span><span class="n">static_alloc</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>We also build the beam search translator.</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">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">lp_alpha</span><span class="p">,</span> |
| <span class="n">K</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="n">tgt_max_len</span> <span class="o">+</span> <span class="mi">100</span><span class="p">)</span> |
| <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">'Use beam_size=</span><span class="si">{}</span><span class="s1">, alpha=</span><span class="si">{}</span><span class="s1">, K=</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">beam_size</span><span class="p">,</span> <span class="n">lp_alpha</span><span class="p">,</span> <span class="n">lp_k</span><span class="p">))</span> |
| </pre></div> |
| </div> |
| <p>We define evaluation function as follows. The <code class="docutils literal notranslate"><span class="pre">evaluate</span></code> function use |
| beam search translator to generate outputs for the validation and |
| testing datasets.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">evaluate</span><span class="p">(</span><span class="n">data_loader</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Evaluate given the data loader</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> data_loader : gluon.data.DataLoader</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> avg_loss : float</span> |
| <span class="sd"> Average loss</span> |
| <span class="sd"> real_translation_out : list of list of str</span> |
| <span class="sd"> The translation output</span> |
| <span class="sd"> """</span> |
| <span class="n">translation_out</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">all_inst_ids</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">avg_loss_denom</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="n">avg_loss</span> <span class="o">=</span> <span class="mf">0.0</span> |
| <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="p">(</span><span class="n">src_seq</span><span class="p">,</span> <span class="n">tgt_seq</span><span class="p">,</span> <span class="n">src_valid_length</span><span class="p">,</span> <span class="n">tgt_valid_length</span><span class="p">,</span> <span class="n">inst_ids</span><span class="p">)</span> \ |
| <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">data_loader</span><span class="p">):</span> |
| <span class="n">src_seq</span> <span class="o">=</span> <span class="n">src_seq</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> |
| <span class="n">tgt_seq</span> <span class="o">=</span> <span class="n">tgt_seq</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> |
| <span class="n">src_valid_length</span> <span class="o">=</span> <span class="n">src_valid_length</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> |
| <span class="n">tgt_valid_length</span> <span class="o">=</span> <span class="n">tgt_valid_length</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> |
| <span class="c1"># Calculating Loss</span> |
| <span class="n">out</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">src_seq</span><span class="p">,</span> <span class="n">tgt_seq</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">src_valid_length</span><span class="p">,</span> <span class="n">tgt_valid_length</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> |
| <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_function</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">tgt_seq</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:],</span> <span class="n">tgt_valid_length</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">asscalar</span><span class="p">()</span> |
| <span class="n">all_inst_ids</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">inst_ids</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span> |
| <span class="n">avg_loss</span> <span class="o">+=</span> <span class="n">loss</span> <span class="o">*</span> <span class="p">(</span><span class="n">tgt_seq</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> |
| <span class="n">avg_loss_denom</span> <span class="o">+=</span> <span class="p">(</span><span class="n">tgt_seq</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> |
| <span class="c1"># Translate</span> |
| <span class="n">samples</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">sample_valid_length</span> <span class="o">=</span>\ |
| <span class="n">translator</span><span class="o">.</span><span class="n">translate</span><span class="p">(</span><span class="n">src_seq</span><span class="o">=</span><span class="n">src_seq</span><span class="p">,</span> <span class="n">src_valid_length</span><span class="o">=</span><span class="n">src_valid_length</span><span class="p">)</span> |
| <span class="n">max_score_sample</span> <span class="o">=</span> <span class="n">samples</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="n">sample_valid_length</span> <span class="o">=</span> <span class="n">sample_valid_length</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="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">max_score_sample</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span> |
| <span class="n">translation_out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> |
| <span class="p">[</span><span class="n">tgt_vocab</span><span class="o">.</span><span class="n">idx_to_token</span><span class="p">[</span><span class="n">ele</span><span class="p">]</span> <span class="k">for</span> <span class="n">ele</span> <span class="ow">in</span> |
| <span class="n">max_score_sample</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">:(</span><span class="n">sample_valid_length</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)]])</span> |
| <span class="n">avg_loss</span> <span class="o">=</span> <span class="n">avg_loss</span> <span class="o">/</span> <span class="n">avg_loss_denom</span> |
| <span class="n">real_translation_out</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">all_inst_ids</span><span class="p">))]</span> |
| <span class="k">for</span> <span class="n">ind</span><span class="p">,</span> <span class="n">sentence</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">all_inst_ids</span><span class="p">,</span> <span class="n">translation_out</span><span class="p">):</span> |
| <span class="n">real_translation_out</span><span class="p">[</span><span class="n">ind</span><span class="p">]</span> <span class="o">=</span> <span class="n">sentence</span> |
| <span class="k">return</span> <span class="n">avg_loss</span><span class="p">,</span> <span class="n">real_translation_out</span> |
| |
| |
| <span class="k">def</span> <span class="nf">write_sentences</span><span class="p">(</span><span class="n">sentences</span><span class="p">,</span> <span class="n">file_path</span><span class="p">):</span> |
| <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">file_path</span><span class="p">,</span> <span class="s1">'w'</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">'utf-8'</span><span class="p">)</span> <span class="k">as</span> <span class="n">of</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">sent</span> <span class="ow">in</span> <span class="n">sentences</span><span class="p">:</span> |
| <span class="n">of</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">' '</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">sent</span><span class="p">)</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="training-epochs"> |
| <h2>Training Epochs<a class="headerlink" href="#training-epochs" title="Permalink to this headline">¶</a></h2> |
| <p>Before entering the training stage, we need to create trainer for |
| updating the parameters. 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="s1">'adam'</span><span class="p">,</span> <span class="p">{</span><span class="s1">'learning_rate'</span><span class="p">:</span> <span class="n">lr</span><span class="p">})</span> |
| </pre></div> |
| </div> |
| <p>We can then write the training loop. During the training, we evaluate on |
| the validation and testing datasets every epoch, and record the |
| parameters that give the hightest BLEU score on the validation dataset. |
| Before performing forward and backward, we first use <code class="docutils literal notranslate"><span class="pre">to_device</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> tells Gluon backend to compute the |
| gradients for the part inside the block.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">best_valid_bleu</span> <span class="o">=</span> <span class="mf">0.0</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">epochs</span><span class="p">):</span> |
| <span class="n">log_avg_loss</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="n">log_avg_gnorm</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="n">log_wc</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="n">log_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="k">for</span> <span class="n">batch_id</span><span class="p">,</span> <span class="p">(</span><span class="n">src_seq</span><span class="p">,</span> <span class="n">tgt_seq</span><span class="p">,</span> <span class="n">src_valid_length</span><span class="p">,</span> <span class="n">tgt_valid_length</span><span class="p">)</span>\ |
| <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">train_data_loader</span><span class="p">):</span> |
| <span class="c1"># logging.info(src_seq.context) Context suddenly becomes GPU.</span> |
| <span class="n">src_seq</span> <span class="o">=</span> <span class="n">src_seq</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> |
| <span class="n">tgt_seq</span> <span class="o">=</span> <span class="n">tgt_seq</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> |
| <span class="n">src_valid_length</span> <span class="o">=</span> <span class="n">src_valid_length</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> |
| <span class="n">tgt_valid_length</span> <span class="o">=</span> <span class="n">tgt_valid_length</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> |
| <span class="k">with</span> <span class="n">mx</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">record</span><span class="p">():</span> |
| <span class="n">out</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">src_seq</span><span class="p">,</span> <span class="n">tgt_seq</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">src_valid_length</span><span class="p">,</span> <span class="n">tgt_valid_length</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> |
| <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_function</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">tgt_seq</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:],</span> <span class="n">tgt_valid_length</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> |
| <span class="n">loss</span> <span class="o">=</span> <span class="n">loss</span> <span class="o">*</span> <span class="p">(</span><span class="n">tgt_seq</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">tgt_valid_length</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> |
| <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span> |
| <span class="n">grads</span> <span class="o">=</span> <span class="p">[</span><span class="n">p</span><span class="o">.</span><span class="n">grad</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> <span class="k">for</span> <span class="n">p</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">values</span><span class="p">()]</span> |
| <span class="n">gnorm</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">clip_global_norm</span><span class="p">(</span><span class="n">grads</span><span class="p">,</span> <span class="n">clip</span><span class="p">)</span> |
| <span class="n">trainer</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> |
| <span class="n">src_wc</span> <span class="o">=</span> <span class="n">src_valid_length</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">asscalar</span><span class="p">()</span> |
| <span class="n">tgt_wc</span> <span class="o">=</span> <span class="p">(</span><span class="n">tgt_valid_length</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">asscalar</span><span class="p">()</span> |
| <span class="n">step_loss</span> <span class="o">=</span> <span class="n">loss</span><span class="o">.</span><span class="n">asscalar</span><span class="p">()</span> |
| <span class="n">log_avg_loss</span> <span class="o">+=</span> <span class="n">step_loss</span> |
| <span class="n">log_avg_gnorm</span> <span class="o">+=</span> <span class="n">gnorm</span> |
| <span class="n">log_wc</span> <span class="o">+=</span> <span class="n">src_wc</span> <span class="o">+</span> <span class="n">tgt_wc</span> |
| <span class="k">if</span> <span class="p">(</span><span class="n">batch_id</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="n">log_interval</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">wps</span> <span class="o">=</span> <span class="n">log_wc</span> <span class="o">/</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">log_start_time</span><span class="p">)</span> |
| <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">'[Epoch </span><span class="si">{}</span><span class="s1"> Batch </span><span class="si">{}</span><span class="s1">/</span><span class="si">{}</span><span class="s1">] loss=</span><span class="si">{:.4f}</span><span class="s1">, ppl=</span><span class="si">{:.4f}</span><span class="s1">, gnorm=</span><span class="si">{:.4f}</span><span class="s1">, '</span> |
| <span class="s1">'throughput=</span><span class="si">{:.2f}</span><span class="s1">K wps, wc=</span><span class="si">{:.2f}</span><span class="s1">K'</span> |
| <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch_id</span><span class="p">,</span> <span class="n">batch_id</span> <span class="o">+</span> <span class="mi">1</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="n">log_avg_loss</span> <span class="o">/</span> <span class="n">log_interval</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">log_avg_loss</span> <span class="o">/</span> <span class="n">log_interval</span><span class="p">),</span> |
| <span class="n">log_avg_gnorm</span> <span class="o">/</span> <span class="n">log_interval</span><span class="p">,</span> |
| <span class="n">wps</span> <span class="o">/</span> <span class="mi">1000</span><span class="p">,</span> <span class="n">log_wc</span> <span class="o">/</span> <span class="mi">1000</span><span class="p">))</span> |
| <span class="n">log_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">log_avg_loss</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="n">log_avg_gnorm</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="n">log_wc</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="n">valid_loss</span><span class="p">,</span> <span class="n">valid_translation_out</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">val_data_loader</span><span class="p">)</span> |
| <span class="n">valid_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">val_tgt_sentences</span><span class="p">],</span> <span class="n">valid_translation_out</span><span class="p">)</span> |
| <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">'[Epoch </span><span class="si">{}</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">, valid bleu=</span><span class="si">{:.2f}</span><span class="s1">'</span> |
| <span class="o">.</span><span class="n">format</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">valid_bleu_score</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span> |
| <span class="n">test_loss</span><span class="p">,</span> <span class="n">test_translation_out</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">test_data_loader</span><span class="p">)</span> |
| <span class="n">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">test_tgt_sentences</span><span class="p">],</span> <span class="n">test_translation_out</span><span class="p">)</span> |
| <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">'[Epoch </span><span class="si">{}</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">, test bleu=</span><span class="si">{:.2f}</span><span class="s1">'</span> |
| <span class="o">.</span><span class="n">format</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="n">test_bleu_score</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span> |
| <span class="n">write_sentences</span><span class="p">(</span><span class="n">valid_translation_out</span><span class="p">,</span> |
| <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">save_dir</span><span class="p">,</span> <span class="s1">'epoch</span><span class="si">{:d}</span><span class="s1">_valid_out.txt'</span><span class="p">)</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch_id</span><span class="p">))</span> |
| <span class="n">write_sentences</span><span class="p">(</span><span class="n">test_translation_out</span><span class="p">,</span> |
| <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">save_dir</span><span class="p">,</span> <span class="s1">'epoch</span><span class="si">{:d}</span><span class="s1">_test_out.txt'</span><span class="p">)</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch_id</span><span class="p">))</span> |
| <span class="k">if</span> <span class="n">valid_bleu_score</span> <span class="o">></span> <span class="n">best_valid_bleu</span><span class="p">:</span> |
| <span class="n">best_valid_bleu</span> <span class="o">=</span> <span class="n">valid_bleu_score</span> |
| <span class="n">save_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</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">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">'Save best parameters to </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">save_path</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="n">save_path</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">epoch_id</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">>=</span> <span class="p">(</span><span class="n">epochs</span> <span class="o">*</span> <span class="mi">2</span><span class="p">)</span> <span class="o">//</span> <span class="mi">3</span><span class="p">:</span> |
| <span class="n">new_lr</span> <span class="o">=</span> <span class="n">trainer</span><span class="o">.</span><span class="n">learning_rate</span> <span class="o">*</span> <span class="n">lr_update_factor</span> |
| <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">'Learning rate change to </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">new_lr</span><span class="p">))</span> |
| <span class="n">trainer</span><span class="o">.</span><span class="n">set_learning_rate</span><span class="p">(</span><span class="n">new_lr</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="summary"> |
| <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2> |
| <p>In this notebook, we have shown how to train a GNMT model on IWSLT 2015 |
| English-Vietnamese using Gluon NLP toolkit. The complete training script |
| can be found |
| <a class="reference external" href="https://github.com/dmlc/gluon-nlp/blob/v0.x/scripts/machine_translation/train_gnmt.py">here</a>. |
| The command to reproduce the result can be seen in the <a class="reference external" href="http://gluon-nlp.mxnet.io/scripts/index.html#machine-translation">nmt scripts |
| page</a>.</p> |
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| <li><a class="reference internal" href="#">Google Neural Machine Translation</a><ul> |
| <li><a class="reference internal" href="#load-mxnet-and-gluon">Load MXNET and Gluon</a></li> |
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| <li><a class="reference internal" href="#build-gnmt-model">Build GNMT Model</a></li> |
| <li><a class="reference internal" href="#training-epochs">Training Epochs</a></li> |
| <li><a class="reference internal" href="#summary">Summary</a></li> |
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