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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Train the neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Use GPUs</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/custom_layer_beginners.html">How to write a custom layer in Apache MxNet Gluon API</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data.html">Data</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/image-augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/normalization/normalization.html">Normalization Blocks</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/activations/activations.html">Activation Blocks</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/custom-loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/init.html">Initialization</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/init.html#Conclusion">Conclusion</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/init.html#Recommended-Next-Steps">Recommended Next Steps</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/trainer.html">Trainer</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/naming.html">Naming of Gluon Parameter and Blocks</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/customop.html">Creating custom operators with numpy</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/text/index.html#language-model">Language Model</a><ul class="simple"> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/ndarray/index.html">NDArray</a><ul class="simple"> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/symbol/index.html">Symbol</a><ul class="simple"> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/autograd.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/onnx/index.html">ONNX</a><ul class="simple"> |
| </ul> |
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| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/performance/index.html">Performance</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/compression/index.html">Compression</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/compression/int8.html">Deploy with int-8</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/inference/index.html">Inference</a><ul class="simple"> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/index.html">Run on AWS</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/use_ec2.html">Run on an EC2 Instance</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/use_sagemaker.html">Run on Amazon SageMaker</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/cloud.html">MXNet on the Cloud</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/extend/index.html">Customization</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/extend/custom_layer.html">How to write a custom layer in Apache MxNet Gluon API</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/extend/customop.html">Creating custom operators with numpy</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l1"><a class="reference internal" href="../../../api/index.html">Python API</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/ndarray/index.html">mxnet.ndarray</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/ndarray/mxnet.ndarray.NDArray.html">NDArray</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/ndarray/_autogen/mxnet.ndarray.NDArray.context.html">mxnet.ndarray.NDArray.context</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/ndarray/_autogen/mxnet.ndarray.NDArray.dtype.html">mxnet.ndarray.NDArray.dtype</a></li> |
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| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/rnn.html">rnn and contrib.rnn</a><ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.rnn.GRUCell.html">mxnet.gluon.rnn.GRUCell</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.contrib.rnn.Conv2DRNNCell.html">mxnet.gluon.contrib.rnn.Conv2DRNNCell</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.contrib.rnn.Conv2DLSTMCell.html">mxnet.gluon.contrib.rnn.Conv2DLSTMCell</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.contrib.rnn.Conv3DLSTMCell.html">mxnet.gluon.contrib.rnn.Conv3DLSTMCell</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.contrib.rnn.Conv3DGRUCell.html">mxnet.gluon.contrib.rnn.Conv3DGRUCell</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.contrib.rnn.VariationalDropoutCell.html">mxnet.gluon.contrib.rnn.VariationalDropoutCell</a></li> |
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| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/mxnet.gluon.loss.html"><code class="docutils literal notranslate"><span class="pre">loss</span></code></a><ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.loss.SoftmaxCrossEntropyLoss.html">mxnet.gluon.loss.SoftmaxCrossEntropyLoss</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.loss.HingeLoss.html">mxnet.gluon.loss.HingeLoss</a></li> |
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| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/mxnet.gluon.parameter.html">Parameter</a><ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/mxnet.gluon.utils.html">utils</a><ul> |
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| <li class="toctree-l2"><a class="reference internal" href="../../../api/gluon-related/index.html">Gluon related modules</a><ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../api/symbol/_autogen/mxnet.symbol.Symbol.one_hot.html">mxnet.symbol.Symbol.one_hot</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/symbol/_autogen/mxnet.symbol.Symbol.pick.html">mxnet.symbol.Symbol.pick</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/symbol/_autogen/mxnet.symbol.Symbol.__getitem__.html">mxnet.symbol.Symbol.__getitem__</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../api/symbol/_autogen/mxnet.symbol.Symbol.infer_type.html">mxnet.symbol.Symbol.infer_type</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/symbol/_autogen/mxnet.symbol.Symbol.infer_shape.html">mxnet.symbol.Symbol.infer_shape</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/symbol/_autogen/mxnet.symbol.Symbol.infer_shape_partial.html">mxnet.symbol.Symbol.infer_shape_partial</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/symbol/_autogen/mxnet.symbol.Symbol.bind.html">mxnet.symbol.Symbol.bind</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/symbol/_autogen/mxnet.symbol.Symbol.simple_bind.html">mxnet.symbol.Symbol.simple_bind</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/symbol/_autogen/mxnet.symbol.Symbol.save.html">mxnet.symbol.Symbol.save</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/symbol/_autogen/mxnet.symbol.Symbol.tojson.html">mxnet.symbol.Symbol.tojson</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/symbol/_autogen/mxnet.symbol.Symbol.debug_str.html">mxnet.symbol.Symbol.debug_str</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/symbol/_autogen/mxnet.symbol.Symbol.clip.html">mxnet.symbol.Symbol.clip</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../api/symbol/mxnet.symbol.linalg.html">mxnet.linalg</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/symbol/_autogen/mxnet.symbol.linalg.gemm.html">mxnet.symbol.linalg.gemm</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../api/symbol/_autogen/mxnet.symbol.linalg.potri.html">mxnet.symbol.linalg.potri</a></li> |
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| <li class="toctree-l2"><a class="reference internal" href="../../../api/symbol-related/index.html">Symbol related modules</a><ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../api/symbol-related/_autogen/mxnet.callback.module_checkpoint.html">mxnet.callback.module_checkpoint</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/symbol-related/_autogen/mxnet.callback.LogValidationMetricsCallback.html">mxnet.callback.LogValidationMetricsCallback</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/symbol-related/_autogen/mxnet.callback.ProgressBar.html">mxnet.callback.ProgressBar</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../api/symbol-related/_autogen/mxnet.module.BaseModule.html">mxnet.module.BaseModule</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/symbol-related/_autogen/mxnet.module.BucketingModule.html">mxnet.module.BucketingModule</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/symbol-related/_autogen/mxnet.module.Module.html">mxnet.module.Module</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/symbol-related/_autogen/mxnet.module.PythonLossModule.html">mxnet.module.PythonLossModule</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../api/symbol-related/_autogen/mxnet.module.SequentialModule.html">mxnet.module.SequentialModule</a></li> |
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| <li class="toctree-l2"><a class="reference internal" href="../../../api/advanced/index.html">Advanced modules</a><ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../api/advanced/mxnet.executor_manager.html">mxnet.executor_manager</a><ul> |
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| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../api/advanced/mxnet.rtc.html">mxnet.rtc</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/advanced/_autogen/mxnet.rtc.CudaKernel.html">mxnet.rtc.CudaKernel</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/advanced/_autogen/mxnet.rtc.CudaModule.html">mxnet.rtc.CudaModule</a></li> |
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| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/advanced/mxnet.test_utils.html">mxnet.test_utils</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/advanced/_autogen/mxnet.test_utils.almost_equal.html">mxnet.test_utils.almost_equal</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/advanced/_autogen/mxnet.test_utils.almost_equal_ignore_nan.html">mxnet.test_utils.almost_equal_ignore_nan</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/advanced/_autogen/mxnet.test_utils.assert_almost_equal.html">mxnet.test_utils.assert_almost_equal</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/advanced/_autogen/mxnet.test_utils.assert_almost_equal_ignore_nan.html">mxnet.test_utils.assert_almost_equal_ignore_nan</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/advanced/_autogen/mxnet.test_utils.assert_exception.html">mxnet.test_utils.assert_exception</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../api/advanced/_autogen/mxnet.test_utils.discard_stderr.html">mxnet.test_utils.discard_stderr</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../api/advanced/_autogen/mxnet.test_utils.random_arrays.html">mxnet.test_utils.random_arrays</a></li> |
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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <ul> |
| <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Train the neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Use GPUs</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/custom_layer_beginners.html">How to write a custom layer in Apache MxNet Gluon API</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../api/ndarray/_autogen/mxnet.ndarray.NDArray.log.html">mxnet.ndarray.NDArray.log</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../api/ndarray/_autogen/mxnet.ndarray.exp.html">mxnet.ndarray.exp</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../api/ndarray/_autogen/mxnet.ndarray.sparse.sgd_mom_update.html">mxnet.ndarray.sparse.sgd_mom_update</a></li> |
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| </li> |
| </ul> |
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| <li class="toctree-l2"><a class="reference internal" href="../../../api/gluon/index.html">mxnet.gluon</a><ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.Sequential.html">mxnet.gluon.nn.Sequential</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.HybridSequential.html">mxnet.gluon.nn.HybridSequential</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.contrib.nn.Concurrent.html">mxnet.gluon.contrib.nn.Concurrent</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.contrib.nn.HybridConcurrent.html">mxnet.gluon.contrib.nn.HybridConcurrent</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.Dense.html">mxnet.gluon.nn.Dense</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.Activation.html">mxnet.gluon.nn.Activation</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.Dropout.html">mxnet.gluon.nn.Dropout</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.Flatten.html">mxnet.gluon.nn.Flatten</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.Lambda.html">mxnet.gluon.nn.Lambda</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.HybridLambda.html">mxnet.gluon.nn.HybridLambda</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.Conv1D.html">mxnet.gluon.nn.Conv1D</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.Conv2D.html">mxnet.gluon.nn.Conv2D</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.Conv3D.html">mxnet.gluon.nn.Conv3D</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.Conv1DTranspose.html">mxnet.gluon.nn.Conv1DTranspose</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.Conv2DTranspose.html">mxnet.gluon.nn.Conv2DTranspose</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.Conv3DTranspose.html">mxnet.gluon.nn.Conv3DTranspose</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.MaxPool1D.html">mxnet.gluon.nn.MaxPool1D</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.MaxPool2D.html">mxnet.gluon.nn.MaxPool2D</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.MaxPool3D.html">mxnet.gluon.nn.MaxPool3D</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.AvgPool1D.html">mxnet.gluon.nn.AvgPool1D</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.AvgPool2D.html">mxnet.gluon.nn.AvgPool2D</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.AvgPool3D.html">mxnet.gluon.nn.AvgPool3D</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.GlobalMaxPool1D.html">mxnet.gluon.nn.GlobalMaxPool1D</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.GlobalMaxPool2D.html">mxnet.gluon.nn.GlobalMaxPool2D</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.GlobalMaxPool3D.html">mxnet.gluon.nn.GlobalMaxPool3D</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.GlobalAvgPool1D.html">mxnet.gluon.nn.GlobalAvgPool1D</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.GlobalAvgPool2D.html">mxnet.gluon.nn.GlobalAvgPool2D</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.GlobalAvgPool3D.html">mxnet.gluon.nn.GlobalAvgPool3D</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.ReflectionPad2D.html">mxnet.gluon.nn.ReflectionPad2D</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.BatchNorm.html">mxnet.gluon.nn.BatchNorm</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.InstanceNorm.html">mxnet.gluon.nn.InstanceNorm</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.LayerNorm.html">mxnet.gluon.nn.LayerNorm</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.contrib.nn.SyncBatchNorm.html">mxnet.gluon.contrib.nn.SyncBatchNorm</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.Embedding.html">mxnet.gluon.nn.Embedding</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.contrib.nn.SparseEmbedding.html">mxnet.gluon.contrib.nn.SparseEmbedding</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.LeakyReLU.html">mxnet.gluon.nn.LeakyReLU</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.PReLU.html">mxnet.gluon.nn.PReLU</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.ELU.html">mxnet.gluon.nn.ELU</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.SELU.html">mxnet.gluon.nn.SELU</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.nn.Swish.html">mxnet.gluon.nn.Swish</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/rnn.html">rnn and contrib.rnn</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.rnn.LSTMCell.html">mxnet.gluon.rnn.LSTMCell</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.rnn.GRUCell.html">mxnet.gluon.rnn.GRUCell</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.rnn.RecurrentCell.html">mxnet.gluon.rnn.RecurrentCell</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.rnn.SequentialRNNCell.html">mxnet.gluon.rnn.SequentialRNNCell</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.rnn.BidirectionalCell.html">mxnet.gluon.rnn.BidirectionalCell</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.rnn.DropoutCell.html">mxnet.gluon.rnn.DropoutCell</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.rnn.ZoneoutCell.html">mxnet.gluon.rnn.ZoneoutCell</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.rnn.ResidualCell.html">mxnet.gluon.rnn.ResidualCell</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.contrib.rnn.Conv1DRNNCell.html">mxnet.gluon.contrib.rnn.Conv1DRNNCell</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.contrib.rnn.Conv2DRNNCell.html">mxnet.gluon.contrib.rnn.Conv2DRNNCell</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.contrib.rnn.Conv3DRNNCell.html">mxnet.gluon.contrib.rnn.Conv3DRNNCell</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.contrib.rnn.Conv1DLSTMCell.html">mxnet.gluon.contrib.rnn.Conv1DLSTMCell</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.contrib.rnn.Conv2DLSTMCell.html">mxnet.gluon.contrib.rnn.Conv2DLSTMCell</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.contrib.rnn.Conv3DLSTMCell.html">mxnet.gluon.contrib.rnn.Conv3DLSTMCell</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.contrib.rnn.Conv1DGRUCell.html">mxnet.gluon.contrib.rnn.Conv1DGRUCell</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.contrib.rnn.Conv2DGRUCell.html">mxnet.gluon.contrib.rnn.Conv2DGRUCell</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.contrib.rnn.Conv3DGRUCell.html">mxnet.gluon.contrib.rnn.Conv3DGRUCell</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.contrib.rnn.VariationalDropoutCell.html">mxnet.gluon.contrib.rnn.VariationalDropoutCell</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.contrib.rnn.LSTMPCell.html">mxnet.gluon.contrib.rnn.LSTMPCell</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.rnn.RNN.html">mxnet.gluon.rnn.RNN</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.rnn.LSTM.html">mxnet.gluon.rnn.LSTM</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.rnn.GRU.html">mxnet.gluon.rnn.GRU</a></li> |
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| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/mxnet.gluon.loss.html"><code class="docutils literal notranslate"><span class="pre">loss</span></code></a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.loss.Loss.html">mxnet.gluon.loss.Loss</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.loss.L2Loss.html">mxnet.gluon.loss.L2Loss</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.loss.L1Loss.html">mxnet.gluon.loss.L1Loss</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss.html">mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.loss.SoftmaxCrossEntropyLoss.html">mxnet.gluon.loss.SoftmaxCrossEntropyLoss</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.loss.KLDivLoss.html">mxnet.gluon.loss.KLDivLoss</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.loss.HuberLoss.html">mxnet.gluon.loss.HuberLoss</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.loss.HingeLoss.html">mxnet.gluon.loss.HingeLoss</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.loss.SquaredHingeLoss.html">mxnet.gluon.loss.SquaredHingeLoss</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.loss.LogisticLoss.html">mxnet.gluon.loss.LogisticLoss</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.loss.TripletLoss.html">mxnet.gluon.loss.TripletLoss</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.loss.CTCLoss.html">mxnet.gluon.loss.CTCLoss</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/mxnet.gluon.parameter.html">Parameter</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.Parameter.initialize.html">mxnet.gluon.Parameter.initialize</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.Parameter.data.html">mxnet.gluon.Parameter.data</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.Parameter.list_data.html">mxnet.gluon.Parameter.list_data</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.Parameter.list_row_sparse_data.html">mxnet.gluon.Parameter.list_row_sparse_data</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.Parameter.row_sparse_data.html">mxnet.gluon.Parameter.row_sparse_data</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.Parameter.set_data.html">mxnet.gluon.Parameter.set_data</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.Parameter.shape.html">mxnet.gluon.Parameter.shape</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.Parameter.grad.html">mxnet.gluon.Parameter.grad</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.Parameter.list_grad.html">mxnet.gluon.Parameter.list_grad</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.Parameter.zero_grad.html">mxnet.gluon.Parameter.zero_grad</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.Parameter.grad_req.html">mxnet.gluon.Parameter.grad_req</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.Parameter.cast.html">mxnet.gluon.Parameter.cast</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.Parameter.list_ctx.html">mxnet.gluon.Parameter.list_ctx</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.Parameter.reset_ctx.html">mxnet.gluon.Parameter.reset_ctx</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.Parameter.var.html">mxnet.gluon.Parameter.var</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/mxnet.gluon.Trainer.html">Trainer</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.Trainer.step.html">mxnet.gluon.Trainer.step</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.Trainer.allreduce_grads.html">mxnet.gluon.Trainer.allreduce_grads</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.Trainer.update.html">mxnet.gluon.Trainer.update</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.Trainer.load_states.html">mxnet.gluon.Trainer.load_states</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.Trainer.save_states.html">mxnet.gluon.Trainer.save_states</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.Trainer.learning_rate.html">mxnet.gluon.Trainer.learning_rate</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.Trainer.set_learning_rate.html">mxnet.gluon.Trainer.set_learning_rate</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/mxnet.gluon.data.html">data</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.data.Dataset.html">mxnet.gluon.data.Dataset</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.data.ArrayDataset.html">mxnet.gluon.data.ArrayDataset</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.data.RecordFileDataset.html">mxnet.gluon.data.RecordFileDataset</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.data.Sampler.html">mxnet.gluon.data.Sampler</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.data.SequentialSampler.html">mxnet.gluon.data.SequentialSampler</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.data.RandomSampler.html">mxnet.gluon.data.RandomSampler</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.data.BatchSampler.html">mxnet.gluon.data.BatchSampler</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.data.DataLoader.html">mxnet.gluon.data.DataLoader</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/mxnet.gluon.data.vision.html">data.vision</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.data.vision.datasets.MNIST.html">mxnet.gluon.data.vision.datasets.MNIST</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.data.vision.datasets.FashionMNIST.html">mxnet.gluon.data.vision.datasets.FashionMNIST</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.data.vision.datasets.CIFAR10.html">mxnet.gluon.data.vision.datasets.CIFAR10</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.data.vision.datasets.CIFAR100.html">mxnet.gluon.data.vision.datasets.CIFAR100</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.data.vision.datasets.ImageRecordDataset.html">mxnet.gluon.data.vision.datasets.ImageRecordDataset</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.data.vision.datasets.ImageFolderDataset.html">mxnet.gluon.data.vision.datasets.ImageFolderDataset</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.data.vision.transforms.Compose.html">mxnet.gluon.data.vision.transforms.Compose</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.data.vision.transforms.Cast.html">mxnet.gluon.data.vision.transforms.Cast</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.data.vision.transforms.ToTensor.html">mxnet.gluon.data.vision.transforms.ToTensor</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.data.vision.transforms.Normalize.html">mxnet.gluon.data.vision.transforms.Normalize</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.data.vision.transforms.RandomResizedCrop.html">mxnet.gluon.data.vision.transforms.RandomResizedCrop</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/_autogen/mxnet.gluon.data.vision.transforms.CenterCrop.html">mxnet.gluon.data.vision.transforms.CenterCrop</a></li> |
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| </li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/mxnet.gluon.utils.html">utils</a><ul> |
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| </ul> |
| </li> |
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| |
| <h1>Source code for mxnet.optimizer.optimizer</h1><div class="highlight"><pre> |
| <span></span><span class="c1"># coding: utf-8</span> |
| <span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span> |
| <span class="c1"># or more contributor license agreements. See the NOTICE file</span> |
| <span class="c1"># distributed with this work for additional information</span> |
| <span class="c1"># regarding copyright ownership. The ASF licenses this file</span> |
| <span class="c1"># to you under the Apache License, Version 2.0 (the</span> |
| <span class="c1"># "License"); you may not use this file except in compliance</span> |
| <span class="c1"># with the License. You may obtain a copy of the License at</span> |
| <span class="c1">#</span> |
| <span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span> |
| <span class="c1">#</span> |
| <span class="c1"># Unless required by applicable law or agreed to in writing,</span> |
| <span class="c1"># software distributed under the License is distributed on an</span> |
| <span class="c1"># "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span> |
| <span class="c1"># KIND, either express or implied. See the License for the</span> |
| <span class="c1"># specific language governing permissions and limitations</span> |
| <span class="c1"># under the License.</span> |
| |
| <span class="c1"># pylint: disable=too-many-lines</span> |
| <span class="sd">"""Weight updating functions."""</span> |
| <span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">absolute_import</span> |
| <span class="kn">import</span> <span class="nn">logging</span> |
| <span class="kn">import</span> <span class="nn">math</span> |
| <span class="kn">import</span> <span class="nn">pickle</span> |
| <span class="kn">import</span> <span class="nn">warnings</span> |
| <span class="kn">import</span> <span class="nn">os</span> |
| <span class="kn">import</span> <span class="nn">numpy</span> |
| <span class="kn">from</span> <span class="nn">..base</span> <span class="k">import</span> <span class="n">py_str</span> |
| <span class="kn">from</span> <span class="nn">..ndarray</span> <span class="k">import</span> <span class="p">(</span><span class="n">NDArray</span><span class="p">,</span> <span class="n">zeros</span><span class="p">,</span> <span class="n">clip</span><span class="p">,</span> <span class="n">sqrt</span><span class="p">,</span> <span class="n">cast</span><span class="p">,</span> <span class="n">maximum</span><span class="p">,</span> <span class="nb">abs</span> <span class="k">as</span> <span class="n">NDabs</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">multiply</span><span class="p">)</span> |
| <span class="kn">from</span> <span class="nn">..ndarray</span> <span class="k">import</span> <span class="p">(</span><span class="n">sgd_update</span><span class="p">,</span> <span class="n">sgd_mom_update</span><span class="p">,</span> <span class="n">adam_update</span><span class="p">,</span> <span class="n">rmsprop_update</span><span class="p">,</span> <span class="n">rmspropalex_update</span><span class="p">,</span> |
| <span class="n">mp_sgd_update</span><span class="p">,</span> <span class="n">mp_sgd_mom_update</span><span class="p">,</span> <span class="n">square</span><span class="p">,</span> <span class="n">ftrl_update</span><span class="p">,</span> <span class="n">ftml_update</span><span class="p">,</span> |
| <span class="n">signsgd_update</span><span class="p">,</span> <span class="n">signum_update</span><span class="p">,</span> <span class="n">nag_mom_update</span><span class="p">,</span> <span class="n">mp_nag_mom_update</span><span class="p">,</span> |
| <span class="n">multi_sgd_update</span><span class="p">,</span> <span class="n">multi_sgd_mom_update</span><span class="p">,</span> <span class="n">multi_mp_sgd_update</span><span class="p">,</span> |
| <span class="n">multi_mp_sgd_mom_update</span><span class="p">)</span> |
| <span class="kn">from</span> <span class="nn">..ndarray</span> <span class="k">import</span> <span class="n">sparse</span> |
| <span class="kn">from</span> <span class="nn">..random</span> <span class="k">import</span> <span class="n">normal</span> |
| <span class="kn">from</span> <span class="nn">..util</span> <span class="k">import</span> <span class="n">is_np_array</span> |
| |
| <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span> |
| <span class="s1">'AdaDelta'</span><span class="p">,</span> <span class="s1">'AdaGrad'</span><span class="p">,</span> <span class="s1">'Adam'</span><span class="p">,</span> <span class="s1">'Adamax'</span><span class="p">,</span> <span class="s1">'DCASGD'</span><span class="p">,</span> <span class="s1">'FTML'</span><span class="p">,</span> <span class="s1">'Ftrl'</span><span class="p">,</span> <span class="s1">'LBSGD'</span><span class="p">,</span> |
| <span class="s1">'NAG'</span><span class="p">,</span> <span class="s1">'NDabs'</span><span class="p">,</span> <span class="s1">'Nadam'</span><span class="p">,</span> <span class="s1">'Optimizer'</span><span class="p">,</span> <span class="s1">'RMSProp'</span><span class="p">,</span> <span class="s1">'SGD'</span><span class="p">,</span> <span class="s1">'SGLD'</span><span class="p">,</span> <span class="s1">'Signum'</span><span class="p">,</span> |
| <span class="s1">'Test'</span><span class="p">,</span> <span class="s1">'Updater'</span><span class="p">,</span> <span class="s1">'ccSGD'</span><span class="p">,</span> <span class="s1">'create'</span><span class="p">,</span> <span class="s1">'get_updater'</span><span class="p">,</span> <span class="s1">'register'</span> |
| <span class="p">]</span> |
| |
| <span class="k">def</span> <span class="nf">_flatten_list</span><span class="p">(</span><span class="n">nested_list</span><span class="p">):</span> |
| <span class="k">return</span> <span class="p">[</span><span class="n">item</span> <span class="k">for</span> <span class="n">sublist</span> <span class="ow">in</span> <span class="n">nested_list</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">sublist</span><span class="p">]</span> |
| |
| <div class="viewcode-block" id="Optimizer"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.Optimizer.html#mxnet.optimizer.Optimizer">[docs]</a><span class="k">class</span> <span class="nc">Optimizer</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span> |
| <span class="sd">"""The base class inherited by all optimizers.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> rescale_grad : float, optional, default 1.0</span> |
| <span class="sd"> Multiply the gradient with `rescale_grad` before updating. Often</span> |
| <span class="sd"> choose to be ``1.0/batch_size``.</span> |
| |
| <span class="sd"> param_idx2name : dict from int to string, optional, default None</span> |
| <span class="sd"> A dictionary that maps int index to string name.</span> |
| |
| <span class="sd"> clip_gradient : float, optional, default None</span> |
| <span class="sd"> Clip the gradient by projecting onto the box ``[-clip_gradient, clip_gradient]``.</span> |
| |
| <span class="sd"> learning_rate : float, optional, default 0.01</span> |
| <span class="sd"> The initial learning rate.</span> |
| |
| <span class="sd"> lr_scheduler : LRScheduler, optional, default None</span> |
| <span class="sd"> The learning rate scheduler.</span> |
| |
| <span class="sd"> wd : float, optional, default 0.0</span> |
| <span class="sd"> The weight decay (or L2 regularization) coefficient. Modifies objective</span> |
| <span class="sd"> by adding a penalty for having large weights.</span> |
| |
| <span class="sd"> sym: Symbol, optional, default None</span> |
| <span class="sd"> The Symbol this optimizer is applying to.</span> |
| |
| <span class="sd"> begin_num_update : int, optional, default 0</span> |
| <span class="sd"> The initial number of updates.</span> |
| |
| <span class="sd"> multi_precision : bool, optional, default False</span> |
| <span class="sd"> Flag to control the internal precision of the optimizer.</span> |
| <span class="sd"> False: results in using the same precision as the weights (default),</span> |
| <span class="sd"> True: makes internal 32-bit copy of the weights and applies gradients</span> |
| <span class="sd"> in 32-bit precision even if actual weights used in the model have lower precision.</span> |
| <span class="sd"> Turning this on can improve convergence and accuracy when training with float16.</span> |
| |
| <span class="sd"> param_dict : dict of int -> gluon.Parameter, default None</span> |
| <span class="sd"> Dictionary of parameter index to gluon.Parameter, used to lookup parameter attributes</span> |
| <span class="sd"> such as lr_mult, wd_mult, etc. param_dict shall not be deep copied.</span> |
| |
| <span class="sd"> Properties</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> learning_rate : float</span> |
| <span class="sd"> The current learning rate of the optimizer. Given an Optimizer object</span> |
| <span class="sd"> optimizer, its learning rate can be accessed as optimizer.learning_rate.</span> |
| <span class="sd"> """</span> |
| <div class="viewcode-block" id="Optimizer.__init__"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.Optimizer.html#mxnet.optimizer.Optimizer.__init__">[docs]</a> <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">rescale_grad</span><span class="o">=</span><span class="mf">1.</span><span class="p">,</span> <span class="n">param_idx2name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> |
| <span class="n">clip_gradient</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> |
| <span class="n">lr_scheduler</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sym</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">begin_num_update</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> |
| <span class="n">multi_precision</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">param_dict</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">rescale_grad</span> <span class="o">=</span> <span class="n">rescale_grad</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">=</span> <span class="n">learning_rate</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span> <span class="o">=</span> <span class="n">lr_scheduler</span> |
| <span class="k">if</span> <span class="n">lr_scheduler</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="o">.</span><span class="n">base_lr</span> <span class="o">=</span> <span class="n">learning_rate</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">wd</span> <span class="o">=</span> <span class="n">wd</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">lr_mult</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">wd_mult</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">begin_num_update</span> <span class="o">=</span> <span class="n">begin_num_update</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_update</span> <span class="o">=</span> <span class="n">begin_num_update</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_all_index_update_counts</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span> <span class="p">:</span> <span class="p">{}}</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_index_update_count</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_all_index_update_counts</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">clip_gradient</span> <span class="o">=</span> <span class="n">clip_gradient</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">multi_precision</span> <span class="o">=</span> <span class="n">multi_precision</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">aggregate_num</span> <span class="o">=</span> <span class="mi">0</span> |
| |
| <span class="k">if</span> <span class="n">param_idx2name</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">param_idx2name</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">param_idx2name</span><span class="p">,</span> <span class="nb">dict</span><span class="p">),</span> \ |
| <span class="s1">'param_idx2name should be a dict of param indexes to names.'</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">idx2name</span> <span class="o">=</span> <span class="n">param_idx2name</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">sym_info</span> <span class="o">=</span> <span class="p">(</span><span class="n">sym</span><span class="o">.</span><span class="n">attr_dict</span><span class="p">(),</span> <span class="n">sym</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">())</span> <span class="k">if</span> <span class="n">sym</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">param_dict</span> <span class="o">=</span> <span class="n">param_dict</span> <span class="k">if</span> <span class="n">param_dict</span> <span class="k">else</span> <span class="p">{}</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">allow_np_array</span> <span class="o">=</span> <span class="n">is_np_array</span><span class="p">()</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">set_lr_mult</span><span class="p">({})</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">set_wd_mult</span><span class="p">({})</span></div> |
| |
| <span class="n">opt_registry</span> <span class="o">=</span> <span class="p">{}</span> |
| |
| <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">register</span><span class="p">(</span><span class="n">klass</span><span class="p">):</span> |
| <span class="sd">"""Registers a new optimizer.</span> |
| |
| <span class="sd"> Once an optimizer is registered, we can create an instance of this</span> |
| <span class="sd"> optimizer with `create_optimizer` later.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| |
| <span class="sd"> >>> @mx.optimizer.Optimizer.register</span> |
| <span class="sd"> ... class MyOptimizer(mx.optimizer.Optimizer):</span> |
| <span class="sd"> ... pass</span> |
| <span class="sd"> >>> optim = mx.optimizer.Optimizer.create_optimizer('MyOptimizer')</span> |
| <span class="sd"> >>> print(type(optim))</span> |
| <span class="sd"> <class '__main__.MyOptimizer'></span> |
| <span class="sd"> """</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">klass</span><span class="p">,</span> <span class="nb">type</span><span class="p">))</span> |
| <span class="n">name</span> <span class="o">=</span> <span class="n">klass</span><span class="o">.</span><span class="vm">__name__</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">Optimizer</span><span class="o">.</span><span class="n">opt_registry</span><span class="p">:</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">'WARNING: New optimizer </span><span class="si">%s</span><span class="s1">.</span><span class="si">%s</span><span class="s1"> is overriding '</span> |
| <span class="s1">'existing optimizer </span><span class="si">%s</span><span class="s1">.</span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> |
| <span class="p">(</span><span class="n">klass</span><span class="o">.</span><span class="vm">__module__</span><span class="p">,</span> <span class="n">klass</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> |
| <span class="n">Optimizer</span><span class="o">.</span><span class="n">opt_registry</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="vm">__module__</span><span class="p">,</span> |
| <span class="n">Optimizer</span><span class="o">.</span><span class="n">opt_registry</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="vm">__name__</span><span class="p">))</span> |
| <span class="n">Optimizer</span><span class="o">.</span><span class="n">opt_registry</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">klass</span> |
| <span class="k">return</span> <span class="n">klass</span> |
| |
| <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">create_optimizer</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="sd">"""Instantiates an optimizer with a given name and kwargs.</span> |
| |
| <span class="sd"> .. note:: We can use the alias `create` for ``Optimizer.create_optimizer``.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> name: str</span> |
| <span class="sd"> Name of the optimizer. Should be the name</span> |
| <span class="sd"> of a subclass of Optimizer. Case insensitive.</span> |
| |
| <span class="sd"> kwargs: dict</span> |
| <span class="sd"> Parameters for the optimizer.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> Optimizer</span> |
| <span class="sd"> An instantiated optimizer.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> sgd = mx.optimizer.Optimizer.create_optimizer('sgd')</span> |
| <span class="sd"> >>> type(sgd)</span> |
| <span class="sd"> <class 'mxnet.optimizer.SGD'></span> |
| <span class="sd"> >>> adam = mx.optimizer.create('adam', learning_rate=.1)</span> |
| <span class="sd"> >>> type(adam)</span> |
| <span class="sd"> <class 'mxnet.optimizer.Adam'></span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">name</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="ow">in</span> <span class="n">Optimizer</span><span class="o">.</span><span class="n">opt_registry</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">Optimizer</span><span class="o">.</span><span class="n">opt_registry</span><span class="p">[</span><span class="n">name</span><span class="o">.</span><span class="n">lower</span><span class="p">()](</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Cannot find optimizer </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="n">name</span><span class="p">)</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">learning_rate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_update</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> |
| |
| <span class="k">def</span> <span class="nf">create_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> |
| <span class="sd">"""Creates auxiliary state for a given weight.</span> |
| |
| <span class="sd"> Some optimizers require additional states, e.g. as momentum, in addition</span> |
| <span class="sd"> to gradients in order to update weights. This function creates state</span> |
| <span class="sd"> for a given weight which will be used in `update`. This function is</span> |
| <span class="sd"> called only once for each weight.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> index : int</span> |
| <span class="sd"> An unique index to identify the weight.</span> |
| <span class="sd"> weight : NDArray</span> |
| <span class="sd"> The weight.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> state : any obj</span> |
| <span class="sd"> The state associated with the weight.</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">def</span> <span class="nf">create_state_multi_precision</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> |
| <span class="sd">"""Creates auxiliary state for a given weight, including FP32 high</span> |
| <span class="sd"> precision copy if original weight is FP16.</span> |
| |
| <span class="sd"> This method is provided to perform automatic mixed precision training</span> |
| <span class="sd"> for optimizers that do not support it themselves.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> index : int</span> |
| <span class="sd"> An unique index to identify the weight.</span> |
| <span class="sd"> weight : NDArray</span> |
| <span class="sd"> The weight.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> state : any obj</span> |
| <span class="sd"> The state associated with the weight.</span> |
| <span class="sd"> """</span> |
| <span class="n">weight_master_copy</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">multi_precision</span> <span class="ow">and</span> <span class="n">weight</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float16</span><span class="p">:</span> |
| <span class="n">weight_master_copy</span> <span class="o">=</span> <span class="n">weight</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">weight_master_copy</span><span class="p">,)</span> <span class="o">+</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">create_state</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">weight_master_copy</span><span class="p">),)</span> |
| <span class="k">if</span> <span class="n">weight</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float16</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">multi_precision</span><span class="p">:</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">"Accumulating with float16 in optimizer can lead to "</span> |
| <span class="s2">"poor accuracy or slow convergence. "</span> |
| <span class="s2">"Consider using multi_precision=True option of the "</span> |
| <span class="s2">"optimizer"</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_state</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span> |
| <span class="sd">"""Updates the given parameter using the corresponding gradient and state.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> index : int</span> |
| <span class="sd"> The unique index of the parameter into the individual learning</span> |
| <span class="sd"> rates and weight decays. Learning rates and weight decay</span> |
| <span class="sd"> may be set via `set_lr_mult()` and `set_wd_mult()`, respectively.</span> |
| <span class="sd"> weight : NDArray</span> |
| <span class="sd"> The parameter to be updated.</span> |
| <span class="sd"> grad : NDArray</span> |
| <span class="sd"> The gradient of the objective with respect to this parameter.</span> |
| <span class="sd"> state : any obj</span> |
| <span class="sd"> The state returned by `create_state()`.</span> |
| <span class="sd"> """</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="nf">update_multi_precision</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span> |
| <span class="sd">"""Updates the given parameter using the corresponding gradient and state.</span> |
| <span class="sd"> Mixed precision version.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> index : int</span> |
| <span class="sd"> The unique index of the parameter into the individual learning</span> |
| <span class="sd"> rates and weight decays. Learning rates and weight decay</span> |
| <span class="sd"> may be set via `set_lr_mult()` and `set_wd_mult()`, respectively.</span> |
| <span class="sd"> weight : NDArray</span> |
| <span class="sd"> The parameter to be updated.</span> |
| <span class="sd"> grad : NDArray</span> |
| <span class="sd"> The gradient of the objective with respect to this parameter.</span> |
| <span class="sd"> state : any obj</span> |
| <span class="sd"> The state returned by `create_state()`.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">multi_precision</span> <span class="ow">and</span> <span class="n">weight</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float16</span><span class="p">:</span> |
| <span class="c1"># Wrapper for mixed precision</span> |
| <span class="n">weight_master_copy</span> <span class="o">=</span> <span class="n">state</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">original_state</span> <span class="o">=</span> <span class="n">state</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="n">grad32</span> <span class="o">=</span> <span class="n">grad</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">weight_master_copy</span><span class="p">,</span> <span class="n">grad32</span><span class="p">,</span> <span class="n">original_state</span><span class="p">)</span> |
| <span class="n">cast</span><span class="p">(</span><span class="n">weight_master_copy</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">weight</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">set_learning_rate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lr</span><span class="p">):</span> |
| <span class="sd">"""Sets a new learning rate of the optimizer.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> lr : float</span> |
| <span class="sd"> The new learning rate of the optimizer.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> <span class="c1"># pylint: disable=no-else-raise</span> |
| <span class="k">raise</span> <span class="ne">UserWarning</span><span class="p">(</span><span class="s2">"LRScheduler of the optimizer has already been "</span> |
| <span class="s2">"defined. Note that set_learning_rate can mutate "</span> |
| <span class="s2">"the value of the learning rate of the optimizer "</span> |
| <span class="s2">"only when the LRScheduler of the optimizer is "</span> |
| <span class="s2">"undefined."</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">=</span> <span class="n">lr</span> |
| |
| <span class="k">def</span> <span class="nf">set_lr_scale</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args_lrscale</span><span class="p">):</span> <span class="c1"># pylint: disable=unused-argument</span> |
| <span class="sd">"""[DEPRECATED] Sets lr scale. Use set_lr_mult instead."""</span> |
| <span class="k">raise</span> <span class="ne">DeprecationWarning</span> |
| |
| <span class="k">def</span> <span class="nf">set_lr_mult</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args_lr_mult</span><span class="p">):</span> |
| <span class="sd">"""Sets an individual learning rate multiplier for each parameter.</span> |
| |
| <span class="sd"> If you specify a learning rate multiplier for a parameter, then</span> |
| <span class="sd"> the learning rate for the parameter will be set as the product of</span> |
| <span class="sd"> the global learning rate `self.lr` and its multiplier.</span> |
| |
| <span class="sd"> .. note:: The default learning rate multiplier of a `Variable`</span> |
| <span class="sd"> can be set with `lr_mult` argument in the constructor.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> args_lr_mult : dict of str/int to float</span> |
| <span class="sd"> For each of its key-value entries, the learning rate multipler for the</span> |
| <span class="sd"> parameter specified in the key will be set as the given value.</span> |
| |
| <span class="sd"> You can specify the parameter with either its name or its index.</span> |
| <span class="sd"> If you use the name, you should pass `sym` in the constructor,</span> |
| <span class="sd"> and the name you specified in the key of `args_lr_mult` should match</span> |
| <span class="sd"> the name of the parameter in `sym`. If you use the index, it should</span> |
| <span class="sd"> correspond to the index of the parameter used in the `update` method.</span> |
| |
| <span class="sd"> Specifying a parameter by its index is only supported for backward</span> |
| <span class="sd"> compatibility, and we recommend to use the name instead.</span> |
| <span class="sd"> """</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">lr_mult</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sym_info</span><span class="p">:</span> |
| <span class="n">attr</span><span class="p">,</span> <span class="n">arg_names</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sym_info</span> |
| <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">arg_names</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">attr</span> <span class="ow">and</span> <span class="s1">'__lr_mult__'</span> <span class="ow">in</span> <span class="n">attr</span><span class="p">[</span><span class="n">name</span><span class="p">]:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">lr_mult</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">attr</span><span class="p">[</span><span class="n">name</span><span class="p">][</span><span class="s1">'__lr_mult__'</span><span class="p">])</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">lr_mult</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">args_lr_mult</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">set_wd_mult</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args_wd_mult</span><span class="p">):</span> |
| <span class="sd">"""Sets an individual weight decay multiplier for each parameter.</span> |
| |
| <span class="sd"> By default, if `param_idx2name` was provided in the</span> |
| <span class="sd"> constructor, the weight decay multipler is set as 0 for all</span> |
| <span class="sd"> parameters whose name don't end with ``_weight`` or</span> |
| <span class="sd"> ``_gamma``.</span> |
| |
| <span class="sd"> .. note:: The default weight decay multiplier for a `Variable`</span> |
| <span class="sd"> can be set with its `wd_mult` argument in the constructor.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> args_wd_mult : dict of string/int to float</span> |
| <span class="sd"> For each of its key-value entries, the weight decay multipler for the</span> |
| <span class="sd"> parameter specified in the key will be set as the given value.</span> |
| |
| <span class="sd"> You can specify the parameter with either its name or its index.</span> |
| <span class="sd"> If you use the name, you should pass `sym` in the constructor,</span> |
| <span class="sd"> and the name you specified in the key of `args_lr_mult` should match</span> |
| <span class="sd"> the name of the parameter in `sym`. If you use the index, it should</span> |
| <span class="sd"> correspond to the index of the parameter used in the `update` method.</span> |
| |
| <span class="sd"> Specifying a parameter by its index is only supported for backward</span> |
| <span class="sd"> compatibility, and we recommend to use the name instead.</span> |
| <span class="sd"> """</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">wd_mult</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">idx2name</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">n</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">'_weight'</span><span class="p">)</span> <span class="ow">or</span> <span class="n">n</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">'_gamma'</span><span class="p">)):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">wd_mult</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sym_info</span><span class="p">:</span> |
| <span class="n">attr</span><span class="p">,</span> <span class="n">arg_names</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sym_info</span> |
| <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">arg_names</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">attr</span> <span class="ow">and</span> <span class="s1">'__wd_mult__'</span> <span class="ow">in</span> <span class="n">attr</span><span class="p">[</span><span class="n">name</span><span class="p">]:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">wd_mult</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">attr</span><span class="p">[</span><span class="n">name</span><span class="p">][</span><span class="s1">'__wd_mult__'</span><span class="p">])</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">wd_mult</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">args_wd_mult</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_set_current_context</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">device_id</span><span class="p">):</span> |
| <span class="sd">"""Sets the number of the currently handled device.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> device_id : int</span> |
| <span class="sd"> The number of current device.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">device_id</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_all_index_update_counts</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_all_index_update_counts</span><span class="p">[</span><span class="n">device_id</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_index_update_count</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_all_index_update_counts</span><span class="p">[</span><span class="n">device_id</span><span class="p">]</span> |
| |
| <span class="k">def</span> <span class="nf">_update_count</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span> |
| <span class="sd">"""Updates num_update.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> index : int or list of int</span> |
| <span class="sd"> The index to be updated.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)):</span> |
| <span class="n">index</span> <span class="o">=</span> <span class="p">[</span><span class="n">index</span><span class="p">]</span> |
| <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">index</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">idx</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_index_update_count</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_index_update_count</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">begin_num_update</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_index_update_count</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_update</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_index_update_count</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_update</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_get_lrs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indices</span><span class="p">):</span> |
| <span class="sd">"""Gets the learning rates given the indices of the weights.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> indices : list of int</span> |
| <span class="sd"> Indices corresponding to weights.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> lrs : list of float</span> |
| <span class="sd"> Learning rates for those indices.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">lr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_update</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">lr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> |
| |
| <span class="n">lrs</span> <span class="o">=</span> <span class="p">[</span><span class="n">lr</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">indices</span><span class="p">]</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">indices</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">index</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_dict</span><span class="p">:</span> |
| <span class="n">lrs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_dict</span><span class="p">[</span><span class="n">index</span><span class="p">]</span><span class="o">.</span><span class="n">lr_mult</span> |
| <span class="k">elif</span> <span class="n">index</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_mult</span><span class="p">:</span> |
| <span class="n">lrs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_mult</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> |
| <span class="k">elif</span> <span class="n">index</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">idx2name</span><span class="p">:</span> |
| <span class="n">lrs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_mult</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">idx2name</span><span class="p">[</span><span class="n">index</span><span class="p">],</span> <span class="mf">1.0</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">lrs</span> |
| |
| <span class="k">def</span> <span class="nf">_get_lr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span> |
| <span class="sd">"""Gets the learning rate given the index of the weight.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> index : int</span> |
| <span class="sd"> The index corresponding to the weight.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> lr : float</span> |
| <span class="sd"> Learning rate for this index.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_lrs</span><span class="p">([</span><span class="n">index</span><span class="p">])[</span><span class="mi">0</span><span class="p">]</span> |
| |
| <span class="k">def</span> <span class="nf">_get_wds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indices</span><span class="p">):</span> |
| <span class="sd">"""Gets weight decays for indices.</span> |
| <span class="sd"> Returns 0 for non-weights if the name of weights are provided for `__init__`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> indices : list of int</span> |
| <span class="sd"> Indices of weights.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> wds : list of float</span> |
| <span class="sd"> Weight decays for those indices.</span> |
| <span class="sd"> """</span> |
| <span class="n">wds</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">wd</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">indices</span><span class="p">]</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">indices</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">index</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_dict</span><span class="p">:</span> |
| <span class="n">wds</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_dict</span><span class="p">[</span><span class="n">index</span><span class="p">]</span><span class="o">.</span><span class="n">wd_mult</span> |
| <span class="k">elif</span> <span class="n">index</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">wd_mult</span><span class="p">:</span> |
| <span class="n">wds</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">wd_mult</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> |
| <span class="k">elif</span> <span class="n">index</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">idx2name</span><span class="p">:</span> |
| <span class="n">wds</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">wd_mult</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">idx2name</span><span class="p">[</span><span class="n">index</span><span class="p">],</span> <span class="mf">1.0</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">wds</span> |
| |
| <span class="k">def</span> <span class="nf">_get_wd</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span> |
| <span class="sd">"""Gets weight decay for index.</span> |
| <span class="sd"> Returns 0 for non-weights if the name of weights are provided for `__init__`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> index : int</span> |
| <span class="sd"> The index of weight.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> wd : float</span> |
| <span class="sd"> Weight decay for this index.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_wds</span><span class="p">([</span><span class="n">index</span><span class="p">])[</span><span class="mi">0</span><span class="p">]</span> |
| |
| <span class="k">def</span> <span class="nf">__getstate__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="c1"># do not include param_dict in the state</span> |
| <span class="k">del</span> <span class="n">ret</span><span class="p">[</span><span class="s1">'param_dict'</span><span class="p">]</span> |
| <span class="k">return</span> <span class="n">ret</span> |
| |
| <span class="k">def</span> <span class="nf">__setstate__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span> <span class="o">=</span> <span class="n">state</span> |
| <span class="c1"># param_dict needs to be explicitly set by the trainer</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">param_dict</span> <span class="o">=</span> <span class="p">{}</span></div> |
| |
| <span class="c1"># convenience wrapper for Optimizer.Register</span> |
| <span class="n">register</span> <span class="o">=</span> <span class="n">Optimizer</span><span class="o">.</span><span class="n">register</span> <span class="c1"># pylint: disable=invalid-name</span> |
| |
| <span class="c1"># pylint: disable=line-too-long</span> |
| <div class="viewcode-block" id="SGD"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.SGD.html#mxnet.optimizer.SGD">[docs]</a><span class="nd">@register</span> |
| <span class="k">class</span> <span class="nc">SGD</span><span class="p">(</span><span class="n">Optimizer</span><span class="p">):</span> |
| <span class="sd">"""The SGD optimizer with momentum and weight decay.</span> |
| |
| <span class="sd"> If the storage types of grad is ``row_sparse`` and ``lazy_update`` is True, \</span> |
| <span class="sd"> **lazy updates** are applied by::</span> |
| |
| <span class="sd"> for row in grad.indices:</span> |
| <span class="sd"> rescaled_grad[row] = lr * (rescale_grad * clip(grad[row], clip_gradient) + wd * weight[row])</span> |
| <span class="sd"> state[row] = momentum[row] * state[row] + rescaled_grad[row]</span> |
| <span class="sd"> weight[row] = weight[row] - state[row]</span> |
| |
| <span class="sd"> The sparse update only updates the momentum for the weights whose row_sparse</span> |
| <span class="sd"> gradient indices appear in the current batch, rather than updating it for all</span> |
| <span class="sd"> indices. Compared with the original update, it can provide large</span> |
| <span class="sd"> improvements in model training throughput for some applications. However, it</span> |
| <span class="sd"> provides slightly different semantics than the original update, and</span> |
| <span class="sd"> may lead to different empirical results.</span> |
| |
| <span class="sd"> In the case when ``update_on_kvstore`` is set to False (either globally via</span> |
| <span class="sd"> MXNET_UPDATE_ON_KVSTORE=0 environment variable or as a parameter in</span> |
| <span class="sd"> :class:`~mxnet.gluon.Trainer`) SGD optimizer can perform aggregated update</span> |
| <span class="sd"> of parameters, which may lead to improved performance. The aggregation size</span> |
| <span class="sd"> is controlled by MXNET_OPTIMIZER_AGGREGATION_SIZE environment variable and</span> |
| <span class="sd"> defaults to 4.</span> |
| |
| <span class="sd"> Otherwise, **standard updates** are applied by::</span> |
| |
| <span class="sd"> rescaled_grad = lr * (rescale_grad * clip(grad, clip_gradient) + wd * weight)</span> |
| <span class="sd"> state = momentum * state + rescaled_grad</span> |
| <span class="sd"> weight = weight - state</span> |
| |
| <span class="sd"> For details of the update algorithm see</span> |
| <span class="sd"> :class:`~mxnet.ndarray.sgd_update` and :class:`~mxnet.ndarray.sgd_mom_update`.</span> |
| |
| <span class="sd"> This optimizer accepts the following parameters in addition to those accepted</span> |
| <span class="sd"> by :class:`.Optimizer`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> momentum : float, optional</span> |
| <span class="sd"> The momentum value.</span> |
| <span class="sd"> lazy_update : bool, optional</span> |
| <span class="sd"> Default is True. If True, lazy updates are applied \</span> |
| <span class="sd"> if the storage types of weight and grad are both ``row_sparse``.</span> |
| <span class="sd"> multi_precision: bool, optional</span> |
| <span class="sd"> Flag to control the internal precision of the optimizer.</span> |
| <span class="sd"> False: results in using the same precision as the weights (default),</span> |
| <span class="sd"> True: makes internal 32-bit copy of the weights and applies gradients</span> |
| <span class="sd"> in 32-bit precision even if actual weights used in the model have lower precision.</span> |
| <span class="sd"> Turning this on can improve convergence and accuracy when training with float16.</span> |
| <span class="sd"> """</span> |
| <div class="viewcode-block" id="SGD.__init__"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.SGD.html#mxnet.optimizer.SGD.__init__">[docs]</a> <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">lazy_update</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">SGD</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> <span class="o">=</span> <span class="n">momentum</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">lazy_update</span> <span class="o">=</span> <span class="n">lazy_update</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">aggregate_num</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s1">'MXNET_OPTIMIZER_AGGREGATION_SIZE'</span><span class="p">,</span> <span class="s2">"4"</span><span class="p">))</span></div> |
| |
| <span class="k">def</span> <span class="nf">create_state_multi_precision</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> |
| <span class="n">weight_master_copy</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">multi_precision</span> <span class="ow">and</span> <span class="n">weight</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float16</span><span class="p">:</span> |
| <span class="n">weight_master_copy</span> <span class="o">=</span> <span class="n">weight</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> |
| <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">create_state</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">weight_master_copy</span><span class="p">),</span> <span class="n">weight_master_copy</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">weight</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float16</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">multi_precision</span><span class="p">:</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">"Accumulating with float16 in optimizer can lead to "</span> |
| <span class="s2">"poor accuracy or slow convergence. "</span> |
| <span class="s2">"Consider using multi_precision=True option of the "</span> |
| <span class="s2">"SGD optimizer"</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_state</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">create_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> |
| <span class="n">momentum</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> <span class="o">!=</span> <span class="mf">0.0</span><span class="p">:</span> |
| <span class="n">stype</span> <span class="o">=</span> <span class="n">weight</span><span class="o">.</span><span class="n">stype</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lazy_update</span> <span class="k">else</span> <span class="s1">'default'</span> |
| <span class="n">momentum</span> <span class="o">=</span> <span class="n">zeros</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">stype</span><span class="o">=</span><span class="n">stype</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">momentum</span> |
| |
| <span class="k">def</span> <span class="nf">_update_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">states</span><span class="p">,</span> <span class="n">multi_precision</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="n">aggregate</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span> |
| <span class="n">indices</span> <span class="o">=</span> <span class="p">[</span><span class="n">indices</span><span class="p">]</span> |
| <span class="n">weights</span> <span class="o">=</span> <span class="p">[</span><span class="n">weights</span><span class="p">]</span> |
| <span class="n">grads</span> <span class="o">=</span> <span class="p">[</span><span class="n">grads</span><span class="p">]</span> |
| <span class="n">states</span> <span class="o">=</span> <span class="p">[</span><span class="n">states</span><span class="p">]</span> |
| <span class="k">for</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">):</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="n">aggregate</span> <span class="o">=</span> <span class="p">(</span><span class="n">aggregate</span> <span class="ow">and</span> |
| <span class="n">weight</span><span class="o">.</span><span class="n">stype</span> <span class="o">==</span> <span class="s1">'default'</span> <span class="ow">and</span> |
| <span class="n">grad</span><span class="o">.</span><span class="n">stype</span> <span class="o">==</span> <span class="s1">'default'</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_update_count</span><span class="p">(</span><span class="n">indices</span><span class="p">)</span> |
| <span class="n">lrs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_lrs</span><span class="p">(</span><span class="n">indices</span><span class="p">)</span> |
| <span class="n">wds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_wds</span><span class="p">(</span><span class="n">indices</span><span class="p">)</span> |
| |
| <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'rescale_grad'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">rescale_grad</span><span class="p">}</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">kwargs</span><span class="p">[</span><span class="s1">'momentum'</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span><span class="p">:</span> |
| <span class="n">kwargs</span><span class="p">[</span><span class="s1">'clip_gradient'</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span> |
| |
| <span class="k">if</span> <span class="n">aggregate</span><span class="p">:</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">multi_precision</span><span class="p">:</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">multi_sgd_mom_update</span><span class="p">(</span><span class="o">*</span><span class="n">_flatten_list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">states</span><span class="p">)),</span> <span class="n">out</span><span class="o">=</span><span class="n">weights</span><span class="p">,</span> |
| <span class="n">num_weights</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">weights</span><span class="p">),</span> <span class="n">lrs</span><span class="o">=</span><span class="n">lrs</span><span class="p">,</span> <span class="n">wds</span><span class="o">=</span><span class="n">wds</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">multi_sgd_update</span><span class="p">(</span><span class="o">*</span><span class="n">_flatten_list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">)),</span> <span class="n">out</span><span class="o">=</span><span class="n">weights</span><span class="p">,</span> |
| <span class="n">num_weights</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">weights</span><span class="p">),</span> <span class="n">lrs</span><span class="o">=</span><span class="n">lrs</span><span class="p">,</span> <span class="n">wds</span><span class="o">=</span><span class="n">wds</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">multi_mp_sgd_mom_update</span><span class="p">(</span><span class="o">*</span><span class="n">_flatten_list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="o">*</span><span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">states</span><span class="p">))),</span> |
| <span class="n">out</span><span class="o">=</span><span class="n">weights</span><span class="p">,</span> <span class="n">num_weights</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">weights</span><span class="p">),</span> |
| <span class="n">lrs</span><span class="o">=</span><span class="n">lrs</span><span class="p">,</span> <span class="n">wds</span><span class="o">=</span><span class="n">wds</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">multi_mp_sgd_update</span><span class="p">(</span><span class="o">*</span><span class="n">_flatten_list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> |
| <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">states</span><span class="p">))[</span><span class="mi">1</span><span class="p">])),</span> |
| <span class="n">out</span><span class="o">=</span><span class="n">weights</span><span class="p">,</span> <span class="n">num_weights</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">weights</span><span class="p">),</span> |
| <span class="n">lrs</span><span class="o">=</span><span class="n">lrs</span><span class="p">,</span> <span class="n">wds</span><span class="o">=</span><span class="n">wds</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">,</span> <span class="n">lr</span><span class="p">,</span> <span class="n">wd</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">states</span><span class="p">,</span> <span class="n">lrs</span><span class="p">,</span> <span class="n">wds</span><span class="p">):</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">multi_precision</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">state</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">sgd_mom_update</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> |
| <span class="n">lazy_update</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lazy_update</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">sgd_update</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> <span class="n">lazy_update</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lazy_update</span><span class="p">,</span> |
| <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">state</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">mp_sgd_mom_update</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">state</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">out</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> |
| <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">mp_sgd_update</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">out</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> |
| <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_update_impl</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">,</span> <span class="n">multi_precision</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">update_multi_precision</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span> |
| <span class="n">use_multi_precision</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">multi_precision</span> <span class="ow">and</span> <span class="n">weight</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float16</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">use_multi_precision</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">multi_precision</span> <span class="ow">and</span> <span class="n">weight</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float16</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_update_impl</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">,</span> |
| <span class="n">multi_precision</span><span class="o">=</span><span class="n">use_multi_precision</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Signum"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.Signum.html#mxnet.optimizer.Signum">[docs]</a><span class="nd">@register</span> |
| <span class="k">class</span> <span class="nc">Signum</span><span class="p">(</span><span class="n">Optimizer</span><span class="p">):</span> |
| <span class="sa">r</span><span class="sd">"""The Signum optimizer that takes the sign of gradient or momentum.</span> |
| |
| <span class="sd"> The optimizer updates the weight by::</span> |
| |
| <span class="sd"> rescaled_grad = rescale_grad * clip(grad, clip_gradient) + wd * weight</span> |
| <span class="sd"> state = momentum * state + (1-momentum)*rescaled_grad</span> |
| <span class="sd"> weight = (1 - lr * wd_lh) * weight - lr * sign(state)</span> |
| |
| <span class="sd"> References</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli & Anima Anandkumar. (2018).</span> |
| <span class="sd"> signSGD: Compressed Optimisation for Non-Convex Problems. In ICML'18.</span> |
| |
| <span class="sd"> See: https://arxiv.org/abs/1802.04434</span> |
| |
| <span class="sd"> For details of the update algorithm see</span> |
| <span class="sd"> :class:`~mxnet.ndarray.signsgd_update` and :class:`~mxnet.ndarray.signum_update`.</span> |
| |
| <span class="sd"> This optimizer accepts the following parameters in addition to those accepted</span> |
| <span class="sd"> by :class:`.Optimizer`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> momentum : float, optional</span> |
| <span class="sd"> The momentum value.</span> |
| <span class="sd"> wd_lh : float, optional</span> |
| <span class="sd"> The amount of decoupled weight decay regularization, see details in the original paper at:\</span> |
| <span class="sd"> https://arxiv.org/abs/1711.05101</span> |
| <span class="sd"> """</span> |
| <div class="viewcode-block" id="Signum.__init__"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.Signum.html#mxnet.optimizer.Signum.__init__">[docs]</a> <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">wd_lh</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Signum</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">learning_rate</span><span class="o">=</span><span class="n">learning_rate</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> <span class="o">=</span> <span class="n">momentum</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">wd_lh</span> <span class="o">=</span> <span class="n">wd_lh</span></div> |
| |
| <span class="k">def</span> <span class="nf">create_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> |
| <span class="n">momentum</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> <span class="o">!=</span> <span class="mf">0.0</span><span class="p">:</span> |
| <span class="n">momentum</span> <span class="o">=</span> <span class="n">zeros</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">stype</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">stype</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">momentum</span> |
| |
| <span class="k">def</span> <span class="nf">_update_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_update_count</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">lr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_lr</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">wd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_wd</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| |
| <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'rescale_grad'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">rescale_grad</span><span class="p">}</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">kwargs</span><span class="p">[</span><span class="s1">'momentum'</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span><span class="p">:</span> |
| <span class="n">kwargs</span><span class="p">[</span><span class="s1">'clip_gradient'</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">wd_lh</span><span class="p">:</span> |
| <span class="n">kwargs</span><span class="p">[</span><span class="s1">'wd_lh'</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">wd_lh</span> |
| |
| <span class="k">if</span> <span class="n">state</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">signum_update</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> |
| <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">signsgd_update</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> |
| <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_update_impl</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="FTML"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.FTML.html#mxnet.optimizer.FTML">[docs]</a><span class="nd">@register</span> |
| <span class="k">class</span> <span class="nc">FTML</span><span class="p">(</span><span class="n">Optimizer</span><span class="p">):</span> |
| <span class="sd">"""The FTML optimizer.</span> |
| |
| <span class="sd"> This class implements the optimizer described in</span> |
| <span class="sd"> *FTML - Follow the Moving Leader in Deep Learning*,</span> |
| <span class="sd"> available at http://proceedings.mlr.press/v70/zheng17a/zheng17a.pdf.</span> |
| |
| <span class="sd"> Denote time step by t. The optimizer updates the weight by::</span> |
| |
| <span class="sd"> rescaled_grad = clip(grad * rescale_grad + wd * weight, clip_gradient)</span> |
| <span class="sd"> v = beta2 * v + (1 - beta2) * square(rescaled_grad)</span> |
| <span class="sd"> d_t = (1 - power(beta1, t)) / lr * square_root(v / (1 - power(beta2, t))) + epsilon)</span> |
| <span class="sd"> z = beta1 * z + (1 - beta1) * rescaled_grad - (d_t - beta1 * d_(t-1)) * weight</span> |
| <span class="sd"> weight = - z / d_t</span> |
| |
| <span class="sd"> For details of the update algorithm, see :class:`~mxnet.ndarray.ftml_update`.</span> |
| |
| <span class="sd"> This optimizer accepts the following parameters in addition to those accepted</span> |
| <span class="sd"> by :class:`.Optimizer`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> beta1 : float, optional</span> |
| <span class="sd"> 0 < beta1 < 1. Generally close to 0.5.</span> |
| <span class="sd"> beta2 : float, optional</span> |
| <span class="sd"> 0 < beta2 < 1. Generally close to 1.</span> |
| <span class="sd"> epsilon : float, optional</span> |
| <span class="sd"> Small value to avoid division by 0.</span> |
| <span class="sd"> """</span> |
| <div class="viewcode-block" id="FTML.__init__"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.FTML.html#mxnet.optimizer.FTML.__init__">[docs]</a> <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">beta1</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">beta2</span><span class="o">=</span><span class="mf">0.999</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">FTML</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">beta1</span> <span class="o">=</span> <span class="n">beta1</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">beta2</span> <span class="o">=</span> <span class="n">beta2</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span> <span class="o">=</span> <span class="n">epsilon</span></div> |
| |
| <span class="k">def</span> <span class="nf">create_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">zeros</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span> <span class="c1"># d_0</span> |
| <span class="n">zeros</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span> <span class="c1"># v_0</span> |
| <span class="n">zeros</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">dtype</span><span class="p">))</span> <span class="c1"># z_0</span> |
| |
| <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_update_count</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">lr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_lr</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">wd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_wd</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">t</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_index_update_count</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> |
| |
| <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'beta1'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta1</span><span class="p">,</span> <span class="s1">'beta2'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta2</span><span class="p">,</span> <span class="s1">'epsilon'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span><span class="p">,</span> |
| <span class="s1">'rescale_grad'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">rescale_grad</span><span class="p">,</span> <span class="s1">'t'</span><span class="p">:</span> <span class="n">t</span><span class="p">}</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span><span class="p">:</span> |
| <span class="n">kwargs</span><span class="p">[</span><span class="s1">'clip_grad'</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span> |
| |
| <span class="n">prev_d</span><span class="p">,</span> <span class="n">prev_v</span><span class="p">,</span> <span class="n">prev_z</span> <span class="o">=</span> <span class="n">state</span> |
| <span class="n">ftml_update</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">prev_d</span><span class="p">,</span> <span class="n">prev_v</span><span class="p">,</span> <span class="n">prev_z</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> |
| <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="LBSGD"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.LBSGD.html#mxnet.optimizer.LBSGD">[docs]</a><span class="nd">@register</span> |
| <span class="k">class</span> <span class="nc">LBSGD</span><span class="p">(</span><span class="n">Optimizer</span><span class="p">):</span> |
| <span class="sd">"""The Large Batch SGD optimizer with momentum and weight decay.</span> |
| |
| <span class="sd"> The optimizer updates the weight by::</span> |
| |
| <span class="sd"> state = momentum * state + lr * rescale_grad * clip(grad, clip_gradient) + wd * weight</span> |
| <span class="sd"> weight = weight - state</span> |
| |
| <span class="sd"> For details of the update algorithm see :class:`~mxnet.ndarray.sgd_update`</span> |
| <span class="sd"> and :class:`~mxnet.ndarray.sgd_mom_update`.</span> |
| <span class="sd"> In addition to the SGD updates the LBSGD optimizer uses the LARS, Layer-wise</span> |
| <span class="sd"> Adaptive Rate Scaling, algorithm to have a separate learning rate for each</span> |
| <span class="sd"> layer of the network, which leads to better stability over large batch sizes.</span> |
| |
| <span class="sd"> This optimizer accepts the following parameters in addition to those accepted</span> |
| <span class="sd"> by :class:`.Optimizer`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> momentum : float, optional</span> |
| <span class="sd"> The momentum value.</span> |
| <span class="sd"> multi_precision: bool, optional</span> |
| <span class="sd"> Flag to control the internal precision of the optimizer.</span> |
| <span class="sd"> False: results in using the same precision as the weights (default),</span> |
| <span class="sd"> True: makes internal 32-bit copy of the weights and applies gradients</span> |
| <span class="sd"> in 32-bit precision even if actual weights used in the model have lower precision.</span> |
| <span class="sd"> Turning this on can improve convergence and accuracy when training with float16.</span> |
| |
| <span class="sd"> warmup_strategy: string ('linear', 'power2', 'sqrt'. , 'lars' default : 'linear')</span> |
| <span class="sd"> warmup_epochs: unsigned, default: 5</span> |
| <span class="sd"> batch_scale: unsigned, default: 1 (same as batch size*numworkers)</span> |
| <span class="sd"> updates_per_epoch: updates_per_epoch (default: 32, Default might not reflect true number batches per epoch. Used for warmup.)</span> |
| <span class="sd"> begin_epoch: unsigned, default 0, starting epoch.</span> |
| <span class="sd"> """</span> |
| <div class="viewcode-block" id="LBSGD.__init__"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.LBSGD.html#mxnet.optimizer.LBSGD.__init__">[docs]</a> <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">multi_precision</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">warmup_strategy</span><span class="o">=</span><span class="s1">'linear'</span><span class="p">,</span> |
| <span class="n">warmup_epochs</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">batch_scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">updates_per_epoch</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">begin_epoch</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_epochs</span><span class="o">=</span><span class="mi">60</span><span class="p">,</span> |
| <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">LBSGD</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">'Running Large-Batch SGD Algorithm'</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">'(Batch_scale=</span><span class="si">%f</span><span class="s1">, warmup_epochs=</span><span class="si">%d</span><span class="s1">, warmup_strategy=</span><span class="si">%s</span><span class="s1">, updates_per_epoch=</span><span class="si">%d</span><span class="s1">)'</span><span class="p">,</span> |
| <span class="n">batch_scale</span><span class="p">,</span> <span class="n">warmup_epochs</span><span class="p">,</span> <span class="n">warmup_strategy</span><span class="p">,</span> <span class="n">updates_per_epoch</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> <span class="o">=</span> <span class="n">momentum</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">multi_precision</span> <span class="o">=</span> <span class="n">multi_precision</span> |
| <span class="c1"># new user parameters for large batch</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">warmup_strategy</span> <span class="o">=</span> <span class="n">warmup_strategy</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">warmup_epochs</span> <span class="o">=</span> <span class="n">warmup_epochs</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">batch_scale</span> <span class="o">=</span> <span class="n">batch_scale</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">updates_per_epoch</span> <span class="o">=</span> <span class="n">updates_per_epoch</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">init_updates</span> <span class="o">=</span> <span class="n">begin_epoch</span> <span class="o">*</span> <span class="n">updates_per_epoch</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_epochs</span> <span class="o">=</span> <span class="n">num_epochs</span> |
| <span class="c1"># addl internal usage parameters and storage</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">lbmult</span> <span class="o">=</span> <span class="mi">1</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">cumgrads</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="c1"># for adaptive lr</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">adaptive</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">admult</span> <span class="o">=</span> <span class="mi">1</span> <span class="c1"># adaptation constant</span></div> |
| |
| <span class="k">def</span> <span class="nf">create_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> |
| <span class="n">momentum</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="n">weight_master_copy</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">multi_precision</span> <span class="ow">and</span> <span class="n">weight</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float16</span><span class="p">:</span> |
| <span class="n">weight_master_copy</span> <span class="o">=</span> <span class="n">array</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> <span class="o">!=</span> <span class="mf">0.0</span><span class="p">:</span> |
| <span class="n">momentum</span> <span class="o">=</span> <span class="n">zeros</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> |
| <span class="n">stype</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">stype</span><span class="p">)</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">momentum</span><span class="p">,</span> <span class="n">weight_master_copy</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">weight</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float16</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">multi_precision</span><span class="p">:</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">"Accumulating with float16 in optimizer can lead to "</span> |
| <span class="s2">"poor accuracy or slow convergence. "</span> |
| <span class="s2">"Consider using multi_precision=True option of the "</span> |
| <span class="s2">"SGD optimizer"</span><span class="p">)</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> <span class="o">!=</span> <span class="mf">0.0</span><span class="p">:</span> |
| <span class="n">momentum</span> <span class="o">=</span> <span class="n">zeros</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">stype</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">stype</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">momentum</span> |
| |
| <span class="k">def</span> <span class="nf">_get_lbmult</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nup</span><span class="p">):</span> |
| <span class="sd">"""Returns lr scaling factor for large batch according to warmup schedule</span> |
| <span class="sd"> (to be implemented)</span> |
| <span class="sd"> """</span> |
| <span class="n">nwup</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">warmup_epochs</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">updates_per_epoch</span> |
| <span class="n">strategy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">warmup_strategy</span> |
| <span class="n">maxmult</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">batch_scale</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">nup</span> <span class="o">>=</span> <span class="n">nwup</span><span class="p">:</span> |
| <span class="n">mult</span> <span class="o">=</span> <span class="n">maxmult</span> |
| <span class="k">elif</span> <span class="n">nwup</span> <span class="o"><=</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="n">mult</span> <span class="o">=</span> <span class="mf">1.0</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">if</span> <span class="p">(</span><span class="n">strategy</span> <span class="o">==</span> <span class="s1">'linear'</span><span class="p">):</span> |
| <span class="n">mult</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">+</span> <span class="p">(</span><span class="n">maxmult</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">nup</span> <span class="o">/</span> <span class="n">nwup</span> |
| <span class="k">elif</span> <span class="p">(</span><span class="n">strategy</span> <span class="o">==</span> <span class="s1">'power2'</span><span class="p">):</span> |
| <span class="n">mult</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">+</span> <span class="p">(</span><span class="n">maxmult</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">nup</span><span class="o">*</span><span class="n">nup</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">nwup</span><span class="o">*</span><span class="n">nwup</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="p">(</span><span class="n">strategy</span> <span class="o">==</span> <span class="s1">'sqrt'</span><span class="p">):</span> |
| <span class="n">mult</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">+</span> <span class="p">(</span><span class="n">maxmult</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">nup</span><span class="p">)</span> <span class="o">/</span> <span class="n">nwup</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">mult</span> <span class="o">=</span> <span class="mf">1.0</span> |
| <span class="k">return</span> <span class="n">mult</span> |
| |
| <span class="k">def</span> <span class="nf">_get_lars</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">wd</span><span class="p">):</span> |
| <span class="sd">"""Returns a scaling factor for the learning rate for this layer</span> |
| <span class="sd"> default is 1</span> |
| <span class="sd"> """</span> |
| <span class="n">weight2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_l2norm</span><span class="p">(</span><span class="n">weight</span><span class="p">)</span> |
| <span class="n">grad2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_l2norm</span><span class="p">(</span><span class="n">g</span><span class="p">)</span> |
| <span class="n">lars</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">weight2</span> <span class="o">/</span> <span class="p">(</span><span class="n">grad2</span> <span class="o">+</span> <span class="n">wd</span> <span class="o">*</span> <span class="n">weight2</span> <span class="o">+</span> <span class="mf">1e-18</span><span class="p">))</span> |
| <span class="k">if</span> <span class="n">lars</span> <span class="o"><</span> <span class="mf">0.01</span><span class="p">:</span> |
| <span class="n">lars</span> <span class="o">=</span> <span class="mf">0.01</span> |
| <span class="k">elif</span> <span class="n">lars</span> <span class="o">></span> <span class="mi">100</span><span class="p">:</span> |
| <span class="n">lars</span> <span class="o">=</span> <span class="mi">100</span> |
| <span class="k">return</span> <span class="n">lars</span> |
| |
| <span class="k">def</span> <span class="nf">_l2norm</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span> |
| <span class="s2">"inner product implementation"</span> |
| <span class="n">norm</span> <span class="o">=</span> <span class="n">multiply</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> |
| <span class="k">return</span> <span class="n">norm</span> |
| |
| <span class="k">def</span> <span class="nf">_reset_cum_gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span> |
| <span class="s2">"called every macro-batch to reset cumulated gradients to 0 for a given index"</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">cumgrads</span><span class="p">[</span><span class="n">index</span><span class="p">][</span><span class="s1">'cum_grad'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span> |
| |
| <span class="k">def</span> <span class="nf">_get_cum_gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span> |
| <span class="s2">"get the cumulated gradient for index"</span> |
| <span class="k">if</span> <span class="n">index</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">cumgrads</span><span class="p">:</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">cumgrads</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="p">{}</span> |
| |
| <span class="k">def</span> <span class="nf">_put_cum_gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">cgrad</span><span class="p">):</span> |
| <span class="s2">"store cumulated gradient for index"</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">cumgrads</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">cgrad</span> |
| |
| <span class="k">def</span> <span class="nf">_cumulate_gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span> |
| <span class="s2">"Cumulate gradients for large-batch emulation. Cumulated by index (layer)"</span> |
| <span class="n">cgrad</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_cum_gradient</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">cgrad</span><span class="p">:</span> |
| <span class="n">num_cums</span> <span class="o">=</span> <span class="n">cgrad</span><span class="p">[</span><span class="s1">'num_cums'</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">num_cums</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">cum_grad</span> <span class="o">=</span> <span class="n">cgrad</span><span class="p">[</span><span class="s1">'cum_grad'</span><span class="p">]</span> <span class="o">+</span> <span class="n">grad</span> |
| <span class="n">num_cums</span> <span class="o">+=</span> <span class="mi">1</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">cum_grad</span> <span class="o">=</span> <span class="n">grad</span> |
| <span class="n">num_cums</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">init_updates</span> <span class="o">+</span> <span class="mi">1</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">cum_grad</span> <span class="o">=</span> <span class="n">grad</span> |
| <span class="n">num_cums</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">init_updates</span> <span class="o">+</span> <span class="mi">1</span> |
| <span class="n">cgrad</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'cum_grad'</span><span class="p">:</span> <span class="n">cum_grad</span><span class="p">,</span> <span class="s1">'num_cums'</span><span class="p">:</span> <span class="n">num_cums</span><span class="p">}</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_put_cum_gradient</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">cgrad</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">cgrad</span> |
| |
| <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="k">assert</span> <span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| |
| <span class="n">lr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_lr</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">wd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_wd</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_update_count</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| |
| <span class="c1"># new stuff for large batch</span> |
| <span class="n">cgrad</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cumulate_gradient</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="n">index</span><span class="p">)</span> |
| <span class="k">if</span> <span class="p">(</span><span class="n">cgrad</span><span class="p">[</span><span class="s1">'num_cums'</span><span class="p">]</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_scale</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">grad</span> <span class="o">=</span> <span class="n">cgrad</span><span class="p">[</span><span class="s1">'cum_grad'</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_scale</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">warmup_strategy</span> <span class="o">==</span> <span class="s1">'lars'</span><span class="p">:</span> |
| <span class="n">lbmult</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_lars</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">wd</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">lbmult</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_lbmult</span><span class="p">(</span><span class="n">cgrad</span><span class="p">[</span><span class="s1">'num_cums'</span><span class="p">])</span> |
| <span class="n">lr</span> <span class="o">=</span> <span class="n">lr</span> <span class="o">*</span> <span class="n">lbmult</span> |
| <span class="c1"># do the regular sgd update flow</span> |
| <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'rescale_grad'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">rescale_grad</span><span class="p">}</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">kwargs</span><span class="p">[</span><span class="s1">'momentum'</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span><span class="p">:</span> |
| <span class="n">kwargs</span><span class="p">[</span><span class="s1">'clip_gradient'</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span> |
| <span class="n">use_multi_precision</span> <span class="o">=</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">state</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">))</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">use_multi_precision</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">state</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">sgd_mom_update</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">sgd_update</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">state</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">mp_sgd_mom_update</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">state</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">out</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">,</span> |
| <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">mp_sgd_update</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">out</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="c1"># reset update count and cumulated gradient per large batch</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_reset_cum_gradient</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">lr</span> <span class="o">=</span> <span class="mf">0.0</span> |
| <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="n">sgd_update</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| <span class="c1"># pylint: enable=line-too-long</span> |
| <div class="viewcode-block" id="DCASGD"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.DCASGD.html#mxnet.optimizer.DCASGD">[docs]</a><span class="nd">@register</span> |
| <span class="k">class</span> <span class="nc">DCASGD</span><span class="p">(</span><span class="n">Optimizer</span><span class="p">):</span> |
| <span class="sd">"""The DCASGD optimizer.</span> |
| |
| <span class="sd"> This class implements the optimizer described in *Asynchronous Stochastic Gradient Descent</span> |
| <span class="sd"> with Delay Compensation for Distributed Deep Learning*,</span> |
| <span class="sd"> available at https://arxiv.org/abs/1609.08326.</span> |
| |
| <span class="sd"> This optimizer accepts the following parameters in addition to those accepted</span> |
| <span class="sd"> by :class:`.Optimizer`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> momentum : float, optional</span> |
| <span class="sd"> The momentum value.</span> |
| |
| <span class="sd"> lamda : float, optional</span> |
| <span class="sd"> Scale DC value.</span> |
| <span class="sd"> """</span> |
| <div class="viewcode-block" id="DCASGD.__init__"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.DCASGD.html#mxnet.optimizer.DCASGD.__init__">[docs]</a> <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">lamda</span><span class="o">=</span><span class="mf">0.04</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">DCASGD</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> <span class="o">=</span> <span class="n">momentum</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">weight_previous</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">lamda</span> <span class="o">=</span> <span class="n">lamda</span></div> |
| |
| <span class="k">def</span> <span class="nf">create_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> <span class="o">==</span> <span class="mf">0.0</span><span class="p">:</span> |
| <span class="k">return</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">weight</span><span class="o">.</span><span class="n">copy</span><span class="p">())</span> <span class="c1"># previous weight</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">zeros</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span> <span class="c1"># momentum</span> |
| <span class="n">weight</span><span class="o">.</span><span class="n">copy</span><span class="p">())</span> <span class="c1"># previous weight</span> |
| |
| <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_update_count</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">lr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_lr</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">wd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_wd</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| |
| <span class="n">grad</span> <span class="o">=</span> <span class="n">grad</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">rescale_grad</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">grad</span> <span class="o">=</span> <span class="n">clip</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span><span class="p">)</span> |
| |
| <span class="n">mom</span><span class="p">,</span> <span class="n">previous_weight</span> <span class="o">=</span> <span class="n">state</span> |
| <span class="k">if</span> <span class="n">mom</span><span class="p">:</span> |
| <span class="n">mom</span><span class="p">[:]</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> |
| <span class="n">mom</span><span class="p">[:]</span> <span class="o">+=</span> <span class="o">-</span><span class="n">lr</span> <span class="o">*</span> <span class="p">(</span><span class="n">grad</span> <span class="o">+</span> <span class="n">wd</span> <span class="o">*</span> <span class="n">weight</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">lamda</span> \ |
| <span class="o">*</span> <span class="n">grad</span> <span class="o">*</span> <span class="n">grad</span> <span class="o">*</span> <span class="p">(</span><span class="n">weight</span> <span class="o">-</span> <span class="n">previous_weight</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">assert</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> <span class="o">==</span> <span class="mf">0.0</span><span class="p">)</span> |
| <span class="n">mom</span> <span class="o">=</span> <span class="o">-</span><span class="n">lr</span> <span class="o">*</span> <span class="p">(</span><span class="n">grad</span> <span class="o">+</span> <span class="n">wd</span> <span class="o">*</span> <span class="n">weight</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">lamda</span> \ |
| <span class="o">*</span> <span class="n">grad</span> <span class="o">*</span> <span class="n">grad</span> <span class="o">*</span> <span class="p">(</span><span class="n">weight</span> <span class="o">-</span> <span class="n">previous_weight</span><span class="p">))</span> |
| <span class="n">previous_weight</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">weight</span> |
| <span class="n">weight</span><span class="p">[:]</span> <span class="o">+=</span> <span class="n">mom</span></div> |
| |
| <div class="viewcode-block" id="NAG"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.NAG.html#mxnet.optimizer.NAG">[docs]</a><span class="nd">@register</span> |
| <span class="k">class</span> <span class="nc">NAG</span><span class="p">(</span><span class="n">Optimizer</span><span class="p">):</span> |
| <span class="sd">"""Nesterov accelerated gradient.</span> |
| |
| <span class="sd"> This optimizer updates each weight by::</span> |
| |
| <span class="sd"> state = momentum * state + grad + wd * weight</span> |
| <span class="sd"> weight = weight - (lr * (grad + momentum * state))</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> momentum : float, optional</span> |
| <span class="sd"> The momentum value.</span> |
| <span class="sd"> multi_precision: bool, optional</span> |
| <span class="sd"> Flag to control the internal precision of the optimizer.</span> |
| <span class="sd"> False: results in using the same precision as the weights (default),</span> |
| <span class="sd"> True: makes internal 32-bit copy of the weights and applies gradients</span> |
| <span class="sd"> in 32-bit precision even if actual weights used in the model have lower precision.</span> |
| <span class="sd"> Turning this on can improve convergence and accuracy when training with float16.</span> |
| <span class="sd"> """</span> |
| <div class="viewcode-block" id="NAG.__init__"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.NAG.html#mxnet.optimizer.NAG.__init__">[docs]</a> <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">NAG</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> <span class="o">=</span> <span class="n">momentum</span></div> |
| |
| <span class="k">def</span> <span class="nf">create_state_multi_precision</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> |
| <span class="n">weight_master_copy</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">multi_precision</span> <span class="ow">and</span> <span class="n">weight</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float16</span><span class="p">:</span> |
| <span class="n">weight_master_copy</span> <span class="o">=</span> <span class="n">weight</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> |
| <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">create_state</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">weight_master_copy</span><span class="p">),</span> <span class="n">weight_master_copy</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">weight</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float16</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">multi_precision</span><span class="p">:</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">"Accumulating with float16 in optimizer can lead to "</span> |
| <span class="s2">"poor accuracy or slow convergence. "</span> |
| <span class="s2">"Consider using multi_precision=True option of the "</span> |
| <span class="s2">"NAG optimizer"</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_state</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">create_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> |
| <span class="n">momentum</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> <span class="o">!=</span> <span class="mf">0.0</span><span class="p">:</span> |
| <span class="n">momentum</span> <span class="o">=</span> <span class="n">zeros</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">momentum</span> |
| |
| <span class="k">def</span> <span class="nf">_update_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">,</span> <span class="n">multi_precision</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_update_count</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">lr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_lr</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">wd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_wd</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| |
| <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'rescale_grad'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">rescale_grad</span><span class="p">}</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">kwargs</span><span class="p">[</span><span class="s1">'momentum'</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span><span class="p">:</span> |
| <span class="n">kwargs</span><span class="p">[</span><span class="s1">'clip_gradient'</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">multi_precision</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">state</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">nag_mom_update</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">sgd_update</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">state</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">mp_nag_mom_update</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">state</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">out</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> |
| <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">mp_sgd_update</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">out</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> |
| <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_update_impl</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">,</span> <span class="n">multi_precision</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">update_multi_precision</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span> |
| <span class="n">use_multi_precision</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">multi_precision</span> <span class="ow">and</span> <span class="n">weight</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float16</span> \ |
| <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">state</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_update_impl</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">,</span> |
| <span class="n">multi_precision</span><span class="o">=</span><span class="n">use_multi_precision</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="SGLD"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.SGLD.html#mxnet.optimizer.SGLD">[docs]</a><span class="nd">@register</span> |
| <span class="k">class</span> <span class="nc">SGLD</span><span class="p">(</span><span class="n">Optimizer</span><span class="p">):</span> |
| <span class="sd">"""Stochastic Gradient Riemannian Langevin Dynamics.</span> |
| |
| <span class="sd"> This class implements the optimizer described in the paper *Stochastic Gradient</span> |
| <span class="sd"> Riemannian Langevin Dynamics on the Probability Simplex*, available at</span> |
| <span class="sd"> https://papers.nips.cc/paper/4883-stochastic-gradient-riemannian-langevin-dynamics-on-the-probability-simplex.pdf.</span> |
| |
| <span class="sd"> """</span> |
| <div class="viewcode-block" id="SGLD.__init__"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.SGLD.html#mxnet.optimizer.SGLD.__init__">[docs]</a> <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">SGLD</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| <span class="k">def</span> <span class="nf">create_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> |
| <span class="k">return</span> <span class="kc">None</span> |
| |
| <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_update_count</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">lr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_lr</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">wd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_wd</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| |
| <span class="n">grad</span> <span class="o">=</span> <span class="n">grad</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">rescale_grad</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">grad</span> <span class="o">=</span> <span class="n">clip</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span><span class="p">)</span> |
| <span class="n">weight</span><span class="p">[:]</span> <span class="o">+=</span> <span class="o">-</span> <span class="n">lr</span><span class="o">/</span><span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">grad</span> <span class="o">+</span> <span class="n">wd</span> <span class="o">*</span> <span class="n">weight</span><span class="p">)</span> |
| <span class="n">weight</span><span class="p">[:]</span> <span class="o">+=</span> <span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">lr</span><span class="p">),</span> <span class="n">shape</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> |
| <span class="n">dtype</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">)</span></div> |
| |
| |
| |
| <span class="nd">@register</span> <span class="c1"># pylint: disable=invalid-name</span> |
| <span class="k">class</span> <span class="nc">ccSGD</span><span class="p">(</span><span class="n">SGD</span><span class="p">):</span> |
| <span class="sd">"""[DEPRECATED] Same as `SGD`. Left here for backward compatibility."""</span> |
| <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">ccSGD</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="Adam"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.Adam.html#mxnet.optimizer.Adam">[docs]</a><span class="nd">@register</span> |
| <span class="k">class</span> <span class="nc">Adam</span><span class="p">(</span><span class="n">Optimizer</span><span class="p">):</span> |
| <span class="sd">"""The Adam optimizer.</span> |
| |
| <span class="sd"> This class implements the optimizer described in *Adam: A Method for</span> |
| <span class="sd"> Stochastic Optimization*, available at http://arxiv.org/abs/1412.6980.</span> |
| |
| <span class="sd"> If the storage types of grad is ``row_sparse``, and ``lazy_update`` is True, \</span> |
| <span class="sd"> **lazy updates** at step t are applied by::</span> |
| |
| <span class="sd"> for row in grad.indices:</span> |
| <span class="sd"> rescaled_grad[row] = clip(grad[row] * rescale_grad + wd * weight[row], clip_gradient)</span> |
| <span class="sd"> m[row] = beta1 * m[row] + (1 - beta1) * rescaled_grad[row]</span> |
| <span class="sd"> v[row] = beta2 * v[row] + (1 - beta2) * (rescaled_grad[row]**2)</span> |
| <span class="sd"> lr = learning_rate * sqrt(1 - beta1**t) / (1 - beta2**t)</span> |
| <span class="sd"> w[row] = w[row] - lr * m[row] / (sqrt(v[row]) + epsilon)</span> |
| |
| <span class="sd"> The lazy update only updates the mean and var for the weights whose row_sparse</span> |
| <span class="sd"> gradient indices appear in the current batch, rather than updating it for all indices.</span> |
| <span class="sd"> Compared with the original update, it can provide large improvements in model training</span> |
| <span class="sd"> throughput for some applications. However, it provides slightly different semantics than</span> |
| <span class="sd"> the original update, and may lead to different empirical results.</span> |
| |
| <span class="sd"> Otherwise, **standard updates** at step t are applied by::</span> |
| |
| <span class="sd"> rescaled_grad = clip(grad * rescale_grad + wd * weight, clip_gradient)</span> |
| <span class="sd"> m = beta1 * m + (1 - beta1) * rescaled_grad</span> |
| <span class="sd"> v = beta2 * v + (1 - beta2) * (rescaled_grad**2)</span> |
| <span class="sd"> lr = learning_rate * sqrt(1 - beta1**t) / (1 - beta2**t)</span> |
| <span class="sd"> w = w - lr * m / (sqrt(v) + epsilon)</span> |
| |
| <span class="sd"> This optimizer accepts the following parameters in addition to those accepted</span> |
| <span class="sd"> by :class:`.Optimizer`.</span> |
| |
| <span class="sd"> For details of the update algorithm, see :class:`~mxnet.ndarray.adam_update`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> beta1 : float, optional</span> |
| <span class="sd"> Exponential decay rate for the first moment estimates.</span> |
| <span class="sd"> beta2 : float, optional</span> |
| <span class="sd"> Exponential decay rate for the second moment estimates.</span> |
| <span class="sd"> epsilon : float, optional</span> |
| <span class="sd"> Small value to avoid division by 0.</span> |
| <span class="sd"> lazy_update : bool, optional</span> |
| <span class="sd"> Default is True. If True, lazy updates are applied \</span> |
| <span class="sd"> if the storage types of weight and grad are both ``row_sparse``.</span> |
| <span class="sd"> """</span> |
| <div class="viewcode-block" id="Adam.__init__"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.Adam.html#mxnet.optimizer.Adam.__init__">[docs]</a> <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">beta1</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">beta2</span><span class="o">=</span><span class="mf">0.999</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">,</span> |
| <span class="n">lazy_update</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Adam</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">learning_rate</span><span class="o">=</span><span class="n">learning_rate</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">beta1</span> <span class="o">=</span> <span class="n">beta1</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">beta2</span> <span class="o">=</span> <span class="n">beta2</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span> <span class="o">=</span> <span class="n">epsilon</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">lazy_update</span> <span class="o">=</span> <span class="n">lazy_update</span></div> |
| |
| <span class="k">def</span> <span class="nf">create_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> |
| <span class="n">stype</span> <span class="o">=</span> <span class="n">weight</span><span class="o">.</span><span class="n">stype</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lazy_update</span> <span class="k">else</span> <span class="s1">'default'</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">zeros</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> |
| <span class="n">stype</span><span class="o">=</span><span class="n">stype</span><span class="p">),</span> <span class="c1"># mean</span> |
| <span class="n">zeros</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> |
| <span class="n">stype</span><span class="o">=</span><span class="n">stype</span><span class="p">))</span> <span class="c1"># variance</span> |
| |
| <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_update_count</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">lr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_lr</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">wd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_wd</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| |
| <span class="n">t</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_index_update_count</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> |
| <span class="n">coef1</span> <span class="o">=</span> <span class="mf">1.</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta1</span><span class="o">**</span><span class="n">t</span> |
| <span class="n">coef2</span> <span class="o">=</span> <span class="mf">1.</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta2</span><span class="o">**</span><span class="n">t</span> |
| <span class="n">lr</span> <span class="o">*=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">coef2</span><span class="p">)</span><span class="o">/</span><span class="n">coef1</span> |
| |
| <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'beta1'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta1</span><span class="p">,</span> <span class="s1">'beta2'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta2</span><span class="p">,</span> <span class="s1">'epsilon'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span><span class="p">,</span> |
| <span class="s1">'rescale_grad'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">rescale_grad</span><span class="p">}</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span><span class="p">:</span> |
| <span class="n">kwargs</span><span class="p">[</span><span class="s1">'clip_gradient'</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span> |
| |
| <span class="n">mean</span><span class="p">,</span> <span class="n">var</span> <span class="o">=</span> <span class="n">state</span> |
| <span class="n">adam_update</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> |
| <span class="n">lazy_update</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lazy_update</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="AdaGrad"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.AdaGrad.html#mxnet.optimizer.AdaGrad">[docs]</a><span class="nd">@register</span> |
| <span class="k">class</span> <span class="nc">AdaGrad</span><span class="p">(</span><span class="n">Optimizer</span><span class="p">):</span> |
| <span class="sd">"""AdaGrad optimizer.</span> |
| |
| <span class="sd"> This class implements the AdaGrad optimizer described in *Adaptive Subgradient</span> |
| <span class="sd"> Methods for Online Learning and Stochastic Optimization*, and available at</span> |
| <span class="sd"> http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf.</span> |
| |
| <span class="sd"> This optimizer updates each weight by::</span> |
| |
| <span class="sd"> grad = clip(grad * rescale_grad, clip_gradient)</span> |
| <span class="sd"> history += square(grad)</span> |
| <span class="sd"> div = grad / sqrt(history + float_stable_eps)</span> |
| <span class="sd"> weight += (div + weight * wd) * -lr</span> |
| |
| <span class="sd"> This optimizer accepts the following parameters in addition to those accepted</span> |
| <span class="sd"> by :class:`.Optimizer`.</span> |
| |
| <span class="sd"> See Also</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> :meth:`mxnet.ndarray.sparse.adagrad_update`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> eps: float, optional</span> |
| <span class="sd"> Initial value of the history accumulator. Avoids division by 0.</span> |
| |
| <span class="sd"> """</span> |
| <div class="viewcode-block" id="AdaGrad.__init__"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.AdaGrad.html#mxnet.optimizer.AdaGrad.__init__">[docs]</a> <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-7</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">AdaGrad</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">float_stable_eps</span> <span class="o">=</span> <span class="n">eps</span></div> |
| |
| <span class="k">def</span> <span class="nf">create_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">zeros</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">stype</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">stype</span><span class="p">)</span> <span class="c1"># history</span> |
| |
| <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_update_count</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">lr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_lr</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">wd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_wd</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| |
| <span class="n">is_sparse</span> <span class="o">=</span> <span class="n">grad</span><span class="o">.</span><span class="n">stype</span> <span class="o">==</span> <span class="s1">'row_sparse'</span> |
| <span class="n">history</span> <span class="o">=</span> <span class="n">state</span> |
| |
| <span class="k">if</span> <span class="n">is_sparse</span><span class="p">:</span> |
| <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'epsilon'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">float_stable_eps</span><span class="p">,</span> |
| <span class="s1">'rescale_grad'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">rescale_grad</span><span class="p">}</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span><span class="p">:</span> |
| <span class="n">kwargs</span><span class="p">[</span><span class="s1">'clip_gradient'</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span> |
| <span class="n">sparse</span><span class="o">.</span><span class="n">adagrad_update</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">history</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">grad</span> <span class="o">=</span> <span class="n">grad</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">rescale_grad</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">grad</span> <span class="o">=</span> <span class="n">clip</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span><span class="p">)</span> |
| <span class="n">history</span><span class="p">[:]</span> <span class="o">+=</span> <span class="n">square</span><span class="p">(</span><span class="n">grad</span><span class="p">)</span> |
| <span class="n">div</span> <span class="o">=</span> <span class="n">grad</span> <span class="o">/</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">history</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">float_stable_eps</span><span class="p">)</span> |
| <span class="n">weight</span><span class="p">[:]</span> <span class="o">+=</span> <span class="p">(</span><span class="n">div</span> <span class="o">+</span> <span class="n">weight</span> <span class="o">*</span> <span class="n">wd</span><span class="p">)</span> <span class="o">*</span> <span class="o">-</span><span class="n">lr</span></div> |
| |
| <div class="viewcode-block" id="RMSProp"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.RMSProp.html#mxnet.optimizer.RMSProp">[docs]</a><span class="nd">@register</span> |
| <span class="k">class</span> <span class="nc">RMSProp</span><span class="p">(</span><span class="n">Optimizer</span><span class="p">):</span> |
| <span class="sd">"""The RMSProp optimizer.</span> |
| |
| <span class="sd"> Two versions of RMSProp are implemented:</span> |
| |
| <span class="sd"> If ``centered=False``, we follow</span> |
| <span class="sd"> http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf by</span> |
| <span class="sd"> Tieleman & Hinton, 2012.</span> |
| <span class="sd"> For details of the update algorithm see :class:`~mxnet.ndarray.rmsprop_update`.</span> |
| |
| <span class="sd"> If ``centered=True``, we follow http://arxiv.org/pdf/1308.0850v5.pdf (38)-(45)</span> |
| <span class="sd"> by Alex Graves, 2013.</span> |
| <span class="sd"> For details of the update algorithm see :class:`~mxnet.ndarray.rmspropalex_update`.</span> |
| |
| <span class="sd"> This optimizer accepts the following parameters in addition to those accepted</span> |
| <span class="sd"> by :class:`.Optimizer`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> gamma1: float, optional</span> |
| <span class="sd"> A decay factor of moving average over past squared gradient.</span> |
| <span class="sd"> gamma2: float, optional</span> |
| <span class="sd"> A "momentum" factor. Only used if `centered`=``True``.</span> |
| <span class="sd"> epsilon : float, optional</span> |
| <span class="sd"> Small value to avoid division by 0.</span> |
| <span class="sd"> centered : bool, optional</span> |
| <span class="sd"> Flag to control which version of RMSProp to use.::</span> |
| |
| <span class="sd"> True: will use Graves's version of `RMSProp`,</span> |
| <span class="sd"> False: will use Tieleman & Hinton's version of `RMSProp`.</span> |
| |
| <span class="sd"> clip_weights : float, optional</span> |
| <span class="sd"> Clips weights into range ``[-clip_weights, clip_weights]``.</span> |
| <span class="sd"> """</span> |
| <div class="viewcode-block" id="RMSProp.__init__"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.RMSProp.html#mxnet.optimizer.RMSProp.__init__">[docs]</a> <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">gamma1</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">gamma2</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> |
| <span class="n">epsilon</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">,</span> <span class="n">centered</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">clip_weights</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">RMSProp</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">learning_rate</span><span class="o">=</span><span class="n">learning_rate</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">gamma1</span> <span class="o">=</span> <span class="n">gamma1</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">gamma2</span> <span class="o">=</span> <span class="n">gamma2</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">centered</span> <span class="o">=</span> <span class="n">centered</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span> <span class="o">=</span> <span class="n">epsilon</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">clip_weights</span> <span class="o">=</span> <span class="n">clip_weights</span></div> |
| |
| <span class="k">def</span> <span class="nf">create_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">centered</span><span class="p">:</span> |
| <span class="k">return</span> <span class="p">(</span> |
| <span class="n">zeros</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">stype</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">stype</span><span class="p">),</span> <span class="c1"># n</span> |
| <span class="n">zeros</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">stype</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">stype</span><span class="p">),</span> <span class="c1"># g</span> |
| <span class="n">zeros</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">stype</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">stype</span><span class="p">))</span> <span class="c1"># delta</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">zeros</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">stype</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">stype</span><span class="p">),)</span> <span class="c1"># n</span> |
| |
| <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_update_count</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">lr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_lr</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">wd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_wd</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| |
| <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'gamma1'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma1</span><span class="p">,</span> <span class="s1">'epsilon'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span><span class="p">,</span> |
| <span class="s1">'rescale_grad'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">rescale_grad</span><span class="p">}</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">centered</span><span class="p">:</span> |
| <span class="n">kwargs</span><span class="p">[</span><span class="s1">'gamma2'</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma2</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span><span class="p">:</span> |
| <span class="n">kwargs</span><span class="p">[</span><span class="s1">'clip_gradient'</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_weights</span><span class="p">:</span> |
| <span class="n">kwargs</span><span class="p">[</span><span class="s1">'clip_weights'</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_weights</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">centered</span><span class="p">:</span> |
| <span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="p">)</span> <span class="o">=</span> <span class="n">state</span> |
| <span class="n">rmsprop_update</span><span class="p">(</span> |
| <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">n</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">delta</span> <span class="o">=</span> <span class="n">state</span> |
| <span class="n">rmspropalex_update</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">delta</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> |
| <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="AdaDelta"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.AdaDelta.html#mxnet.optimizer.AdaDelta">[docs]</a><span class="nd">@register</span> |
| <span class="k">class</span> <span class="nc">AdaDelta</span><span class="p">(</span><span class="n">Optimizer</span><span class="p">):</span> |
| <span class="sd">"""The AdaDelta optimizer.</span> |
| |
| <span class="sd"> This class implements AdaDelta, an optimizer described in *ADADELTA: An adaptive</span> |
| <span class="sd"> learning rate method*, available at https://arxiv.org/abs/1212.5701.</span> |
| |
| <span class="sd"> This optimizer updates each weight by::</span> |
| |
| <span class="sd"> grad = clip(grad * rescale_grad + wd * weight, clip_gradient)</span> |
| <span class="sd"> acc_grad = rho * acc_grad + (1. - rho) * grad * grad</span> |
| <span class="sd"> delta = sqrt(acc_delta + epsilon) / sqrt(acc_grad + epsilon) * grad</span> |
| <span class="sd"> acc_delta = rho * acc_delta + (1. - rho) * delta * delta</span> |
| <span class="sd"> weight -= (delta + wd * weight)</span> |
| |
| <span class="sd"> This optimizer accepts the following parameters in addition to those accepted</span> |
| <span class="sd"> by :class:`.Optimizer`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> rho: float</span> |
| <span class="sd"> Decay rate for both squared gradients and delta.</span> |
| <span class="sd"> epsilon : float</span> |
| <span class="sd"> Small value to avoid division by 0.</span> |
| <span class="sd"> """</span> |
| <div class="viewcode-block" id="AdaDelta.__init__"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.AdaDelta.html#mxnet.optimizer.AdaDelta.__init__">[docs]</a> <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">rho</span><span class="o">=</span><span class="mf">0.90</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">AdaDelta</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">rho</span> <span class="o">=</span> <span class="n">rho</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span> <span class="o">=</span> <span class="n">epsilon</span></div> |
| |
| <span class="k">def</span> <span class="nf">create_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">zeros</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">),</span> <span class="c1"># accumulated g</span> |
| <span class="n">zeros</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">))</span> <span class="c1"># accumulated delta</span> |
| |
| <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="n">wd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_wd</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_update_count</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| |
| <span class="c1"># preprocess grad</span> |
| <span class="n">grad</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rescale_grad</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">grad</span> <span class="o">=</span> <span class="n">clip</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span><span class="p">)</span> |
| |
| <span class="c1"># accumulated g and delta initlization</span> |
| <span class="n">acc_g</span><span class="p">,</span> <span class="n">acc_delta</span> <span class="o">=</span> <span class="n">state</span> |
| |
| <span class="c1"># update g, delta</span> |
| <span class="n">acc_g</span><span class="p">[:]</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rho</span> |
| <span class="n">acc_g</span><span class="p">[:]</span> <span class="o">+=</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">rho</span><span class="p">)</span> <span class="o">*</span> <span class="n">grad</span> <span class="o">*</span> <span class="n">grad</span> |
| <span class="n">current_delta</span> <span class="o">=</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">acc_delta</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span><span class="p">)</span> <span class="o">/</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">acc_g</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span><span class="p">)</span> <span class="o">*</span> <span class="n">grad</span> |
| <span class="n">acc_delta</span><span class="p">[:]</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rho</span> |
| <span class="n">acc_delta</span><span class="p">[:]</span> <span class="o">+=</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">rho</span><span class="p">)</span> <span class="o">*</span> <span class="n">current_delta</span> <span class="o">*</span> <span class="n">current_delta</span> |
| |
| <span class="c1"># update weight</span> |
| <span class="n">weight</span><span class="p">[:]</span> <span class="o">-=</span> <span class="n">current_delta</span> <span class="o">+</span> <span class="n">wd</span> <span class="o">*</span> <span class="n">weight</span></div> |
| |
| <span class="c1">#pylint: disable=invalid-name</span> |
| <span class="c1">#pylint: disable=line-too-long</span> |
| <div class="viewcode-block" id="Ftrl"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.Ftrl.html#mxnet.optimizer.Ftrl">[docs]</a><span class="nd">@register</span> |
| <span class="k">class</span> <span class="nc">Ftrl</span><span class="p">(</span><span class="n">Optimizer</span><span class="p">):</span> |
| <span class="sd">"""The Ftrl optimizer.</span> |
| |
| <span class="sd"> Referenced from *Ad Click Prediction: a View from the Trenches*, available at</span> |
| <span class="sd"> http://dl.acm.org/citation.cfm?id=2488200.</span> |
| |
| <span class="sd"> eta :</span> |
| <span class="sd"> .. math::</span> |
| <span class="sd"> \\eta_{t,i} = \\frac{learningrate}{\\beta+\\sqrt{\\sum_{s=1}^tg_{s,i}^2}}</span> |
| |
| <span class="sd"> The optimizer updates the weight by::</span> |
| |
| <span class="sd"> rescaled_grad = clip(grad * rescale_grad, clip_gradient)</span> |
| <span class="sd"> z += rescaled_grad - (sqrt(n + rescaled_grad**2) - sqrt(n)) * weight / learning_rate</span> |
| <span class="sd"> n += rescaled_grad**2</span> |
| <span class="sd"> w = (sign(z) * lamda1 - z) / ((beta + sqrt(n)) / learning_rate + wd) * (abs(z) > lamda1)</span> |
| |
| <span class="sd"> If the storage types of weight, state and grad are all ``row_sparse``, \</span> |
| <span class="sd"> **sparse updates** are applied by::</span> |
| |
| <span class="sd"> for row in grad.indices:</span> |
| <span class="sd"> rescaled_grad[row] = clip(grad[row] * rescale_grad, clip_gradient)</span> |
| <span class="sd"> z[row] += rescaled_grad[row] - (sqrt(n[row] + rescaled_grad[row]**2) - sqrt(n[row])) * weight[row] / learning_rate</span> |
| <span class="sd"> n[row] += rescaled_grad[row]**2</span> |
| <span class="sd"> w[row] = (sign(z[row]) * lamda1 - z[row]) / ((beta + sqrt(n[row])) / learning_rate + wd) * (abs(z[row]) > lamda1)</span> |
| |
| <span class="sd"> The sparse update only updates the z and n for the weights whose row_sparse</span> |
| <span class="sd"> gradient indices appear in the current batch, rather than updating it for all</span> |
| <span class="sd"> indices. Compared with the original update, it can provide large</span> |
| <span class="sd"> improvements in model training throughput for some applications. However, it</span> |
| <span class="sd"> provides slightly different semantics than the original update, and</span> |
| <span class="sd"> may lead to different empirical results.</span> |
| |
| <span class="sd"> For details of the update algorithm, see :class:`~mxnet.ndarray.ftrl_update`.</span> |
| |
| <span class="sd"> This optimizer accepts the following parameters in addition to those accepted</span> |
| <span class="sd"> by :class:`.Optimizer`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> lamda1 : float, optional</span> |
| <span class="sd"> L1 regularization coefficient.</span> |
| <span class="sd"> learning_rate : float, optional</span> |
| <span class="sd"> The initial learning rate.</span> |
| <span class="sd"> beta : float, optional</span> |
| <span class="sd"> Per-coordinate learning rate correlation parameter.</span> |
| <span class="sd"> """</span> |
| |
| <div class="viewcode-block" id="Ftrl.__init__"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.Ftrl.html#mxnet.optimizer.Ftrl.__init__">[docs]</a> <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lamda1</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Ftrl</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">lamda1</span> <span class="o">=</span> <span class="n">lamda1</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">beta</span> <span class="o">=</span> <span class="n">beta</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">=</span> <span class="n">learning_rate</span></div> |
| |
| <span class="k">def</span> <span class="nf">create_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">zeros</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">stype</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">stype</span><span class="p">),</span> <span class="c1"># z</span> |
| <span class="n">zeros</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">stype</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">stype</span><span class="p">))</span> <span class="c1"># n</span> |
| |
| <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_update_count</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">wd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_wd</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">lr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_lr</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| |
| <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'lamda1'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">lamda1</span><span class="p">,</span> <span class="s1">'beta'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta</span><span class="p">,</span> <span class="s1">'rescale_grad'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">rescale_grad</span><span class="p">}</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span><span class="p">:</span> |
| <span class="n">kwargs</span><span class="p">[</span><span class="s1">'clip_gradient'</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span> |
| |
| <span class="c1"># accumulated g and delta initialization</span> |
| <span class="n">z</span><span class="p">,</span> <span class="n">n</span> <span class="o">=</span> <span class="n">state</span> |
| <span class="n">ftrl_update</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> |
| <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| <span class="c1"># pylint: enable=line-too-long</span> |
| <div class="viewcode-block" id="Adamax"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.Adamax.html#mxnet.optimizer.Adamax">[docs]</a><span class="nd">@register</span> |
| <span class="k">class</span> <span class="nc">Adamax</span><span class="p">(</span><span class="n">Optimizer</span><span class="p">):</span> |
| <span class="sd">"""The AdaMax optimizer.</span> |
| |
| <span class="sd"> It is a variant of Adam based on the infinity norm</span> |
| <span class="sd"> available at http://arxiv.org/abs/1412.6980 Section 7.</span> |
| |
| <span class="sd"> The optimizer updates the weight by::</span> |
| |
| <span class="sd"> grad = clip(grad * rescale_grad + wd * weight, clip_gradient)</span> |
| <span class="sd"> m = beta1 * m_t + (1 - beta1) * grad</span> |
| <span class="sd"> u = maximum(beta2 * u, abs(grad))</span> |
| <span class="sd"> weight -= lr / (1 - beta1**t) * m / u</span> |
| |
| <span class="sd"> This optimizer accepts the following parameters in addition to those accepted</span> |
| <span class="sd"> by :class:`.Optimizer`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> beta1 : float, optional</span> |
| <span class="sd"> Exponential decay rate for the first moment estimates.</span> |
| <span class="sd"> beta2 : float, optional</span> |
| <span class="sd"> Exponential decay rate for the second moment estimates.</span> |
| <span class="sd"> """</span> |
| <div class="viewcode-block" id="Adamax.__init__"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.Adamax.html#mxnet.optimizer.Adamax.__init__">[docs]</a> <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.002</span><span class="p">,</span> <span class="n">beta1</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">beta2</span><span class="o">=</span><span class="mf">0.999</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Adamax</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">learning_rate</span><span class="o">=</span><span class="n">learning_rate</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">beta1</span> <span class="o">=</span> <span class="n">beta1</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">beta2</span> <span class="o">=</span> <span class="n">beta2</span></div> |
| |
| <span class="k">def</span> <span class="nf">create_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">zeros</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span> <span class="c1"># mean</span> |
| <span class="n">zeros</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">dtype</span><span class="p">))</span> <span class="c1"># variance</span> |
| |
| <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_update_count</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">lr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_lr</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">wd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_wd</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| |
| <span class="n">t</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_index_update_count</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> |
| <span class="n">lr</span> <span class="o">/=</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta1</span><span class="o">**</span><span class="n">t</span><span class="p">)</span> |
| |
| <span class="c1"># preprocess grad</span> |
| <span class="n">grad</span> <span class="o">=</span> <span class="n">grad</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">rescale_grad</span> <span class="o">+</span> <span class="n">wd</span> <span class="o">*</span> <span class="n">weight</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">grad</span> <span class="o">=</span> <span class="n">clip</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span><span class="p">)</span> |
| |
| <span class="c1"># update m_t and u_t</span> |
| <span class="n">m_t</span><span class="p">,</span> <span class="n">u_t</span> <span class="o">=</span> <span class="n">state</span> |
| <span class="n">m_t</span><span class="p">[:]</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta1</span> |
| <span class="n">m_t</span><span class="p">[:]</span> <span class="o">+=</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta1</span><span class="p">)</span> <span class="o">*</span> <span class="n">grad</span> |
| <span class="n">u_t</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">maximum</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">beta2</span> <span class="o">*</span> <span class="n">u_t</span><span class="p">,</span> <span class="n">NDabs</span><span class="p">(</span><span class="n">grad</span><span class="p">))</span> |
| |
| <span class="c1"># update weight</span> |
| <span class="n">weight</span><span class="p">[:]</span> <span class="o">-=</span> <span class="n">lr</span> <span class="o">*</span> <span class="n">m_t</span> <span class="o">/</span> <span class="n">u_t</span></div> |
| |
| <div class="viewcode-block" id="Nadam"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.Nadam.html#mxnet.optimizer.Nadam">[docs]</a><span class="nd">@register</span> |
| <span class="k">class</span> <span class="nc">Nadam</span><span class="p">(</span><span class="n">Optimizer</span><span class="p">):</span> |
| <span class="sd">"""The Nesterov Adam optimizer.</span> |
| |
| <span class="sd"> Much like Adam is essentially RMSprop with momentum,</span> |
| <span class="sd"> Nadam is Adam RMSprop with Nesterov momentum available</span> |
| <span class="sd"> at http://cs229.stanford.edu/proj2015/054_report.pdf.</span> |
| |
| <span class="sd"> This optimizer accepts the following parameters in addition to those accepted</span> |
| <span class="sd"> by :class:`.Optimizer`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> beta1 : float, optional</span> |
| <span class="sd"> Exponential decay rate for the first moment estimates.</span> |
| <span class="sd"> beta2 : float, optional</span> |
| <span class="sd"> Exponential decay rate for the second moment estimates.</span> |
| <span class="sd"> epsilon : float, optional</span> |
| <span class="sd"> Small value to avoid division by 0.</span> |
| <span class="sd"> schedule_decay : float, optional</span> |
| <span class="sd"> Exponential decay rate for the momentum schedule</span> |
| <span class="sd"> """</span> |
| <div class="viewcode-block" id="Nadam.__init__"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.Nadam.html#mxnet.optimizer.Nadam.__init__">[docs]</a> <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">beta1</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">beta2</span><span class="o">=</span><span class="mf">0.999</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">,</span> |
| <span class="n">schedule_decay</span><span class="o">=</span><span class="mf">0.004</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Nadam</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">learning_rate</span><span class="o">=</span><span class="n">learning_rate</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">beta1</span> <span class="o">=</span> <span class="n">beta1</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">beta2</span> <span class="o">=</span> <span class="n">beta2</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span> <span class="o">=</span> <span class="n">epsilon</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">schedule_decay</span> <span class="o">=</span> <span class="n">schedule_decay</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">m_schedule</span> <span class="o">=</span> <span class="mf">1.</span></div> |
| |
| <span class="k">def</span> <span class="nf">create_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">zeros</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span> <span class="c1"># mean</span> |
| <span class="n">zeros</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">dtype</span><span class="p">))</span> <span class="c1"># variance</span> |
| |
| <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_update_count</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">lr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_lr</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">wd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_wd</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| |
| <span class="n">t</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_index_update_count</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> |
| |
| <span class="c1"># preprocess grad</span> |
| <span class="n">grad</span> <span class="o">=</span> <span class="n">grad</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">rescale_grad</span> <span class="o">+</span> <span class="n">wd</span> <span class="o">*</span> <span class="n">weight</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">grad</span> <span class="o">=</span> <span class="n">clip</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_gradient</span><span class="p">)</span> |
| |
| <span class="c1"># warming momentum schedule</span> |
| <span class="n">momentum_t</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta1</span> <span class="o">*</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="p">(</span><span class="nb">pow</span><span class="p">(</span><span class="mf">0.96</span><span class="p">,</span> <span class="n">t</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">schedule_decay</span><span class="p">)))</span> |
| <span class="n">momentum_t_1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta1</span> <span class="o">*</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="p">(</span><span class="nb">pow</span><span class="p">(</span><span class="mf">0.96</span><span class="p">,</span> <span class="p">(</span><span class="n">t</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">schedule_decay</span><span class="p">)))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">m_schedule</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_schedule</span> <span class="o">*</span> <span class="n">momentum_t</span> |
| <span class="n">m_schedule_next</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_schedule</span> <span class="o">*</span> <span class="n">momentum_t_1</span> |
| |
| <span class="c1"># update m_t and v_t</span> |
| <span class="n">m_t</span><span class="p">,</span> <span class="n">v_t</span> <span class="o">=</span> <span class="n">state</span> |
| <span class="n">m_t</span><span class="p">[:]</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta1</span> |
| <span class="n">m_t</span><span class="p">[:]</span> <span class="o">+=</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta1</span><span class="p">)</span> <span class="o">*</span> <span class="n">grad</span> |
| <span class="n">v_t</span><span class="p">[:]</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta2</span> |
| <span class="n">v_t</span><span class="p">[:]</span> <span class="o">+=</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta2</span><span class="p">)</span> <span class="o">*</span> <span class="n">grad</span> <span class="o">*</span> <span class="n">grad</span> |
| |
| <span class="n">grad_prime</span> <span class="o">=</span> <span class="n">grad</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_schedule</span><span class="p">)</span> |
| <span class="n">m_t_prime</span> <span class="o">=</span> <span class="n">m_t</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">m_schedule_next</span><span class="p">)</span> |
| <span class="n">v_t_prime</span> <span class="o">=</span> <span class="n">v_t</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="nb">pow</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">beta2</span><span class="p">,</span> <span class="n">t</span><span class="p">))</span> |
| <span class="n">m_t_bar</span> <span class="o">=</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">momentum_t</span><span class="p">)</span> <span class="o">*</span> <span class="n">grad_prime</span> <span class="o">+</span> <span class="n">momentum_t_1</span> <span class="o">*</span> <span class="n">m_t_prime</span> |
| |
| <span class="c1"># update weight</span> |
| <span class="n">weight</span><span class="p">[:]</span> <span class="o">-=</span> <span class="n">lr</span> <span class="o">*</span> <span class="n">m_t_bar</span> <span class="o">/</span> <span class="p">(</span><span class="n">sqrt</span><span class="p">(</span><span class="n">v_t_prime</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span><span class="p">)</span></div> |
| |
| <span class="nd">@register</span> |
| <span class="k">class</span> <span class="nc">Test</span><span class="p">(</span><span class="n">Optimizer</span><span class="p">):</span> |
| <span class="sd">"""The Test optimizer"""</span> |
| <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Test</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">create_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> |
| <span class="sd">"""Creates a state to duplicate weight."""</span> |
| <span class="k">return</span> <span class="n">zeros</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span> |
| <span class="sd">"""Performs w += rescale_grad * grad."""</span> |
| <span class="n">weight</span><span class="p">[:]</span> <span class="o">+=</span> <span class="n">grad</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">rescale_grad</span> |
| <span class="n">state</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">weight</span> |
| |
| <span class="c1"># backward compatibility wrapper for Optimizer.CreateOptimizer</span> |
| <span class="n">create</span> <span class="o">=</span> <span class="n">Optimizer</span><span class="o">.</span><span class="n">create_optimizer</span> <span class="c1"># pylint: disable=invalid-name</span> |
| |
| |
| <span class="k">def</span> <span class="nf">_as_classic</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">allow_np</span><span class="p">):</span> |
| <span class="c1"># TODO(junwu): This is a temp solution for allowing converting</span> |
| <span class="c1"># np.ndarray to mx.nd.NDArray to be fed into the optimizer since</span> |
| <span class="c1"># users may have custom optimizers implemented using mx.nd.NDArray ops.</span> |
| <span class="kn">from</span> <span class="nn">..numpy</span> <span class="k">import</span> <span class="n">ndarray</span> <span class="k">as</span> <span class="n">np_ndarray</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span> |
| <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np_ndarray</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">a</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">allow_np</span><span class="p">:</span> |
| <span class="k">return</span> <span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">as_nd_ndarray</span><span class="p">()</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">a</span><span class="p">]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Converting np.ndarray to mx.nd.NDArray is not allowed'</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">np_ndarray</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">allow_np</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">a</span><span class="o">.</span><span class="n">as_nd_ndarray</span><span class="p">()</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Converting np.ndarray to mx.nd.NDArray is not allowed'</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">a</span> |
| |
| |
| |
| <div class="viewcode-block" id="Updater"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.Updater.html#mxnet.optimizer.Updater">[docs]</a><span class="k">class</span> <span class="nc">Updater</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span> |
| <span class="sd">"""Updater for kvstore."""</span> |
| <div class="viewcode-block" id="Updater.__init__"><a class="viewcode-back" href="../../../api/gluon-related/_autogen/mxnet.optimizer.Updater.html#mxnet.optimizer.Updater.__init__">[docs]</a> <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">optimizer</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">states</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">states_synced</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">aggregate_updates</span> <span class="o">=</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">aggregate_num</span> <span class="o">></span> <span class="mi">0</span></div> |
| |
| <span class="k">def</span> <span class="nf">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span> |
| <span class="sd">"""Updates weight given gradient and index."""</span> |
| <span class="n">allow_np</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">allow_np_array</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)):</span> |
| <span class="n">indices</span> <span class="o">=</span> <span class="p">[</span><span class="n">index</span><span class="p">]</span> |
| <span class="n">grads</span> <span class="o">=</span> <span class="p">[</span><span class="n">_as_classic</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="n">allow_np</span><span class="p">)]</span> |
| <span class="n">weights</span> <span class="o">=</span> <span class="p">[</span><span class="n">_as_classic</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">allow_np</span><span class="p">)]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">indices</span> <span class="o">=</span> <span class="n">index</span> |
| <span class="n">grads</span> <span class="o">=</span> <span class="n">_as_classic</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="n">allow_np</span><span class="p">)</span> |
| <span class="n">weights</span> <span class="o">=</span> <span class="n">_as_classic</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">allow_np</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">weights</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">_set_current_context</span><span class="p">(</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">device_id</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">indices</span><span class="p">):</span> |
| <span class="c1"># convert ctypes.char_p.value back to python str if needed</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="nb">bytes</span><span class="p">):</span> |
| <span class="n">indices</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">py_str</span><span class="p">(</span><span class="n">idx</span><span class="p">)</span> |
| <span class="n">idx</span> <span class="o">=</span> <span class="n">indices</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">idx</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">states</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">states</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">create_state_multi_precision</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">states_synced</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="k">elif</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">states_synced</span><span class="p">[</span><span class="n">idx</span><span class="p">]:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">states</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">=</span> \ |
| <span class="bp">self</span><span class="o">.</span><span class="n">sync_state_context</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">states</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> <span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">context</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">states_synced</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">aggregate_updates</span><span class="p">:</span> |
| <span class="c1"># segregate values based on type</span> |
| <span class="n">type_map</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">g</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">w</span><span class="o">.</span><span class="n">dtype</span> <span class="ow">in</span> <span class="n">type_map</span><span class="p">:</span> |
| <span class="n">type_map</span><span class="p">[</span><span class="n">w</span><span class="o">.</span><span class="n">dtype</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">g</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">type_map</span><span class="p">[</span><span class="n">w</span><span class="o">.</span><span class="n">dtype</span><span class="p">]</span> <span class="o">=</span> <span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">g</span><span class="p">)]</span> |
| <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">type_map</span><span class="p">:</span> |
| <span class="n">current_index</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="n">indices</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">grads</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">type_map</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span> |
| <span class="k">while</span> <span class="n">current_index</span> <span class="o"><</span> <span class="nb">len</span><span class="p">(</span><span class="n">indices</span><span class="p">):</span> |
| <span class="n">states</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">step</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">aggregate_num</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">indices</span><span class="p">)</span> <span class="o">-</span> <span class="n">current_index</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">step</span><span class="p">):</span> |
| <span class="n">states</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">states</span><span class="p">[</span><span class="n">indices</span><span class="p">[</span><span class="n">current_index</span> <span class="o">+</span> <span class="n">j</span><span class="p">]])</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">update_multi_precision</span><span class="p">(</span> |
| <span class="n">indices</span><span class="p">[</span><span class="n">current_index</span><span class="p">:</span><span class="n">current_index</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">aggregate_num</span><span class="p">],</span> |
| <span class="n">weights</span><span class="p">[</span><span class="n">current_index</span><span class="p">:</span><span class="n">current_index</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">aggregate_num</span><span class="p">],</span> |
| <span class="n">grads</span><span class="p">[</span><span class="n">current_index</span><span class="p">:</span><span class="n">current_index</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">aggregate_num</span><span class="p">],</span> |
| <span class="n">states</span><span class="p">)</span> |
| <span class="n">current_index</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">aggregate_num</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">g</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">update_multi_precision</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">states</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> |
| |
| <span class="k">def</span> <span class="nf">sync_state_context</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state</span><span class="p">,</span> <span class="n">context</span><span class="p">):</span> |
| <span class="sd">"""sync state context."""</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">state</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">state</span><span class="o">.</span><span class="n">as_in_context</span><span class="p">(</span><span class="n">context</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">state</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span> |
| <span class="n">synced_state</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sync_state_context</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">context</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">state</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">state</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span> |
| <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">synced_state</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="n">synced_state</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">state</span> |
| |
| <span class="k">def</span> <span class="nf">set_states</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">states</span><span class="p">):</span> |
| <span class="sd">"""Sets updater states."""</span> |
| <span class="n">states</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">states</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">states</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">states</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">states</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">states</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">states</span> <span class="o">=</span> <span class="n">states</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">states_synced</span> <span class="o">=</span> <span class="nb">dict</span><span class="o">.</span><span class="n">fromkeys</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">states</span><span class="o">.</span><span class="n">keys</span><span class="p">(),</span> <span class="kc">False</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">get_states</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dump_optimizer</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="sd">"""Gets updater states.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> dump_optimizer : bool, default False</span> |
| <span class="sd"> Whether to also save the optimizer itself. This would also save optimizer</span> |
| <span class="sd"> information such as learning rate and weight decay schedules.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">pickle</span><span class="o">.</span><span class="n">dumps</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">states</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="p">)</span> <span class="k">if</span> <span class="n">dump_optimizer</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">states</span><span class="p">)</span></div> |
| |
| <span class="k">def</span> <span class="nf">get_updater</span><span class="p">(</span><span class="n">optimizer</span><span class="p">):</span> |
| <span class="sd">"""Returns a closure of the updater needed for kvstore.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> optimizer: Optimizer</span> |
| <span class="sd"> The optimizer.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> updater: function</span> |
| <span class="sd"> The closure of the updater.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">Updater</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> |
| </pre></div> |
| |
| </div> |
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