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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Python Tutorials</a><ul class="current"> |
| <li class="toctree-l2 current"><a class="reference internal" href="../index.html">Getting Started</a><ul class="current"> |
| <li class="toctree-l3 current"><a class="reference internal" href="index.html">Crash Course</a><ul class="current"> |
| <li class="toctree-l4"><a class="reference internal" href="0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4 current"><a class="current reference internal" href="#">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="5-datasets.html">Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="5-datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
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| <li class="toctree-l2"><a class="reference internal" href="../../packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
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| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
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| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
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| <li class="toctree-l2"><a class="reference internal" href="../../performance/index.html">Performance</a><ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../performance/backend/dnnl/dnnl_readme.html">Install MXNet with oneDNN</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../performance/backend/dnnl/dnnl_quantization_inc.html">Improving accuracy with Intel® Neural Compressor</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arange.html">mxnet.np.arange</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.array-manipulation.html">Array manipulation routines</a><ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ravel.html">mxnet.np.ravel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.flatten.html">mxnet.np.ndarray.flatten</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.swapaxes.html">mxnet.np.swapaxes</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.T.html">mxnet.np.ndarray.T</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.moveaxis.html">mxnet.np.moveaxis</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.squeeze.html">mxnet.np.squeeze</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.broadcast_arrays.html">mxnet.np.broadcast_arrays</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dstack.html">mxnet.np.dstack</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.split.html">mxnet.np.split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hsplit.html">mxnet.np.hsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vsplit.html">mxnet.np.vsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.array_split.html">mxnet.np.array_split</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.repeat.html">mxnet.np.repeat</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unique.html">mxnet.np.unique</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.delete.html">mxnet.np.delete</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.append.html">mxnet.np.append</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.resize.html">mxnet.np.resize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trim_zeros.html">mxnet.np.trim_zeros</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.roll.html">mxnet.np.roll</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rot90.html">mxnet.np.rot90</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fliplr.html">mxnet.np.fliplr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flipud.html">mxnet.np.flipud</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.io.html">Input and output</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.genfromtxt.html">mxnet.np.genfromtxt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.tolist.html">mxnet.np.ndarray.tolist</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.set_printoptions.html">mxnet.np.set_printoptions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dot.html">mxnet.np.dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vdot.html">mxnet.np.vdot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.inner.html">mxnet.np.inner</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.outer.html">mxnet.np.outer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tensordot.html">mxnet.np.tensordot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.einsum.html">mxnet.np.einsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.multi_dot.html">mxnet.np.linalg.multi_dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.matmul.html">mxnet.np.matmul</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_power.html">mxnet.np.linalg.matrix_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.kron.html">mxnet.np.kron</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.svd.html">mxnet.np.linalg.svd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cholesky.html">mxnet.np.linalg.cholesky</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.qr.html">mxnet.np.linalg.qr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eig.html">mxnet.np.linalg.eig</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigh.html">mxnet.np.linalg.eigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvals.html">mxnet.np.linalg.eigvals</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvalsh.html">mxnet.np.linalg.eigvalsh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.norm.html">mxnet.np.linalg.norm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trace.html">mxnet.np.trace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cond.html">mxnet.np.linalg.cond</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.det.html">mxnet.np.linalg.det</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_rank.html">mxnet.np.linalg.matrix_rank</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.slogdet.html">mxnet.np.linalg.slogdet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.solve.html">mxnet.np.linalg.solve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorsolve.html">mxnet.np.linalg.tensorsolve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.lstsq.html">mxnet.np.linalg.lstsq</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.inv.html">mxnet.np.linalg.inv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.pinv.html">mxnet.np.linalg.pinv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorinv.html">mxnet.np.linalg.tensorinv</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.math.html">Mathematical functions</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sin.html">mxnet.np.sin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cos.html">mxnet.np.cos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tan.html">mxnet.np.tan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsin.html">mxnet.np.arcsin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccos.html">mxnet.np.arccos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan.html">mxnet.np.arctan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.degrees.html">mxnet.np.degrees</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.radians.html">mxnet.np.radians</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hypot.html">mxnet.np.hypot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan2.html">mxnet.np.arctan2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.deg2rad.html">mxnet.np.deg2rad</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rad2deg.html">mxnet.np.rad2deg</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unwrap.html">mxnet.np.unwrap</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sinh.html">mxnet.np.sinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cosh.html">mxnet.np.cosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tanh.html">mxnet.np.tanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsinh.html">mxnet.np.arcsinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccosh.html">mxnet.np.arccosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctanh.html">mxnet.np.arctanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rint.html">mxnet.np.rint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fix.html">mxnet.np.fix</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.floor.html">mxnet.np.floor</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ceil.html">mxnet.np.ceil</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trunc.html">mxnet.np.trunc</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.around.html">mxnet.np.around</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.round_.html">mxnet.np.round_</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sum.html">mxnet.np.sum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.prod.html">mxnet.np.prod</a></li> |
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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <ul class="current"> |
| <li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Python Tutorials</a><ul class="current"> |
| <li class="toctree-l2 current"><a class="reference internal" href="../index.html">Getting Started</a><ul class="current"> |
| <li class="toctree-l3 current"><a class="reference internal" href="index.html">Crash Course</a><ul class="current"> |
| <li class="toctree-l4"><a class="reference internal" href="0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4 current"><a class="current reference internal" href="#">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="5-datasets.html">Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="5-datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
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| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
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| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../performance/backend/dnnl/dnnl_readme.html">Install MXNet with oneDNN</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../performance/backend/dnnl/dnnl_quantization_inc.html">Improving accuracy with Intel® Neural Compressor</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.array-manipulation.html">Array manipulation routines</a><ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.flatten.html">mxnet.np.ndarray.flatten</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hsplit.html">mxnet.np.hsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vsplit.html">mxnet.np.vsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.array_split.html">mxnet.np.array_split</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.repeat.html">mxnet.np.repeat</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.roll.html">mxnet.np.roll</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rot90.html">mxnet.np.rot90</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flipud.html">mxnet.np.flipud</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.io.html">Input and output</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.genfromtxt.html">mxnet.np.genfromtxt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.tolist.html">mxnet.np.ndarray.tolist</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.set_printoptions.html">mxnet.np.set_printoptions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dot.html">mxnet.np.dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vdot.html">mxnet.np.vdot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.inner.html">mxnet.np.inner</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.outer.html">mxnet.np.outer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tensordot.html">mxnet.np.tensordot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.einsum.html">mxnet.np.einsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.multi_dot.html">mxnet.np.linalg.multi_dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.matmul.html">mxnet.np.matmul</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_power.html">mxnet.np.linalg.matrix_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.kron.html">mxnet.np.kron</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.svd.html">mxnet.np.linalg.svd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cholesky.html">mxnet.np.linalg.cholesky</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.qr.html">mxnet.np.linalg.qr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eig.html">mxnet.np.linalg.eig</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigh.html">mxnet.np.linalg.eigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvals.html">mxnet.np.linalg.eigvals</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvalsh.html">mxnet.np.linalg.eigvalsh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.norm.html">mxnet.np.linalg.norm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trace.html">mxnet.np.trace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cond.html">mxnet.np.linalg.cond</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.det.html">mxnet.np.linalg.det</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_rank.html">mxnet.np.linalg.matrix_rank</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.slogdet.html">mxnet.np.linalg.slogdet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.solve.html">mxnet.np.linalg.solve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorsolve.html">mxnet.np.linalg.tensorsolve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.lstsq.html">mxnet.np.linalg.lstsq</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.inv.html">mxnet.np.linalg.inv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.pinv.html">mxnet.np.linalg.pinv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorinv.html">mxnet.np.linalg.tensorinv</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.math.html">Mathematical functions</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sin.html">mxnet.np.sin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cos.html">mxnet.np.cos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tan.html">mxnet.np.tan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsin.html">mxnet.np.arcsin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccos.html">mxnet.np.arccos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan.html">mxnet.np.arctan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.degrees.html">mxnet.np.degrees</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.radians.html">mxnet.np.radians</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hypot.html">mxnet.np.hypot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan2.html">mxnet.np.arctan2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.deg2rad.html">mxnet.np.deg2rad</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rad2deg.html">mxnet.np.rad2deg</a></li> |
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| <!--- Licensed to the Apache Software Foundation (ASF) under one --><!--- or more contributor license agreements. See the NOTICE file --><!--- distributed with this work for additional information --><!--- regarding copyright ownership. The ASF licenses this file --><!--- to you under the Apache License, Version 2.0 (the --><!--- "License"); you may not use this file except in compliance --><!--- with the License. You may obtain a copy of the License at --><!--- http://www.apache.org/licenses/LICENSE-2.0 --><!--- Unless required by applicable law or agreed to in writing, --><!--- software distributed under the License is distributed on an --><!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY --><!--- KIND, either express or implied. See the License for the --><!--- specific language governing permissions and limitations --><!--- under the License. --><div class="section" id="Step-2:-Create-a-neural-network"> |
| <h1>Step 2: Create a neural network<a class="headerlink" href="#Step-2:-Create-a-neural-network" title="Permalink to this headline">¶</a></h1> |
| <p>In this step, you learn how to use NP on Apache MXNet to create neural networks in Gluon. In addition to the <code class="docutils literal notranslate"><span class="pre">np</span></code> package that you learned about in the previous step <a class="reference internal" href="1-nparray.html"><span class="doc">Step 1: Manipulate data with NP on MXNet</span></a>, you also need to import the neural network modules from <code class="docutils literal notranslate"><span class="pre">gluon</span></code>. Gluon includes built-in neural network layers in the following two modules:</p> |
| <ol class="arabic simple"> |
| <li><p><code class="docutils literal notranslate"><span class="pre">mxnet.gluon.nn</span></code>: NN module that maintained by the mxnet team</p></li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">mxnet.gluon.contrib.nn</span></code>: Experiemental module that is contributed by the community</p></li> |
| </ol> |
| <p>Use the following commands to import the packages required for this step.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="kn">from</span> <span class="nn">mxnet</span> <span class="kn">import</span> <span class="n">np</span><span class="p">,</span> <span class="n">npx</span> |
| <span class="kn">from</span> <span class="nn">mxnet.gluon</span> <span class="kn">import</span> <span class="n">nn</span> |
| <span class="n">npx</span><span class="o">.</span><span class="n">set_np</span><span class="p">()</span> <span class="c1"># Change MXNet to the numpy-like mode.</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="Create-your-neural-network’s-first-layer"> |
| <h2>Create your neural network’s first layer<a class="headerlink" href="#Create-your-neural-network’s-first-layer" title="Permalink to this headline">¶</a></h2> |
| <p>In this section, you will create a simple neural network with Gluon. One of the simplest network you can create is a single <strong>Dense</strong> layer or <strong>densely- connected</strong> layer. A dense layer consists of nodes in the input that are connected to every node in the next layer. Use the following code example to start with a dense layer with five output units.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">layer</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span> |
| <span class="n">layer</span> |
| <span class="c1"># output: Dense(-1 -> 5, linear)</span> |
| </pre></div> |
| </div> |
| </div> |
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| <span></span>Dense(-1 -> 5, linear) |
| </pre></div> |
| </div> |
| </div> |
| <p>In the example above, the output is <code class="docutils literal notranslate"><span class="pre">Dense(-1</span> <span class="pre">-></span> <span class="pre">5,</span> <span class="pre">linear)</span></code>. The <strong>-1</strong> in the output denotes that the size of the input layer is not specified during initialization.</p> |
| <p>You can also call the <strong>Dense</strong> layer with an <code class="docutils literal notranslate"><span class="pre">in_units</span></code> parameter if you know the shape of your input unit.</p> |
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| <span></span><span class="n">layer</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="n">in_units</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> |
| <span class="n">layer</span> |
| </pre></div> |
| </div> |
| </div> |
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| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]: |
| </pre></div> |
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| <span></span>Dense(3 -> 5, linear) |
| </pre></div> |
| </div> |
| </div> |
| <p>In addition to the <code class="docutils literal notranslate"><span class="pre">in_units</span></code> param, you can also add an activation function to the layer using the <code class="docutils literal notranslate"><span class="pre">activation</span></code> param. The Dense layer implements the operation</p> |
| <div class="math notranslate nohighlight"> |
| \[output = \sigma(W \cdot X + b)\]</div> |
| <p>Call the Dense layer with an <code class="docutils literal notranslate"><span class="pre">activation</span></code> parameter to use an activation function.</p> |
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| <span></span><span class="n">layer</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="n">in_units</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span><span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>Voila! Congratulations on creating a simple neural network. But for most of your use cases, you will need to create a neural network with more than one dense layer or with multiple types of other layers. In addition to the <code class="docutils literal notranslate"><span class="pre">Dense</span></code> layer, you can find more layers at <a class="reference internal" href="../../../api/gluon/nn/index.html#module-mxnet.gluon.nn"><span class="std std-ref">mxnet nn layers</span></a></p> |
| <p>So now that you have created a neural network, you are probably wondering how to pass data into your network?</p> |
| <p>First, you need to initialize the network weights, if you use the default initialization method which draws random values uniformly in the range <span class="math notranslate nohighlight">\([-0.7, 0.7]\)</span>. You can see this in the following example.</p> |
| <p><strong>Note</strong>: Initialization is discussed at a little deeper detail in the next notebook</p> |
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| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">layer</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| </div> |
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| <div class="prompt empty docutils container"> |
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| <div class="highlight"><pre> |
| [03:51:58] /work/mxnet/src/storage/storage.cc:202: Using Pooled (Naive) StorageManager for CPU |
| </pre></div></div> |
| </div> |
| <p>Now that you have initialized your network, you can give it data. Passing data through a network is also called a forward pass. You can do a forward pass with random data, shown in the following example. First, you create a <code class="docutils literal notranslate"><span class="pre">(10,3)</span></code> shape random input <code class="docutils literal notranslate"><span class="pre">x</span></code> and feed the data into the layer to compute the output.</p> |
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| <span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,(</span><span class="mi">10</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span> |
| <span class="n">layer</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]: |
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| <div class="output_area highlight-none notranslate"><div class="highlight"><pre> |
| <span></span>array([[0.00881556, 0.01138476, 0. , 0. , 0.01936117], |
| [0. , 0.0035577 , 0.06854778, 0.03361227, 0. ], |
| [0.05338536, 0.01661206, 0. , 0.00864646, 0. ], |
| [0. , 0. , 0. , 0.03724934, 0.03653988], |
| [0. , 0.00657675, 0.00472842, 0. , 0.04593495], |
| [0. , 0.00795121, 0. , 0. , 0.0595542 ], |
| [0.02296758, 0.01650022, 0. , 0. , 0.03874438], |
| [0.02207369, 0.02308735, 0.04558432, 0.01468477, 0. ], |
| [0. , 0.02254252, 0. , 0. , 0.03834942], |
| [0.08092358, 0.04224379, 0. , 0. , 0. ]]) |
| </pre></div> |
| </div> |
| </div> |
| <p>The layer produces a <code class="docutils literal notranslate"><span class="pre">(10,5)</span></code> shape output from your <code class="docutils literal notranslate"><span class="pre">(10,3)</span></code> input.</p> |
| <p><strong>When you don’t specify the ``in_unit`` parameter, the system automatically infers it during the first time you feed in data during the first forward step after you create and initialize the weights.</strong></p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]: |
| </pre></div> |
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| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">layer</span><span class="o">.</span><span class="n">params</span> |
| </pre></div> |
| </div> |
| </div> |
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| <span></span>{'weight': Parameter (shape=(5, 3), dtype=float32), |
| 'bias': Parameter (shape=(5,), dtype=float32)} |
| </pre></div> |
| </div> |
| </div> |
| <p>The <code class="docutils literal notranslate"><span class="pre">weights</span></code> and <code class="docutils literal notranslate"><span class="pre">bias</span></code> can be accessed using the <code class="docutils literal notranslate"><span class="pre">.data()</span></code> method.</p> |
| <div class="nbinput docutils container"> |
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| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">layer</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| </div> |
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| <span></span>array([[ 0.01607367, 0.05928481, -0.0319057 ], |
| [-0.05814854, 0.01664302, -0.02215988], |
| [-0.04094896, 0.03231322, 0.05914024], |
| [ 0.05500493, 0.03504761, 0.05073748], |
| [ 0.00943237, -0.06525595, -0.04184696]]) |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="Chain-layers-into-a-neural-network-using-nn.Sequential"> |
| <h2>Chain layers into a neural network using nn.Sequential<a class="headerlink" href="#Chain-layers-into-a-neural-network-using-nn.Sequential" title="Permalink to this headline">¶</a></h2> |
| <p>Sequential provides a special way of rapidly building networks when when the network architecture follows a common design pattern: the layers look like a stack of pancakes. Many networks follow this pattern: a bunch of layers, one stacked on top of another, where the output of each layer is fed directly to the input to the next layer. To use sequential, simply provide a list of layers (pass in the layers by calling <code class="docutils literal notranslate"><span class="pre">net.add(<Layer</span> <span class="pre">goes</span> <span class="pre">here!></span></code>). To do this you can use your previous example of |
| Dense layers and create a 3-layer multi layer perceptron. You can create a sequential block using <code class="docutils literal notranslate"><span class="pre">nn.Sequential()</span></code> method and add layers using <code class="docutils literal notranslate"><span class="pre">add()</span></code> method.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]: |
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| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">net</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span> |
| |
| <span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="n">in_units</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span><span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">),</span> |
| <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">25</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">),</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span> |
| <span class="n">net</span> |
| </pre></div> |
| </div> |
| </div> |
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| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]: |
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| <div class="output_area highlight-none notranslate"><div class="highlight"><pre> |
| <span></span>Sequential( |
| (0): Dense(3 -> 5, Activation(relu)) |
| (1): Dense(-1 -> 25, Activation(relu)) |
| (2): Dense(-1 -> 2, linear) |
| ) |
| </pre></div> |
| </div> |
| </div> |
| <p>The layers are ordered exactly the way you defined your neural network with index starting from 0. You can access the layers by indexing the network using <code class="docutils literal notranslate"><span class="pre">[]</span></code>.</p> |
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| <span></span><span class="n">net</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| </pre></div> |
| </div> |
| </div> |
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| <span></span>Dense(-1 -> 25, Activation(relu)) |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="Create-a-custom-neural-network-architecture-flexibly"> |
| <h2>Create a custom neural network architecture flexibly<a class="headerlink" href="#Create-a-custom-neural-network-architecture-flexibly" title="Permalink to this headline">¶</a></h2> |
| <p><code class="docutils literal notranslate"><span class="pre">nn.Sequential()</span></code> allows you to create your multi-layer neural network with existing layers from <code class="docutils literal notranslate"><span class="pre">gluon.nn</span></code>. It also includes a pre-defined <code class="docutils literal notranslate"><span class="pre">forward()</span></code> function that sequentially executes added layers. But what if the built-in layers are not sufficient for your needs. If you want to create networks like ResNet which has complex but repeatable components, how do you create such a network?</p> |
| <p>In gluon, every neural network layer is defined by using a base class <code class="docutils literal notranslate"><span class="pre">nn.Block()</span></code>. A Block has one main job - define a forward method that takes some input x and generates an output. A Block can just do something simple like apply an activation function. It can combine multiple layers together in a single block or also combine a bunch of other Blocks together in creative ways to create complex networks like Resnet. In this case, you will construct three Dense layers. The <code class="docutils literal notranslate"><span class="pre">forward()</span></code> method |
| can then invoke the layers in turn to generate its output.</p> |
| <p>Create a subclass of <code class="docutils literal notranslate"><span class="pre">nn.Block</span></code> and implement two methods by using the following code.</p> |
| <ul class="simple"> |
| <li><p><code class="docutils literal notranslate"><span class="pre">__init__</span></code> create the layers</p></li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">forward</span></code> define the forward function.</p></li> |
| </ul> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="k">class</span> <span class="nc">Net</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Block</span><span class="p">):</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span> |
| <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">x</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nbinput docutils container"> |
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| </pre></div> |
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| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="k">class</span> <span class="nc">MLP</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Block</span><span class="p">):</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">dense1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">dense2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">25</span><span class="p">,</span><span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">dense3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
| <span class="n">layer1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">layer2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense2</span><span class="p">(</span><span class="n">layer1</span><span class="p">)</span> |
| <span class="n">layer3</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense3</span><span class="p">(</span><span class="n">layer2</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">layer3</span> |
| |
| <span class="n">net</span> <span class="o">=</span> <span class="n">MLP</span><span class="p">()</span> |
| <span class="n">net</span> |
| </pre></div> |
| </div> |
| </div> |
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| </pre></div> |
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| <div class="output_area highlight-none notranslate"><div class="highlight"><pre> |
| <span></span>MLP( |
| (dense1): Dense(-1 -> 5, Activation(relu)) |
| (dense2): Dense(-1 -> 25, Activation(relu)) |
| (dense3): Dense(-1 -> 2, linear) |
| ) |
| </pre></div> |
| </div> |
| </div> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">net</span><span class="o">.</span><span class="n">dense1</span><span class="o">.</span><span class="n">params</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]: |
| </pre></div> |
| </div> |
| <div class="output_area highlight-none notranslate"><div class="highlight"><pre> |
| <span></span>{'weight': Parameter (shape=(5, -1), dtype=float32), |
| 'bias': Parameter (shape=(5,), dtype=float32)} |
| </pre></div> |
| </div> |
| </div> |
| <p>Each layer includes parameters that are stored in a <code class="docutils literal notranslate"><span class="pre">Parameter</span></code> class. You can access them using the <code class="docutils literal notranslate"><span class="pre">params()</span></code> method.</p> |
| </div> |
| <div class="section" id="Creating-custom-layers-using-Parameters-(Blocks-API)"> |
| <h2>Creating custom layers using Parameters (Blocks API)<a class="headerlink" href="#Creating-custom-layers-using-Parameters-(Blocks-API)" title="Permalink to this headline">¶</a></h2> |
| <p>MXNet includes a <code class="docutils literal notranslate"><span class="pre">Parameter</span></code> method to hold your parameters in each layer. You can create custom layers using the <code class="docutils literal notranslate"><span class="pre">Parameter</span></code> class to include computation that may otherwise be not included in the built-in layers. For example, for a dense layer, the weights and biases will be created using the <code class="docutils literal notranslate"><span class="pre">Parameter</span></code> method. But if you want to add additional computation to the dense layer, you can create it using parameter method.</p> |
| <p>Instantiate a parameter, e.g weights with a size <code class="docutils literal notranslate"><span class="pre">(5,0)</span></code> using the <code class="docutils literal notranslate"><span class="pre">shape</span></code> argument.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="kn">from</span> <span class="nn">mxnet.gluon</span> <span class="kn">import</span> <span class="n">Parameter</span> |
| |
| <span class="n">weight</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s2">"custom_parameter_weight"</span><span class="p">,</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span> |
| <span class="n">bias</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s2">"custom_parameter_bias"</span><span class="p">,</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span> |
| |
| <span class="n">weight</span><span class="p">,</span><span class="n">bias</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]: |
| </pre></div> |
| </div> |
| <div class="output_area highlight-none notranslate"><div class="highlight"><pre> |
| <span></span>(Parameter (shape=(5, -1), dtype=<class 'numpy.float32'>), |
| Parameter (shape=(5, -1), dtype=<class 'numpy.float32'>)) |
| </pre></div> |
| </div> |
| </div> |
| <p>The <code class="docutils literal notranslate"><span class="pre">Parameter</span></code> method includes a <code class="docutils literal notranslate"><span class="pre">grad_req</span></code> argument that specifies how you want to capture gradients for this Parameter. Under the hood, that lets gluon know that it has to call <code class="docutils literal notranslate"><span class="pre">.attach_grad()</span></code> on the underlying array. By default, the gradient is updated everytime the gradient is written to the grad <code class="docutils literal notranslate"><span class="pre">grad_req='write'</span></code>.</p> |
| <p>Now that you know how parameters work, you are ready to create your very own fully-connected custom layer.</p> |
| <p>To create the custom layers using parameters, you can use the same skeleton with <code class="docutils literal notranslate"><span class="pre">nn.Block</span></code> base class. You will create a custom dense layer that takes parameter x and returns computed <code class="docutils literal notranslate"><span class="pre">w*x</span> <span class="pre">+</span> <span class="pre">b</span></code> without any activation function</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="k">class</span> <span class="nc">custom_layer</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Block</span><span class="p">):</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out_units</span><span class="p">,</span> <span class="n">in_units</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">in_units</span><span class="p">,</span><span class="n">out_units</span><span class="p">),</span> <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s2">"bias"</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">out_units</span><span class="p">,),</span> <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">())</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>Parameter can be instantiated before the corresponding data is instantiated. For example, when you instantiate a Block but the shapes of each parameter still need to be inferred, the Parameter will wait for the shape to be inferred before allocating memory.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">dense</span> <span class="o">=</span> <span class="n">custom_layer</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="n">in_units</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span> |
| <span class="n">dense</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span> |
| <span class="n">dense</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)))</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]: |
| </pre></div> |
| </div> |
| <div class="output_area highlight-none notranslate"><div class="highlight"><pre> |
| <span></span>array([[-0.05604633, -0.06238654, 0.02687173], |
| [-0.02687152, -0.04365591, -0.00518382], |
| [-0.02849396, -0.09980228, -0.00695815], |
| [-0.04527343, -0.00275569, -0.01376584]]) |
| </pre></div> |
| </div> |
| </div> |
| <p>Similarly, you can use the following code to implement a famous network called <a class="reference external" href="http://yann.lecun.com/exdb/lenet/">LeNet</a> through <code class="docutils literal notranslate"><span class="pre">nn.Block</span></code> using the built-in <code class="docutils literal notranslate"><span class="pre">Dense</span></code> layer and using <code class="docutils literal notranslate"><span class="pre">custom_layer</span></code> as the last layer</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="k">class</span> <span class="nc">LeNet</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Block</span><span class="p">):</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">channels</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">pool1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">strides</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">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">channels</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">pool2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">strides</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">dense1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">120</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">"relu"</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">dense2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">84</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">"relu"</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">dense3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">x</span> |
| |
| <span class="n">lenet</span> <span class="o">=</span> <span class="n">LeNet</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="k">class</span> <span class="nc">LeNet_custom</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Block</span><span class="p">):</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">channels</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">pool1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">strides</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">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">channels</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">pool2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">strides</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">dense1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">120</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">"relu"</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">dense2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">84</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">"relu"</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">dense3</span> <span class="o">=</span> <span class="n">custom_layer</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">84</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">x</span> |
| |
| <span class="n">lenet_custom</span> <span class="o">=</span> <span class="n">LeNet_custom</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">image_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">28</span><span class="p">,</span><span class="mi">28</span><span class="p">))</span> |
| |
| <span class="n">lenet</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span> |
| <span class="n">lenet_custom</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"Lenet:"</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="n">lenet</span><span class="p">(</span><span class="n">image_data</span><span class="p">))</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"Custom Lenet:"</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="n">lenet_custom</span><span class="p">(</span><span class="n">image_data</span><span class="p">))</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area docutils container"> |
| <div class="highlight"><pre> |
| Lenet: |
| [[-0.00081668 -0.00340701 0.00199039 -0.00121501 -0.00063157 0.00081476 |
| -0.0011025 -0.00216652 -0.00015363 -0.00110007]] |
| Custom Lenet: |
| [[-0.02656163 0.04184292 -0.00254037 -0.06494093 -0.00952166 -0.01921579 |
| 0.05423243 -0.02774546 0.06823301 0.00313227]] |
| </pre></div></div> |
| </div> |
| <p>You can use <code class="docutils literal notranslate"><span class="pre">.data</span></code> method to access the weights and bias of a particular layer. For example, the following accesses the first layer’s weight and sixth layer’s bias.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">lenet</span><span class="o">.</span><span class="n">conv1</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">lenet</span><span class="o">.</span><span class="n">dense1</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]: |
| </pre></div> |
| </div> |
| <div class="output_area highlight-none notranslate"><div class="highlight"><pre> |
| <span></span>((6, 1, 3, 3), (120,)) |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="Using-predefined-(pretrained)-architectures"> |
| <h2>Using predefined (pretrained) architectures<a class="headerlink" href="#Using-predefined-(pretrained)-architectures" title="Permalink to this headline">¶</a></h2> |
| <p>Till now, you have seen how to create your own neural network architectures. But what if you want to replicate or baseline your dataset using some of the common models in computer visions or natural language processing (NLP). Gluon includes common architectures that you can directly use. The Gluon Model Zoo provides a collection of off-the-shelf models e.g. RESNET, BERT etc. These architectures are found at:</p> |
| <ul class="simple"> |
| <li><p><a class="reference external" href="https://cv.gluon.ai/model_zoo/index.html">Gluon CV model zoo</a></p></li> |
| <li><p><a class="reference external" href="https://nlp.gluon.ai/model_zoo/index.html">Gluon NLP model zoo</a></p></li> |
| </ul> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="kn">from</span> <span class="nn">mxnet.gluon</span> <span class="kn">import</span> <span class="n">model_zoo</span> |
| |
| <span class="n">net</span> <span class="o">=</span> <span class="n">model_zoo</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">resnet50_v2</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">net</span><span class="o">.</span><span class="n">hybridize</span><span class="p">()</span> |
| |
| <span class="n">dummy_input</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">224</span><span class="p">,</span><span class="mi">224</span><span class="p">))</span> |
| <span class="n">output</span> <span class="o">=</span> <span class="n">net</span><span class="p">(</span><span class="n">dummy_input</span><span class="p">)</span> |
| <span class="n">output</span><span class="o">.</span><span class="n">shape</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area docutils container"> |
| <div class="highlight"><pre> |
| Downloading /home/jenkins_slave/.mxnet/models/resnet50_v2-ecdde353.zip00383814-e655-4621-a110-5ffefe3eb69c from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet50_v2-ecdde353.zip... |
| </pre></div></div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]: |
| </pre></div> |
| </div> |
| <div class="output_area highlight-none notranslate"><div class="highlight"><pre> |
| <span></span>(1, 1000) |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="Deciding-the-paradigm-for-your-network"> |
| <h2>Deciding the paradigm for your network<a class="headerlink" href="#Deciding-the-paradigm-for-your-network" title="Permalink to this headline">¶</a></h2> |
| <p>In MXNet, Gluon API (Imperative programming paradigm) provides a user friendly way for quick prototyping, easy debugging and natural control flow for people familiar with python programming.</p> |
| <p>However, at the backend, MXNET can also convert the network using Symbolic or Declarative programming into static graphs with low level optimizations on operators. However, static graphs are less flexible because any logic must be encoded into the graph as special operators like scan, while_loop and cond. It’s also hard to debug.</p> |
| <p>So how can you make use of symbolic programming while getting the flexibility of imperative programming to quickly prototype and debug?</p> |
| <p>Enter <strong>HybridBlock</strong></p> |
| <p>HybridBlocks can run in a fully imperatively way where you define their computation with real functions acting on real inputs. But they’re also capable of running symbolically, acting on placeholders. Gluon hides most of this under the hood so you will only need to know how it works when you want to write your own layers.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">net_hybrid_seq</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">HybridSequential</span><span class="p">()</span> |
| |
| <span class="n">net_hybrid_seq</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="n">in_units</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span><span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">),</span> |
| <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">25</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">),</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="p">)</span> |
| <span class="n">net_hybrid_seq</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]: |
| </pre></div> |
| </div> |
| <div class="output_area highlight-none notranslate"><div class="highlight"><pre> |
| <span></span>HybridSequential( |
| (0): Dense(3 -> 5, Activation(relu)) |
| (1): Dense(-1 -> 25, Activation(relu)) |
| (2): Dense(-1 -> 2, linear) |
| ) |
| </pre></div> |
| </div> |
| </div> |
| <p>To compile and optimize <code class="docutils literal notranslate"><span class="pre">HybridSequential</span></code>, you can call its <code class="docutils literal notranslate"><span class="pre">hybridize</span></code> method.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[23]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">net_hybrid_seq</span><span class="o">.</span><span class="n">hybridize</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="Creating-custom-layers-using-Parameters-(HybridBlocks-API)"> |
| <h2>Creating custom layers using Parameters (HybridBlocks API)<a class="headerlink" href="#Creating-custom-layers-using-Parameters-(HybridBlocks-API)" title="Permalink to this headline">¶</a></h2> |
| <p>When you instantiated your custom layer, you specified the input dimension <code class="docutils literal notranslate"><span class="pre">in_units</span></code> that initializes the weights with the shape specified by <code class="docutils literal notranslate"><span class="pre">in_units</span></code> and <code class="docutils literal notranslate"><span class="pre">out_units</span></code>. If you leave the shape of <code class="docutils literal notranslate"><span class="pre">in_unit</span></code> as unknown, you defer the shape to the first forward pass. For the custom layer, you define the <code class="docutils literal notranslate"><span class="pre">infer_shape()</span></code> method and let the shape be inferred at runtime.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[24]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="k">class</span> <span class="nc">CustomLayer</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out_units</span><span class="p">,</span> <span class="n">in_units</span><span class="o">=-</span><span class="mi">1</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s2">"weight"</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">in_units</span><span class="p">,</span> <span class="n">out_units</span><span class="p">),</span> <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s2">"bias"</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">out_units</span><span class="p">,),</span> <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
| <span class="nb">print</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">())</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="nf">infer_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
| <span class="nb">print</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> |
| <span class="n">dense</span> <span class="o">=</span> <span class="n">CustomLayer</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> |
| |
| <span class="n">dense</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span> |
| <span class="n">dense</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)))</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area stderr docutils container"> |
| <div class="highlight"><pre> |
| /work/mxnet/python/mxnet/util.py:755: UserWarning: Parameter 'weight' is already initialized, ignoring. Set force_reinit=True to re-initialize. |
| return func(*args, **kwargs) |
| /work/mxnet/python/mxnet/util.py:755: UserWarning: Parameter 'bias' is already initialized, ignoring. Set force_reinit=True to re-initialize. |
| return func(*args, **kwargs) |
| </pre></div></div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[24]: |
| </pre></div> |
| </div> |
| <div class="output_area highlight-none notranslate"><div class="highlight"><pre> |
| <span></span>array([[-0.07053316, -0.07457963, 0.01166525], |
| [-0.04170407, -0.07482161, 0.00179428], |
| [-0.07503258, 0.00660181, -0.01401043], |
| [-0.02333996, -0.06775613, 0.01459978]]) |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="Performance"> |
| <h3>Performance<a class="headerlink" href="#Performance" title="Permalink to this headline">¶</a></h3> |
| <p>To get a sense of the speedup from hybridizing, you can compare the performance before and after hybridizing by measuring the time it takes to make 1000 forward passes through the network.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[25]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">time</span> |
| |
| <span class="k">def</span> <span class="nf">benchmark</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
| <span class="n">y</span> <span class="o">=</span> <span class="n">net</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1000</span><span class="p">):</span> |
| <span class="n">y</span> <span class="o">=</span> <span class="n">net</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span> |
| |
| <span class="n">x_bench</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">512</span><span class="p">))</span> |
| |
| <span class="n">net_hybrid_seq</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">HybridSequential</span><span class="p">()</span> |
| |
| <span class="n">net_hybrid_seq</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span><span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">),</span> |
| <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">),</span> |
| <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span> |
| <span class="n">net_hybrid_seq</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Before hybridizing: </span><span class="si">%.4f</span><span class="s1"> sec'</span><span class="o">%</span><span class="p">(</span><span class="n">benchmark</span><span class="p">(</span><span class="n">net_hybrid_seq</span><span class="p">,</span> <span class="n">x_bench</span><span class="p">)))</span> |
| <span class="n">net_hybrid_seq</span><span class="o">.</span><span class="n">hybridize</span><span class="p">()</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'After hybridizing: </span><span class="si">%.4f</span><span class="s1"> sec'</span><span class="o">%</span><span class="p">(</span><span class="n">benchmark</span><span class="p">(</span><span class="n">net_hybrid_seq</span><span class="p">,</span> <span class="n">x_bench</span><span class="p">)))</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area docutils container"> |
| <div class="highlight"><pre> |
| Before hybridizing: 0.6034 sec |
| After hybridizing: 0.2799 sec |
| </pre></div></div> |
| </div> |
| <p>Peeling back another layer, you also have a <code class="docutils literal notranslate"><span class="pre">HybridBlock</span></code> which is the hybrid version of the <code class="docutils literal notranslate"><span class="pre">Block</span></code> API.</p> |
| <p>Similar to the <code class="docutils literal notranslate"><span class="pre">Blocks</span></code> API, you define a <code class="docutils literal notranslate"><span class="pre">forward</span></code> function for <code class="docutils literal notranslate"><span class="pre">HybridBlock</span></code> that takes an input <code class="docutils literal notranslate"><span class="pre">x</span></code>. MXNet takes care of hybridizing the model at the backend so you don’t have to make changes to your code to convert it to a symbolic paradigm.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[26]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="kn">from</span> <span class="nn">mxnet.gluon</span> <span class="kn">import</span> <span class="n">HybridBlock</span> |
| |
| <span class="k">class</span> <span class="nc">MLP_Hybrid</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">dense1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span><span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">dense2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span><span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">dense3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
| <span class="n">layer1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">layer2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense2</span><span class="p">(</span><span class="n">layer1</span><span class="p">)</span> |
| <span class="n">layer3</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense3</span><span class="p">(</span><span class="n">layer2</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">layer3</span> |
| |
| <span class="n">net_hybrid</span> <span class="o">=</span> <span class="n">MLP_Hybrid</span><span class="p">()</span> |
| <span class="n">net_hybrid</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Before hybridizing: </span><span class="si">%.4f</span><span class="s1"> sec'</span><span class="o">%</span><span class="p">(</span><span class="n">benchmark</span><span class="p">(</span><span class="n">net_hybrid</span><span class="p">,</span> <span class="n">x_bench</span><span class="p">)))</span> |
| <span class="n">net_hybrid</span><span class="o">.</span><span class="n">hybridize</span><span class="p">()</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'After hybridizing: </span><span class="si">%.4f</span><span class="s1"> sec'</span><span class="o">%</span><span class="p">(</span><span class="n">benchmark</span><span class="p">(</span><span class="n">net_hybrid</span><span class="p">,</span> <span class="n">x_bench</span><span class="p">)))</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area docutils container"> |
| <div class="highlight"><pre> |
| Before hybridizing: 0.5799 sec |
| After hybridizing: 0.2603 sec |
| </pre></div></div> |
| </div> |
| <p>Given a HybridBlock whose forward computation consists of going through other HybridBlocks, you can compile that section of the network by calling the HybridBlocks <code class="docutils literal notranslate"><span class="pre">.hybridize()</span></code> method.</p> |
| <p>All of MXNet’s predefined layers are HybridBlocks. This means that any network consisting entirely of predefined MXNet layers can be compiled and run at much faster speeds by calling <code class="docutils literal notranslate"><span class="pre">.hybridize()</span></code>.</p> |
| </div> |
| </div> |
| <div class="section" id="Saving-and-Loading-your-models"> |
| <h2>Saving and Loading your models<a class="headerlink" href="#Saving-and-Loading-your-models" title="Permalink to this headline">¶</a></h2> |
| <p>The Blocks API also includes saving your models during and after training so that you can host the model for inference or avoid training the model again from scratch. Another reason would be to train your model using one language (like Python that has a lot of tools for training) and run inference using a different language.</p> |
| <p>There are two ways to save your model in MXNet. 1. Save/load the model weights/parameters only 2. Save/load the model weights/parameters and the architectures</p> |
| <div class="section" id="1.-Save/load-the-model-weights/parameters-only"> |
| <h3>1. Save/load the model weights/parameters only<a class="headerlink" href="#1.-Save/load-the-model-weights/parameters-only" title="Permalink to this headline">¶</a></h3> |
| <p>You can use <code class="docutils literal notranslate"><span class="pre">save_parameters</span></code> and <code class="docutils literal notranslate"><span class="pre">load_parameters</span></code> method to save and load the model weights. Take your simplest model <code class="docutils literal notranslate"><span class="pre">layer</span></code> and save your parameters first. The model parameters are the params that you save <strong>after</strong> you train your model.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[27]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">file_name</span> <span class="o">=</span> <span class="s1">'layer.params'</span> |
| <span class="n">layer</span><span class="o">.</span><span class="n">save_parameters</span><span class="p">(</span><span class="n">file_name</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>And now load this model again. To load the parameters into a model, you will first have to build the model. To do this, you will need to create a simple function to build it.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[28]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="k">def</span> <span class="nf">build_model</span><span class="p">():</span> |
| <span class="n">layer</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="n">in_units</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span><span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">layer</span> |
| |
| <span class="n">layer_new</span> <span class="o">=</span> <span class="n">build_model</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[29]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">layer_new</span><span class="o">.</span><span class="n">load_parameters</span><span class="p">(</span><span class="s1">'layer.params'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <p><strong>Note</strong>: The <code class="docutils literal notranslate"><span class="pre">save_parameters</span></code> and <code class="docutils literal notranslate"><span class="pre">load_parameters</span></code> method is used for models that use a <code class="docutils literal notranslate"><span class="pre">Block</span></code> method instead of <code class="docutils literal notranslate"><span class="pre">HybridBlock</span></code> method to build the model. These models may have complex architectures where the model architectures may change during execution. E.g. if you have a model that uses an if-else conditional statement to choose between two different architectures.</p> |
| </div> |
| <div class="section" id="2.-Save/load-the-model-weights/parameters-and-the-architectures"> |
| <h3>2. Save/load the model weights/parameters and the architectures<a class="headerlink" href="#2.-Save/load-the-model-weights/parameters-and-the-architectures" title="Permalink to this headline">¶</a></h3> |
| <p>For models that use the <strong>HybridBlock</strong>, the model architecture stays static and do no change during execution. Therefore both model parameters <strong>AND</strong> architecture can be saved and loaded using <code class="docutils literal notranslate"><span class="pre">export</span></code>, <code class="docutils literal notranslate"><span class="pre">imports</span></code> methods.</p> |
| <p>Now look at your <code class="docutils literal notranslate"><span class="pre">MLP_Hybrid</span></code> model and export the model using the <code class="docutils literal notranslate"><span class="pre">export</span></code> function. The export function will export the model architecture into a <code class="docutils literal notranslate"><span class="pre">.json</span></code> file and model parameters into a <code class="docutils literal notranslate"><span class="pre">.params</span></code> file.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[30]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">net_hybrid</span><span class="o">.</span><span class="n">export</span><span class="p">(</span><span class="s1">'MLP_hybrid'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[30]: |
| </pre></div> |
| </div> |
| <div class="output_area highlight-none notranslate"><div class="highlight"><pre> |
| <span></span>('MLP_hybrid-symbol.json', 'MLP_hybrid-0000.params') |
| </pre></div> |
| </div> |
| </div> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[31]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">net_hybrid</span><span class="o">.</span><span class="n">export</span><span class="p">(</span><span class="s1">'MLP_hybrid'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[31]: |
| </pre></div> |
| </div> |
| <div class="output_area highlight-none notranslate"><div class="highlight"><pre> |
| <span></span>('MLP_hybrid-symbol.json', 'MLP_hybrid-0000.params') |
| </pre></div> |
| </div> |
| </div> |
| <p>Similarly, to load this model back, you can use <code class="docutils literal notranslate"><span class="pre">gluon.nn.SymbolBlock</span></code>. To demonstrate that, load the network serialized above.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[32]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="kn">import</span> <span class="nn">warnings</span> |
| <span class="k">with</span> <span class="n">warnings</span><span class="o">.</span><span class="n">catch_warnings</span><span class="p">():</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span><span class="p">(</span><span class="s2">"ignore"</span><span class="p">)</span> |
| <span class="n">net_loaded</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">SymbolBlock</span><span class="o">.</span><span class="n">imports</span><span class="p">(</span><span class="s2">"MLP_hybrid-symbol.json"</span><span class="p">,</span> |
| <span class="p">[</span><span class="s1">'data'</span><span class="p">],</span> <span class="s2">"MLP_hybrid-0000.params"</span><span class="p">,</span> |
| <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[33]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">net_loaded</span><span class="p">(</span><span class="n">x_bench</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[33]: |
| </pre></div> |
| </div> |
| <div class="output_area highlight-none notranslate"><div class="highlight"><pre> |
| <span></span>array([[ 0.13653663, -0.07247495]]) |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="Visualizing-your-models"> |
| <h2>Visualizing your models<a class="headerlink" href="#Visualizing-your-models" title="Permalink to this headline">¶</a></h2> |
| <p>In MXNet, the <code class="docutils literal notranslate"><span class="pre">Block.Summary()</span></code> method allows you to view the block’s shape arguments and view the block’s parameters. When you combine multiple blocks into a model, the <code class="docutils literal notranslate"><span class="pre">summary()</span></code> applied on the model allows you to view each block’s summary, the total parameters, and the order of the blocks within the model. To do this the <code class="docutils literal notranslate"><span class="pre">Block.summary()</span></code> method requires one forward pass of the data, through your network, in order to create the graph necessary for capturing the corresponding shapes and |
| parameters. Additionally, this method should be called before the hybridize method, since the hybridize method converts the graph into a symbolic one, potentially changing the operations for optimal computation.</p> |
| <p>Look at the following examples</p> |
| <ul class="simple"> |
| <li><p>layer: our single layer network</p></li> |
| <li><p>Lenet: a non-hybridized LeNet network</p></li> |
| <li><p>net_Hybrid: our MLP Hybrid network</p></li> |
| </ul> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[34]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">layer</span><span class="o">.</span><span class="n">summary</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area docutils container"> |
| <div class="highlight"><pre> |
| -------------------------------------------------------------------------------- |
| Layer (type) Output Shape Param # |
| ================================================================================ |
| Input (10, 3) 0 |
| Activation-1 (10, 5) 0 |
| Dense-2 (10, 5) 20 |
| ================================================================================ |
| Parameters in forward computation graph, duplicate included |
| Total params: 20 |
| Trainable params: 20 |
| Non-trainable params: 0 |
| Shared params in forward computation graph: 0 |
| Unique parameters in model: 20 |
| -------------------------------------------------------------------------------- |
| </pre></div></div> |
| </div> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[35]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">lenet</span><span class="o">.</span><span class="n">summary</span><span class="p">(</span><span class="n">image_data</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area docutils container"> |
| <div class="highlight"><pre> |
| -------------------------------------------------------------------------------- |
| Layer (type) Output Shape Param # |
| ================================================================================ |
| Input (1, 1, 28, 28) 0 |
| Activation-1 (1, 6, 26, 26) 0 |
| Conv2D-2 (1, 6, 26, 26) 60 |
| MaxPool2D-3 (1, 6, 13, 13) 0 |
| Activation-4 (1, 16, 11, 11) 0 |
| Conv2D-5 (1, 16, 11, 11) 880 |
| MaxPool2D-6 (1, 16, 5, 5) 0 |
| Activation-7 (1, 120) 0 |
| Dense-8 (1, 120) 48120 |
| Activation-9 (1, 84) 0 |
| Dense-10 (1, 84) 10164 |
| Dense-11 (1, 10) 850 |
| LeNet-12 (1, 10) 0 |
| ================================================================================ |
| Parameters in forward computation graph, duplicate included |
| Total params: 60074 |
| Trainable params: 60074 |
| Non-trainable params: 0 |
| Shared params in forward computation graph: 0 |
| Unique parameters in model: 60074 |
| -------------------------------------------------------------------------------- |
| </pre></div></div> |
| </div> |
| <p>You are able to print the summaries of the two networks <code class="docutils literal notranslate"><span class="pre">layer</span></code> and <code class="docutils literal notranslate"><span class="pre">lenet</span></code> easily since you didn’t hybridize the two networks. However, the last network <code class="docutils literal notranslate"><span class="pre">net_Hybrid</span></code> was hybridized above and throws an <code class="docutils literal notranslate"><span class="pre">AssertionError</span></code> if you try <code class="docutils literal notranslate"><span class="pre">net_Hybrid.summary(x_bench)</span></code>. To print the summary for <code class="docutils literal notranslate"><span class="pre">net_Hybrid</span></code>, call another instance of the same network and instantiate it for our summary and then hybridize it</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[36]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">net_hybrid_summary</span> <span class="o">=</span> <span class="n">MLP_Hybrid</span><span class="p">()</span> |
| |
| <span class="n">net_hybrid_summary</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span> |
| |
| <span class="n">net_hybrid_summary</span><span class="o">.</span><span class="n">summary</span><span class="p">(</span><span class="n">x_bench</span><span class="p">)</span> |
| |
| <span class="n">net_hybrid_summary</span><span class="o">.</span><span class="n">hybridize</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area docutils container"> |
| <div class="highlight"><pre> |
| -------------------------------------------------------------------------------- |
| Layer (type) Output Shape Param # |
| ================================================================================ |
| Input (1, 512) 0 |
| Activation-1 (1, 256) 0 |
| Dense-2 (1, 256) 131328 |
| Activation-3 (1, 128) 0 |
| Dense-4 (1, 128) 32896 |
| Dense-5 (1, 2) 258 |
| MLP_Hybrid-6 (1, 2) 0 |
| ================================================================================ |
| Parameters in forward computation graph, duplicate included |
| Total params: 164482 |
| Trainable params: 164482 |
| Non-trainable params: 0 |
| Shared params in forward computation graph: 0 |
| Unique parameters in model: 164482 |
| -------------------------------------------------------------------------------- |
| </pre></div></div> |
| </div> |
| </div> |
| <div class="section" id="Next-steps:"> |
| <h2>Next steps:<a class="headerlink" href="#Next-steps:" title="Permalink to this headline">¶</a></h2> |
| <p>Now that you have created a neural network, learn how to automatically compute the gradients in <a class="reference internal" href="3-autograd.html"><span class="doc">Step 3: Automatic differentiation with autograd</span></a>.</p> |
| </div> |
| </div> |
| |
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| <p class="caption"> |
| <span class="caption-text">Table Of Contents</span> |
| </p> |
| <ul> |
| <li><a class="reference internal" href="#">Step 2: Create a neural network</a><ul> |
| <li><a class="reference internal" href="#Create-your-neural-network’s-first-layer">Create your neural network’s first layer</a></li> |
| <li><a class="reference internal" href="#Chain-layers-into-a-neural-network-using-nn.Sequential">Chain layers into a neural network using nn.Sequential</a></li> |
| <li><a class="reference internal" href="#Create-a-custom-neural-network-architecture-flexibly">Create a custom neural network architecture flexibly</a></li> |
| <li><a class="reference internal" href="#Creating-custom-layers-using-Parameters-(Blocks-API)">Creating custom layers using Parameters (Blocks API)</a></li> |
| <li><a class="reference internal" href="#Using-predefined-(pretrained)-architectures">Using predefined (pretrained) architectures</a></li> |
| <li><a class="reference internal" href="#Deciding-the-paradigm-for-your-network">Deciding the paradigm for your network</a></li> |
| <li><a class="reference internal" href="#Creating-custom-layers-using-Parameters-(HybridBlocks-API)">Creating custom layers using Parameters (HybridBlocks API)</a><ul> |
| <li><a class="reference internal" href="#Performance">Performance</a></li> |
| </ul> |
| </li> |
| <li><a class="reference internal" href="#Saving-and-Loading-your-models">Saving and Loading your models</a><ul> |
| <li><a class="reference internal" href="#1.-Save/load-the-model-weights/parameters-only">1. Save/load the model weights/parameters only</a></li> |
| <li><a class="reference internal" href="#2.-Save/load-the-model-weights/parameters-and-the-architectures">2. Save/load the model weights/parameters and the architectures</a></li> |
| </ul> |
| </li> |
| <li><a class="reference internal" href="#Visualizing-your-models">Visualizing your models</a></li> |
| <li><a class="reference internal" href="#Next-steps:">Next steps:</a></li> |
| </ul> |
| </li> |
| </ul> |
| |
| </div> |
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| <div>Step 1: Manipulate data with NP on MXNet</div> |
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| <div>Step 3: Automatic differentiation with autograd</div> |
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