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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Python Tutorials</a><ul class="current"> |
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| <li class="toctree-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"><a class="reference internal" href="2-create-nn.html">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 current"><a class="current reference internal" href="#">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> |
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| <li class="toctree-l3"><a class="reference internal" href="../../packages/autograd/index.html">Automatic Differentiation</a></li> |
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| <li class="toctree-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/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> |
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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> |
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| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </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> |
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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-l3"><a class="reference internal" href="../../packages/optimizer/index.html">Optimizers</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-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"><a class="reference internal" href="2-create-nn.html">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 current"><a class="current reference internal" href="#">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-6:-Train-a-Neural-Network"> |
| <h1>Step 6: Train a Neural Network<a class="headerlink" href="#Step-6:-Train-a-Neural-Network" title="Permalink to this headline">¶</a></h1> |
| <p>Now that you have seen all the necessary components for creating a neural network, you are now ready to put all the pieces together and train a model end to end.</p> |
| <div class="section" id="1.-Data-preparation"> |
| <h2>1. Data preparation<a class="headerlink" href="#1.-Data-preparation" title="Permalink to this headline">¶</a></h2> |
| <p>The typical process for creating and training a model starts with loading and preparing the datasets. For this Network you will use a <a class="reference external" href="https://data.mendeley.com/datasets/hb74ynkjcn/1">dataset of leaf images</a> that consists of healthy and diseased examples of leafs from twelve different plant species. To get this dataset you have to download and extract it with the following commands.</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="c1"># Import all the necessary libraries to train</span> |
| <span class="kn">import</span> <span class="nn">time</span> |
| <span class="kn">import</span> <span class="nn">os</span> |
| <span class="kn">import</span> <span class="nn">zipfile</span> |
| |
| <span class="kn">import</span> <span class="nn">mxnet</span> <span class="k">as</span> <span class="nn">mx</span> |
| <span class="kn">from</span> <span class="nn">mxnet</span> <span class="kn">import</span> <span class="n">np</span><span class="p">,</span> <span class="n">npx</span><span class="p">,</span> <span class="n">gluon</span><span class="p">,</span> <span class="n">init</span><span class="p">,</span> <span class="n">autograd</span> |
| <span class="kn">from</span> <span class="nn">mxnet.gluon</span> <span class="kn">import</span> <span class="n">nn</span> |
| <span class="kn">from</span> <span class="nn">mxnet.gluon.data.vision</span> <span class="kn">import</span> <span class="n">transforms</span> |
| |
| <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span> |
| <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span> |
| <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> |
| |
| <span class="kn">from</span> <span class="nn">prepare_dataset</span> <span class="kn">import</span> <span class="n">process_dataset</span> <span class="c1">#utility code to rearrange the data</span> |
| |
| <span class="n">mx</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">seed</span><span class="p">(</span><span class="mi">42</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>[2]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Download dataset</span> |
| <span class="n">url</span> <span class="o">=</span> <span class="s1">'https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/hb74ynkjcn-1.zip'</span> |
| <span class="n">zip_file_path</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="n">url</span><span class="p">)</span> |
| |
| <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="s1">'plants'</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| |
| <span class="k">with</span> <span class="n">zipfile</span><span class="o">.</span><span class="n">ZipFile</span><span class="p">(</span><span class="n">zip_file_path</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">)</span> <span class="k">as</span> <span class="n">zf</span><span class="p">:</span> |
| <span class="n">zf</span><span class="o">.</span><span class="n">extractall</span><span class="p">(</span><span class="s1">'plants'</span><span class="p">)</span> |
| |
| <span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">zip_file_path</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> |
| Downloading hb74ynkjcn-1.zip from https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/hb74ynkjcn-1.zip... |
| </pre></div></div> |
| </div> |
| <div class="section" id="Data-inspection"> |
| <h3>Data inspection<a class="headerlink" href="#Data-inspection" title="Permalink to this headline">¶</a></h3> |
| <p>If you take a look at the dataset you find the following structure for the directories:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plants</span> |
| <span class="o">|--</span> <span class="n">Alstonia</span> <span class="n">Scholaris</span> <span class="p">(</span><span class="n">P2</span><span class="p">)</span> |
| <span class="o">|--</span> <span class="n">Arjun</span> <span class="p">(</span><span class="n">P1</span><span class="p">)</span> |
| <span class="o">|--</span> <span class="n">Bael</span> <span class="p">(</span><span class="n">P4</span><span class="p">)</span> |
| <span class="o">|--</span> <span class="n">diseased</span> |
| <span class="o">|--</span> <span class="mf">0016_0001.</span><span class="n">JPG</span> |
| <span class="o">|--</span> <span class="o">.</span> |
| <span class="o">|--</span> <span class="o">.</span> |
| <span class="o">|--</span> <span class="o">.</span> |
| <span class="o">|--</span> <span class="mf">0016_0118.</span><span class="n">JPG</span> |
| <span class="o">|--</span> <span class="o">.</span> |
| <span class="o">|--</span> <span class="o">.</span> |
| <span class="o">|--</span> <span class="o">.</span> |
| <span class="o">|--</span> <span class="n">Mango</span> <span class="p">(</span><span class="n">P0</span><span class="p">)</span> |
| <span class="o">|--</span> <span class="n">diseased</span> |
| <span class="o">|--</span> <span class="n">healthy</span> |
| </pre></div> |
| </div> |
| <p>Each plant species has its own directory, for each of those directories you might find subdirectories with examples of diseased leaves, healthy leaves, or both. With this dataset you can formulate different classification problems; for example, you can create a multi-class classifier that determines the species of a plant based on the leaves; you can instead create a binary classifier that tells you whether the plant is healthy or diseased. Additionally, you can create a multi-class, multi-label |
| classifier that tells you both: what species a plant is and whether the plant is diseased or healthy. In this example you will stick to the simplest classification question, which is whether a plant is healthy or not.</p> |
| <p>To do this, you need to manipulate the dataset in two ways. First, you need to combine all images with labels consisting of healthy and diseased, regardless of the species, and then you need to split the data into train, validation, and test sets. We prepared a small utility script that does this to get the dataset ready for you. Once you run this utility code on the data, the structure will be already organized in folders containing the right images in each of the classes, you can use the |
| <code class="docutils literal notranslate"><span class="pre">ImageFolderDataset</span></code> class to import the images from the file to MXNet.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Call the utility function to rearrange the images</span> |
| <span class="n">process_dataset</span><span class="p">(</span><span class="s1">'plants'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>The dataset is located in the <code class="docutils literal notranslate"><span class="pre">datasets</span></code> folder and the new structure looks like this:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">datasets</span> |
| <span class="o">|--</span> <span class="n">test</span> |
| <span class="o">|--</span> <span class="n">diseased</span> |
| <span class="o">|--</span> <span class="n">healthy</span> |
| <span class="o">|--</span> <span class="n">train</span> |
| <span class="o">|--</span> <span class="n">validation</span> |
| <span class="o">|--</span> <span class="n">diseased</span> |
| <span class="o">|--</span> <span class="n">healthy</span> |
| <span class="o">|--</span> <span class="n">image1</span><span class="o">.</span><span class="n">JPG</span> |
| <span class="o">|--</span> <span class="n">image2</span><span class="o">.</span><span class="n">JPG</span> |
| <span class="o">|--</span> <span class="o">.</span> |
| <span class="o">|--</span> <span class="o">.</span> |
| <span class="o">|--</span> <span class="o">.</span> |
| <span class="o">|--</span> <span class="n">imagen</span><span class="o">.</span><span class="n">JPG</span> |
| </pre></div> |
| </div> |
| <p>Now, you need to create three different Dataset objects from the <code class="docutils literal notranslate"><span class="pre">train</span></code>, <code class="docutils literal notranslate"><span class="pre">validation</span></code>, and <code class="docutils literal notranslate"><span class="pre">test</span></code> folders, and the <code class="docutils literal notranslate"><span class="pre">ImageFolderDataset</span></code> class takes care of inferring the classes from the directory names. If you don’t remember how the <code class="docutils literal notranslate"><span class="pre">ImageFolderDataset</span></code> works, take a look at <a class="reference internal" href="5-datasets.html"><span class="doc">Step 5</span></a> of this course for a deeper description.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Use ImageFolderDataset to create a Dataset object from directory structure</span> |
| <span class="n">train_dataset</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">ImageFolderDataset</span><span class="p">(</span><span class="s1">'./datasets/train'</span><span class="p">)</span> |
| <span class="n">val_dataset</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">ImageFolderDataset</span><span class="p">(</span><span class="s1">'./datasets/validation'</span><span class="p">)</span> |
| <span class="n">test_dataset</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">ImageFolderDataset</span><span class="p">(</span><span class="s1">'./datasets/test'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>The result from this operation is a different Dataset object for each folder. These objects hold a collection of images and labels and as such they can be indexed, to get the <span class="math notranslate nohighlight">\(i\)</span>-th element from the dataset. The <span class="math notranslate nohighlight">\(i\)</span>-th element is a tuple with two objects, the first object of the tuple is the image in array form and the second is the corresponding label for that image.</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">sample_idx</span> <span class="o">=</span> <span class="mi">888</span> <span class="c1"># choose a random sample</span> |
| <span class="n">sample</span> <span class="o">=</span> <span class="n">train_dataset</span><span class="p">[</span><span class="n">sample_idx</span><span class="p">]</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">sample</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">sample</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| |
| <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span> |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Data type: </span><span class="si">{</span><span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Label: </span><span class="si">{</span><span class="n">label</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Label description: </span><span class="si">{</span><span class="n">train_dataset</span><span class="o">.</span><span class="n">synsets</span><span class="p">[</span><span class="n">label</span><span class="p">]</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Image shape: </span><span class="si">{</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">"</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> |
| [01:24:53] /work/mxnet/src/storage/storage.cc:202: Using Pooled (Naive) StorageManager for CPU |
| </pre></div></div> |
| </div> |
| <div class="nboutput docutils container"> |
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| <div class="highlight"><pre> |
| Data type: uint8 |
| Label: 0 |
| Label description: diseased |
| Image shape: (4000, 6000, 3) |
| </pre></div></div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
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| <img alt="../../../_images/tutorials_getting-started_crash-course_6-train-nn_12_2.png" src="../../../_images/tutorials_getting-started_crash-course_6-train-nn_12_2.png" /> |
| </div> |
| </div> |
| <p>As you can see from the plot, the image size is very large 4000 x 6000 pixels. Usually, you downsize images before passing them to a neural network to reduce the training time. It is also customary to make slight modifications to the images to improve generalization. That is why you add transformations to the data in a process called Data Augmentation.</p> |
| <p>You can augment data in MXNet using <code class="docutils literal notranslate"><span class="pre">transforms</span></code>. For a complete list of all the available transformations in MXNet check out <a class="reference internal" href="../../../api/gluon/data/vision/transforms/index.html"><span class="doc">available transforms</span></a>. It is very common to use more than one transform per image, and it is also common to process transforms sequentially. To this end, you can use the <code class="docutils literal notranslate"><span class="pre">transforms.Compose</span></code> class. This class is very useful to create a transformation pipeline for your images.</p> |
| <p>You have to compose two different transformation pipelines, one for training and the other one for validating and testing. This is because each pipeline serves different pursposes. You need to downsize, convert to tensor and normalize images across all the different datsets; however, you typically do not want to randomly flip or add color jitter to the validation or test images since you could reduce performance.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Import transforms as compose a series of transformations to the images</span> |
| <span class="kn">from</span> <span class="nn">mxnet.gluon.data.vision</span> <span class="kn">import</span> <span class="n">transforms</span> |
| |
| <span class="n">jitter_param</span> <span class="o">=</span> <span class="mf">0.05</span> |
| |
| <span class="c1"># mean and std for normalizing image value in range (0,1)</span> |
| <span class="n">mean</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">]</span> |
| <span class="n">std</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">]</span> |
| |
| <span class="n">training_transformer</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">224</span><span class="p">,</span> <span class="n">keep_ratio</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">128</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">RandomFlipLeftRight</span><span class="p">(),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">RandomColorJitter</span><span class="p">(</span><span class="n">contrast</span><span class="o">=</span><span class="n">jitter_param</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">)</span> |
| <span class="p">])</span> |
| |
| <span class="n">validation_transformer</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">224</span><span class="p">,</span> <span class="n">keep_ratio</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">128</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">)</span> |
| <span class="p">])</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>With your augmentations ready, you can create the <code class="docutils literal notranslate"><span class="pre">DataLoaders</span></code> to use them. To do this the <code class="docutils literal notranslate"><span class="pre">gluon.data.DataLoader</span></code> class comes in handy. You have to pass the dataset with the applied transformations (notice the <code class="docutils literal notranslate"><span class="pre">.transform_first()</span></code> method on the datasets) to <code class="docutils literal notranslate"><span class="pre">gluon.data.DataLoader</span></code>. Additionally, you need to decide the batch size, which is how many images you will be passing to the network, and whether you want to shuffle the dataset.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Create data loaders</span> |
| <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">4</span> |
| <span class="n">train_loader</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">train_dataset</span><span class="o">.</span><span class="n">transform_first</span><span class="p">(</span><span class="n">training_transformer</span><span class="p">),</span> |
| <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> |
| <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">try_nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">validation_loader</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">val_dataset</span><span class="o">.</span><span class="n">transform_first</span><span class="p">(</span><span class="n">validation_transformer</span><span class="p">),</span> |
| <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> |
| <span class="n">try_nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">test_loader</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">test_dataset</span><span class="o">.</span><span class="n">transform_first</span><span class="p">(</span><span class="n">validation_transformer</span><span class="p">),</span> |
| <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> |
| <span class="n">try_nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>Now, you can inspect the transformations that you made to the images. A prepared utility function has been provided for this.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Function to plot batch</span> |
| <span class="k">def</span> <span class="nf">show_batch</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">fig_size</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">pad</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span> |
| <span class="n">labels</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="mi">2</span> <span class="o">+</span> <span class="mf">0.5</span> <span class="c1"># unnormalize</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">(),</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># clip values</span> |
| <span class="n">size</span> <span class="o">=</span> <span class="n">batch</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">rows</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">size</span> <span class="o">/</span> <span class="n">columns</span><span class="p">)</span> |
| <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">columns</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">fig_size</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">img</span><span class="p">,</span> <span class="n">label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axes</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">batch</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span> |
| <span class="n">ax</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span> |
| <span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="sa">f</span><span class="s2">"Label: </span><span class="si">{</span><span class="n">label</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| <span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">(</span><span class="n">h_pad</span><span class="o">=</span><span class="n">pad</span><span class="p">,</span> <span class="n">w_pad</span><span class="o">=</span><span class="n">pad</span><span class="p">)</span> |
| <span class="n">plt</span><span class="o">.</span><span class="n">show</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>[9]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">train_loader</span><span class="p">:</span> |
| <span class="n">a</span> <span class="o">=</span> <span class="n">batch</span> |
| <span class="k">break</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">show_batch</span><span class="p">(</span><span class="n">a</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"> |
| <img alt="../../../_images/tutorials_getting-started_crash-course_6-train-nn_20_0.png" src="../../../_images/tutorials_getting-started_crash-course_6-train-nn_20_0.png" /> |
| </div> |
| </div> |
| <p>You can see that the original images changed to have different sizes and variations in color and lighting. These changes followed the specified transformations you stated in the pipeline. You are now ready to go to the next step: <strong>Create the architecture</strong>.</p> |
| </div> |
| </div> |
| <div class="section" id="2.-Create-Neural-Network"> |
| <h2>2. Create Neural Network<a class="headerlink" href="#2.-Create-Neural-Network" title="Permalink to this headline">¶</a></h2> |
| <p>Convolutional neural networks are a great tool to capture the spatial relationship of pixel values within images, for this reason they have become the gold standard for computer vision. In this example you will create a small convolutional neural network using what you learned from <a class="reference internal" href="2-create-nn.html"><span class="doc">Step 2</span></a> of this crash course series. First, you can set up two functions that will generate the two types of blocks you intend to use, the convolution block and the dense block. Then you can create |
| an entire network based on these two blocks using a custom class.</p> |
| <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="c1"># The convolutional block has a convolution layer, a max pool layer and a batch normalization layer</span> |
| <span class="k">def</span> <span class="nf">conv_block</span><span class="p">(</span><span class="n">filters</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">batch_norm</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> |
| <span class="n">conv_block</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">conv_block</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">Conv2D</span><span class="p">(</span><span class="n">channels</span><span class="o">=</span><span class="n">filters</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="n">kernel_size</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">MaxPool2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="n">stride</span><span class="p">))</span> |
| <span class="k">if</span> <span class="n">batch_norm</span><span class="p">:</span> |
| <span class="n">conv_block</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">BatchNorm</span><span class="p">())</span> |
| <span class="k">return</span> <span class="n">conv_block</span> |
| |
| <span class="c1"># The dense block consists of a dense layer and a dropout layer</span> |
| <span class="k">def</span> <span class="nf">dense_block</span><span class="p">(</span><span class="n">neurons</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">dropout</span><span class="o">=</span><span class="mf">0.2</span><span class="p">):</span> |
| <span class="n">dense_block</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">dense_block</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="n">neurons</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="n">activation</span><span class="p">))</span> |
| <span class="k">if</span> <span class="n">dropout</span><span class="p">:</span> |
| <span class="n">dense_block</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">Dropout</span><span class="p">(</span><span class="n">dropout</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">dense_block</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Create neural network blueprint using the blocks</span> |
| <span class="k">class</span> <span class="nc">LeafNetwork</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="nb">super</span><span class="p">(</span><span class="n">LeafNetwork</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">conv_block</span><span class="p">(</span><span class="mi">32</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">conv_block</span><span class="p">(</span><span class="mi">64</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">conv3</span> <span class="o">=</span> <span class="n">conv_block</span><span class="p">(</span><span class="mi">128</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Flatten</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">dense_block</span><span class="p">(</span><span class="mi">100</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">dense_block</span><span class="p">(</span><span class="mi">10</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">batch</span><span class="p">):</span> |
| <span class="n">batch</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">batch</span><span class="p">)</span> |
| <span class="n">batch</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">batch</span><span class="p">)</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv3</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="n">batch</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">batch</span><span class="p">)</span> |
| <span class="n">batch</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">batch</span><span class="p">)</span> |
| <span class="n">batch</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">batch</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">batch</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>You have concluded the architecting part of the network, so now you can actually build a model from that architecture for training. As you have seen previously on <a class="reference internal" href="4-components.html"><span class="doc">Step 4</span></a> of this crash course series, to use the network you need to initialize the parameters and hybridize the model.</p> |
| <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="c1"># Create the model based on the blueprint provided and initialize the parameters</span> |
| <span class="n">device</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">()</span> |
| |
| <span class="n">initializer</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">initializer</span><span class="o">.</span><span class="n">Xavier</span><span class="p">()</span> |
| |
| <span class="n">model</span> <span class="o">=</span> <span class="n">LeafNetwork</span><span class="p">()</span> |
| <span class="n">model</span><span class="o">.</span><span class="n">initialize</span><span class="p">(</span><span class="n">initializer</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span> |
| <span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">(</span><span class="n">mx</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="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">))</span> |
| <span class="n">model</span><span class="o">.</span><span class="n">hybridize</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 docutils container"> |
| <div class="highlight"><pre> |
| -------------------------------------------------------------------------------- |
| Layer (type) Output Shape Param # |
| ================================================================================ |
| Input (4, 3, 128, 128) 0 |
| Activation-1 (4, 32, 127, 127) 0 |
| Conv2D-2 (4, 32, 127, 127) 416 |
| MaxPool2D-3 (4, 32, 62, 62) 0 |
| BatchNorm-4 (4, 32, 62, 62) 128 |
| Activation-5 (4, 64, 61, 61) 0 |
| Conv2D-6 (4, 64, 61, 61) 8256 |
| MaxPool2D-7 (4, 64, 29, 29) 0 |
| BatchNorm-8 (4, 64, 29, 29) 256 |
| Activation-9 (4, 128, 28, 28) 0 |
| Conv2D-10 (4, 128, 28, 28) 32896 |
| MaxPool2D-11 (4, 128, 13, 13) 0 |
| BatchNorm-12 (4, 128, 13, 13) 512 |
| Flatten-13 (4, 21632) 0 |
| Activation-14 (4, 100) 0 |
| Dense-15 (4, 100) 2163300 |
| Dropout-16 (4, 100) 0 |
| Activation-17 (4, 10) 0 |
| Dense-18 (4, 10) 1010 |
| Dropout-19 (4, 10) 0 |
| Dense-20 (4, 2) 22 |
| LeafNetwork-21 (4, 2) 0 |
| ================================================================================ |
| Parameters in forward computation graph, duplicate included |
| Total params: 2206796 |
| Trainable params: 2206348 |
| Non-trainable params: 448 |
| Shared params in forward computation graph: 0 |
| Unique parameters in model: 2206796 |
| -------------------------------------------------------------------------------- |
| </pre></div></div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area stderr docutils container"> |
| <div class="highlight"><pre> |
| [01:25:00] /work/mxnet/src/storage/storage.cc:202: Using Pooled (Naive) StorageManager for GPU |
| </pre></div></div> |
| </div> |
| </div> |
| <div class="section" id="3.-Choose-Optimizer-and-Loss-function"> |
| <h2>3. Choose Optimizer and Loss function<a class="headerlink" href="#3.-Choose-Optimizer-and-Loss-function" title="Permalink to this headline">¶</a></h2> |
| <p>With the network created you can move on to choosing an optimizer and a loss function. The network you created uses these components to make an informed decision on how to tune the parameters to fit the final objective better. You can use the <code class="docutils literal notranslate"><span class="pre">gluon.Trainer</span></code> class to help with optimizing these parameters. The <code class="docutils literal notranslate"><span class="pre">gluon.Trainer</span></code> class needs two things to work properly: the parameters needing to be tuned and the optimizer with its corresponding hyperparameters. The trainer uses the error reported |
| by the loss function to optimize these parameters.</p> |
| <p>For this particular dataset you will use Stochastic Gradient Descent as the optimizer and Cross Entropy as the loss function.</p> |
| <div class="nbinput nblast 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="c1"># SGD optimizer</span> |
| <span class="n">optimizer</span> <span class="o">=</span> <span class="s1">'sgd'</span> |
| |
| <span class="c1"># Set parameters</span> |
| <span class="n">optimizer_params</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'learning_rate'</span><span class="p">:</span> <span class="mf">0.001</span><span class="p">}</span> |
| |
| <span class="c1"># Define the trainer for the model</span> |
| <span class="n">trainer</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">Trainer</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(),</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">optimizer_params</span><span class="p">)</span> |
| |
| <span class="c1"># Define the loss function</span> |
| <span class="n">loss_fn</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">loss</span><span class="o">.</span><span class="n">SoftmaxCrossEntropyLoss</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>Finally, you have to set up the training loop, and you need to create a function to evaluate the performance of the network on the validation dataset.</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="c1"># Function to return the accuracy for the validation and test set</span> |
| <span class="k">def</span> <span class="nf">test</span><span class="p">(</span><span class="n">val_data</span><span class="p">):</span> |
| <span class="n">acc</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">val_data</span><span class="p">:</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">labels</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">device</span><span class="p">))</span> |
| <span class="n">acc</span><span class="o">.</span><span class="n">update</span><span class="p">([</span><span class="n">labels</span><span class="p">],</span> <span class="p">[</span><span class="n">outputs</span><span class="p">])</span> |
| |
| <span class="n">_</span><span class="p">,</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="n">acc</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| <span class="k">return</span> <span class="n">accuracy</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="4.-Training-Loop"> |
| <h2>4. Training Loop<a class="headerlink" href="#4.-Training-Loop" title="Permalink to this headline">¶</a></h2> |
| <p>Now that you have everything set up, you can start training your network. This might take some time to train depending on the hardware, number of layers, batch size and images you use. For this particular case, you will only train for 2 epochs.</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="c1"># Start the training loop</span> |
| <span class="n">epochs</span> <span class="o">=</span> <span class="mi">2</span> |
| <span class="n">accuracy</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()</span> |
| <span class="n">log_interval</span> <span class="o">=</span> <span class="mi">5</span> |
| |
| <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span> |
| <span class="n">tic</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> |
| <span class="n">btic</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> |
| <span class="n">accuracy</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span> |
| |
| <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">train_loader</span><span class="p">):</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="k">with</span> <span class="n">mx</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">record</span><span class="p">():</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">device</span><span class="p">))</span> |
| <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_fn</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">label</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">device</span><span class="p">))</span> |
| <span class="n">mx</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span> |
| <span class="n">trainer</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">batch_size</span><span class="p">)</span> |
| <span class="n">accuracy</span><span class="o">.</span><span class="n">update</span><span class="p">([</span><span class="n">label</span><span class="p">],</span> <span class="p">[</span><span class="n">outputs</span><span class="p">])</span> |
| <span class="k">if</span> <span class="n">log_interval</span> <span class="ow">and</span> <span class="p">(</span><span class="n">idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="n">log_interval</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">_</span><span class="p">,</span> <span class="n">acc</span> <span class="o">=</span> <span class="n">accuracy</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"""Epoch[</span><span class="si">{</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="si">}</span><span class="s2">] Batch[</span><span class="si">{</span><span class="n">idx</span> <span class="o">+</span> <span class="mi">1</span><span class="si">}</span><span class="s2">] Speed: </span><span class="si">{</span><span class="n">batch_size</span> <span class="o">/</span> <span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">btic</span><span class="p">)</span><span class="si">}</span><span class="s2"> samples/sec </span><span class="se">\</span> |
| <span class="s2"> batch loss = </span><span class="si">{</span><span class="n">loss</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">}</span><span class="s2"> | accuracy = </span><span class="si">{</span><span class="n">acc</span><span class="si">}</span><span class="s2">"""</span><span class="p">)</span> |
| <span class="n">btic</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> |
| |
| <span class="n">_</span><span class="p">,</span> <span class="n">acc</span> <span class="o">=</span> <span class="n">accuracy</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| |
| <span class="n">acc_val</span> <span class="o">=</span> <span class="n">test</span><span class="p">(</span><span class="n">validation_loader</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"[Epoch </span><span class="si">{</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="si">}</span><span class="s2">] training: accuracy=</span><span class="si">{</span><span class="n">acc</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"[Epoch </span><span class="si">{</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="si">}</span><span class="s2">] time cost: </span><span class="si">{</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tic</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"[Epoch </span><span class="si">{</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="si">}</span><span class="s2">] validation: validation accuracy=</span><span class="si">{</span><span class="n">acc_val</span><span class="si">}</span><span class="s2">"</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> |
| [01:25:02] /work/mxnet/src/operator/cudnn_ops.cc:421: Auto-tuning cuDNN op, set MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable |
| [01:25:02] /work/mxnet/src/operator/cudnn_ops.cc:421: Auto-tuning cuDNN op, set MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable |
| </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> |
| Epoch[1] Batch[5] Speed: 1.2094752082762017 samples/sec batch loss = 0.8946486115455627 | accuracy = 0.5 |
| Epoch[1] Batch[10] Speed: 1.2525317770693851 samples/sec batch loss = 0.715431809425354 | accuracy = 0.6 |
| Epoch[1] Batch[15] Speed: 1.2502698437633675 samples/sec batch loss = 1.2139184474945068 | accuracy = 0.55 |
| Epoch[1] Batch[20] Speed: 1.249549290684904 samples/sec batch loss = 1.330371618270874 | accuracy = 0.575 |
| Epoch[1] Batch[25] Speed: 1.2561324108387326 samples/sec batch loss = 0.9924310445785522 | accuracy = 0.58 |
| Epoch[1] Batch[30] Speed: 1.2576777362422045 samples/sec batch loss = 0.6711029410362244 | accuracy = 0.5916666666666667 |
| Epoch[1] Batch[35] Speed: 1.250056980912456 samples/sec batch loss = 0.3935161232948303 | accuracy = 0.6071428571428571 |
| Epoch[1] Batch[40] Speed: 1.2539254079072892 samples/sec batch loss = 0.6774281859397888 | accuracy = 0.63125 |
| Epoch[1] Batch[45] Speed: 1.265667382941042 samples/sec batch loss = 0.4509645402431488 | accuracy = 0.6277777777777778 |
| Epoch[1] Batch[50] Speed: 1.2612228178483773 samples/sec batch loss = 0.6163473725318909 | accuracy = 0.61 |
| Epoch[1] Batch[55] Speed: 1.25978303507013 samples/sec batch loss = 0.7209182977676392 | accuracy = 0.6 |
| Epoch[1] Batch[60] Speed: 1.2590049928807137 samples/sec batch loss = 0.45505738258361816 | accuracy = 0.6083333333333333 |
| Epoch[1] Batch[65] Speed: 1.2554264110868805 samples/sec batch loss = 1.1987841129302979 | accuracy = 0.6153846153846154 |
| Epoch[1] Batch[70] Speed: 1.2582800956781557 samples/sec batch loss = 0.706019401550293 | accuracy = 0.6142857142857143 |
| Epoch[1] Batch[75] Speed: 1.2537531776857076 samples/sec batch loss = 0.49340009689331055 | accuracy = 0.6166666666666667 |
| Epoch[1] Batch[80] Speed: 1.2577621223632278 samples/sec batch loss = 0.3584567904472351 | accuracy = 0.628125 |
| Epoch[1] Batch[85] Speed: 1.257918478754745 samples/sec batch loss = 0.7450965642929077 | accuracy = 0.6294117647058823 |
| Epoch[1] Batch[90] Speed: 1.254832411312269 samples/sec batch loss = 0.4519902765750885 | accuracy = 0.6277777777777778 |
| Epoch[1] Batch[95] Speed: 1.2551995806728238 samples/sec batch loss = 0.5869596600532532 | accuracy = 0.6342105263157894 |
| Epoch[1] Batch[100] Speed: 1.2510486914677892 samples/sec batch loss = 0.26176637411117554 | accuracy = 0.64 |
| Epoch[1] Batch[105] Speed: 1.256645089181554 samples/sec batch loss = 0.3798101246356964 | accuracy = 0.6428571428571429 |
| Epoch[1] Batch[110] Speed: 1.2536545273890882 samples/sec batch loss = 0.2899395823478699 | accuracy = 0.6477272727272727 |
| Epoch[1] Batch[115] Speed: 1.2575093749838382 samples/sec batch loss = 0.16547241806983948 | accuracy = 0.658695652173913 |
| Epoch[1] Batch[120] Speed: 1.2538265430996671 samples/sec batch loss = 1.0631895065307617 | accuracy = 0.6541666666666667 |
| Epoch[1] Batch[125] Speed: 1.257477706234894 samples/sec batch loss = 0.6659188866615295 | accuracy = 0.656 |
| Epoch[1] Batch[130] Speed: 1.2543817937017492 samples/sec batch loss = 0.43466880917549133 | accuracy = 0.6538461538461539 |
| Epoch[1] Batch[135] Speed: 1.2579621485509929 samples/sec batch loss = 0.18708443641662598 | accuracy = 0.6555555555555556 |
| Epoch[1] Batch[140] Speed: 1.2580081796097924 samples/sec batch loss = 0.7568383812904358 | accuracy = 0.6535714285714286 |
| Epoch[1] Batch[145] Speed: 1.2569334604759939 samples/sec batch loss = 0.6102699041366577 | accuracy = 0.6586206896551724 |
| Epoch[1] Batch[150] Speed: 1.2542231272866458 samples/sec batch loss = 0.7128711342811584 | accuracy = 0.66 |
| Epoch[1] Batch[155] Speed: 1.2599683754037092 samples/sec batch loss = 0.5697439312934875 | accuracy = 0.6629032258064517 |
| Epoch[1] Batch[160] Speed: 1.259060359973908 samples/sec batch loss = 0.5102454423904419 | accuracy = 0.6625 |
| Epoch[1] Batch[165] Speed: 1.2578278476666158 samples/sec batch loss = 0.3453787565231323 | accuracy = 0.6636363636363637 |
| Epoch[1] Batch[170] Speed: 1.2550319760855042 samples/sec batch loss = 0.30261310935020447 | accuracy = 0.6617647058823529 |
| Epoch[1] Batch[175] Speed: 1.257768157111865 samples/sec batch loss = 0.31089457869529724 | accuracy = 0.6657142857142857 |
| Epoch[1] Batch[180] Speed: 1.2593705435243274 samples/sec batch loss = 0.5370045304298401 | accuracy = 0.6625 |
| Epoch[1] Batch[185] Speed: 1.2652339498840475 samples/sec batch loss = 0.16903318464756012 | accuracy = 0.6635135135135135 |
| Epoch[1] Batch[190] Speed: 1.254439005978491 samples/sec batch loss = 0.20440606772899628 | accuracy = 0.6644736842105263 |
| Epoch[1] Batch[195] Speed: 1.2567680285685756 samples/sec batch loss = 0.4329149127006531 | accuracy = 0.6628205128205128 |
| Epoch[1] Batch[200] Speed: 1.2560666745177622 samples/sec batch loss = 0.4615476429462433 | accuracy = 0.6625 |
| Epoch[1] Batch[205] Speed: 1.2567547544749167 samples/sec batch loss = 0.5208989977836609 | accuracy = 0.6609756097560976 |
| Epoch[1] Batch[210] Speed: 1.261930987547383 samples/sec batch loss = 0.3965013921260834 | accuracy = 0.6583333333333333 |
| Epoch[1] Batch[215] Speed: 1.260867562211556 samples/sec batch loss = 0.24366581439971924 | accuracy = 0.6616279069767442 |
| Epoch[1] Batch[220] Speed: 1.2584421501359089 samples/sec batch loss = 2.4047727584838867 | accuracy = 0.6636363636363637 |
| Epoch[1] Batch[225] Speed: 1.2588075628622861 samples/sec batch loss = 0.35746192932128906 | accuracy = 0.6677777777777778 |
| Epoch[1] Batch[230] Speed: 1.2527727984614174 samples/sec batch loss = 0.6212387681007385 | accuracy = 0.6663043478260869 |
| Epoch[1] Batch[235] Speed: 1.2537033353295284 samples/sec batch loss = 1.0685369968414307 | accuracy = 0.6659574468085107 |
| Epoch[1] Batch[240] Speed: 1.2528870284138156 samples/sec batch loss = 0.5241067409515381 | accuracy = 0.6666666666666666 |
| Epoch[1] Batch[245] Speed: 1.2578418988737223 samples/sec batch loss = 0.809574544429779 | accuracy = 0.6673469387755102 |
| Epoch[1] Batch[250] Speed: 1.2566055578895081 samples/sec batch loss = 0.5757331848144531 | accuracy = 0.669 |
| Epoch[1] Batch[255] Speed: 1.2611835668626536 samples/sec batch loss = 0.8489612340927124 | accuracy = 0.6647058823529411 |
| Epoch[1] Batch[260] Speed: 1.2560538854179464 samples/sec batch loss = 0.3527434766292572 | accuracy = 0.6653846153846154 |
| Epoch[1] Batch[265] Speed: 1.2630216898616657 samples/sec batch loss = 0.4529598355293274 | accuracy = 0.6650943396226415 |
| Epoch[1] Batch[270] Speed: 1.258395049051423 samples/sec batch loss = 0.6514775156974792 | accuracy = 0.6648148148148149 |
| Epoch[1] Batch[275] Speed: 1.2548454571170296 samples/sec batch loss = 0.4368631839752197 | accuracy = 0.6645454545454546 |
| Epoch[1] Batch[280] Speed: 1.2592324447716556 samples/sec batch loss = 0.4923084080219269 | accuracy = 0.6642857142857143 |
| Epoch[1] Batch[285] Speed: 1.2552899273127878 samples/sec batch loss = 0.9999520182609558 | accuracy = 0.6649122807017543 |
| Epoch[1] Batch[290] Speed: 1.2568961709398738 samples/sec batch loss = 1.1890711784362793 | accuracy = 0.6620689655172414 |
| Epoch[1] Batch[295] Speed: 1.2551127211670985 samples/sec batch loss = 0.7537636756896973 | accuracy = 0.6601694915254237 |
| Epoch[1] Batch[300] Speed: 1.261246236870676 samples/sec batch loss = 0.8091837167739868 | accuracy = 0.6591666666666667 |
| Epoch[1] Batch[305] Speed: 1.2611924787078315 samples/sec batch loss = 1.0757343769073486 | accuracy = 0.6565573770491804 |
| Epoch[1] Batch[310] Speed: 1.2539641146803695 samples/sec batch loss = 0.520529568195343 | accuracy = 0.6564516129032258 |
| Epoch[1] Batch[315] Speed: 1.2568534225272998 samples/sec batch loss = 0.30272695422172546 | accuracy = 0.6579365079365079 |
| Epoch[1] Batch[320] Speed: 1.2553861110005338 samples/sec batch loss = 0.3815951645374298 | accuracy = 0.65859375 |
| Epoch[1] Batch[325] Speed: 1.2525360785397277 samples/sec batch loss = 0.41755813360214233 | accuracy = 0.6607692307692308 |
| Epoch[1] Batch[330] Speed: 1.2544923775446288 samples/sec batch loss = 0.5407459735870361 | accuracy = 0.6606060606060606 |
| Epoch[1] Batch[335] Speed: 1.257825961622445 samples/sec batch loss = 0.5997440218925476 | accuracy = 0.6619402985074627 |
| Epoch[1] Batch[340] Speed: 1.261878974299729 samples/sec batch loss = 0.6288142204284668 | accuracy = 0.6602941176470588 |
| Epoch[1] Batch[345] Speed: 1.2594964751518967 samples/sec batch loss = 0.7436380386352539 | accuracy = 0.6579710144927536 |
| Epoch[1] Batch[350] Speed: 1.2516019507938845 samples/sec batch loss = 0.5851003527641296 | accuracy = 0.6557142857142857 |
| Epoch[1] Batch[355] Speed: 1.2573402107316132 samples/sec batch loss = 0.6791660189628601 | accuracy = 0.6570422535211268 |
| Epoch[1] Batch[360] Speed: 1.2575229477931196 samples/sec batch loss = 0.8414892554283142 | accuracy = 0.6569444444444444 |
| Epoch[1] Batch[365] Speed: 1.2532719710830096 samples/sec batch loss = 0.322709321975708 | accuracy = 0.6582191780821918 |
| Epoch[1] Batch[370] Speed: 1.2590129291616419 samples/sec batch loss = 0.8271729946136475 | accuracy = 0.6581081081081082 |
| Epoch[1] Batch[375] Speed: 1.2536888143359117 samples/sec batch loss = 0.3898996412754059 | accuracy = 0.6593333333333333 |
| Epoch[1] Batch[380] Speed: 1.2619771196722829 samples/sec batch loss = 0.16443590819835663 | accuracy = 0.6585526315789474 |
| Epoch[1] Batch[385] Speed: 1.2569531419565587 samples/sec batch loss = 0.643121600151062 | accuracy = 0.6571428571428571 |
| Epoch[1] Batch[390] Speed: 1.2546374135583003 samples/sec batch loss = 0.609272301197052 | accuracy = 0.6576923076923077 |
| Epoch[1] Batch[395] Speed: 1.2545744604322464 samples/sec batch loss = 0.4991946816444397 | accuracy = 0.6582278481012658 |
| Epoch[1] Batch[400] Speed: 1.2605042673421727 samples/sec batch loss = 0.5915560722351074 | accuracy = 0.6575 |
| Epoch[1] Batch[405] Speed: 1.2546629343598448 samples/sec batch loss = 0.6265003085136414 | accuracy = 0.658641975308642 |
| Epoch[1] Batch[410] Speed: 1.2491778851974364 samples/sec batch loss = 0.7099711894989014 | accuracy = 0.6573170731707317 |
| Epoch[1] Batch[415] Speed: 1.2479586947875392 samples/sec batch loss = 1.056666612625122 | accuracy = 0.6578313253012048 |
| Epoch[1] Batch[420] Speed: 1.2538723657024098 samples/sec batch loss = 1.3171554803848267 | accuracy = 0.656547619047619 |
| Epoch[1] Batch[425] Speed: 1.2599346903084054 samples/sec batch loss = 0.46549272537231445 | accuracy = 0.658235294117647 |
| Epoch[1] Batch[430] Speed: 1.2551031438818043 samples/sec batch loss = 0.788870096206665 | accuracy = 0.6563953488372093 |
| Epoch[1] Batch[435] Speed: 1.2514350254975577 samples/sec batch loss = 0.6447353363037109 | accuracy = 0.6563218390804598 |
| Epoch[1] Batch[440] Speed: 1.2603929051693699 samples/sec batch loss = 0.5047776699066162 | accuracy = 0.6590909090909091 |
| Epoch[1] Batch[445] Speed: 1.2591398287660927 samples/sec batch loss = 0.46104633808135986 | accuracy = 0.6595505617977528 |
| Epoch[1] Batch[450] Speed: 1.2580808174353044 samples/sec batch loss = 0.47700342535972595 | accuracy = 0.6611111111111111 |
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| Epoch[1] Batch[590] Speed: 1.2584186463484088 samples/sec batch loss = 0.5543620586395264 | accuracy = 0.6758474576271186 |
| Epoch[1] Batch[595] Speed: 1.260029221306409 samples/sec batch loss = 0.7016157507896423 | accuracy = 0.6743697478991597 |
| Epoch[1] Batch[600] Speed: 1.2584112838967658 samples/sec batch loss = 0.7444646954536438 | accuracy = 0.6741666666666667 |
| Epoch[1] Batch[605] Speed: 1.2545307440945765 samples/sec batch loss = 1.2177116870880127 | accuracy = 0.6739669421487603 |
| Epoch[1] Batch[610] Speed: 1.2582364981682421 samples/sec batch loss = 0.37278103828430176 | accuracy = 0.6733606557377049 |
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| Epoch[1] Batch[620] Speed: 1.2567913765909609 samples/sec batch loss = 0.6187570691108704 | accuracy = 0.6737903225806452 |
| Epoch[1] Batch[625] Speed: 1.2560609381981354 samples/sec batch loss = 0.32316815853118896 | accuracy = 0.6744 |
| Epoch[1] Batch[630] Speed: 1.2559122823525748 samples/sec batch loss = 0.9970830678939819 | accuracy = 0.6738095238095239 |
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| Epoch[1] Batch[660] Speed: 1.255212634113258 samples/sec batch loss = 0.9117534756660461 | accuracy = 0.6765151515151515 |
| Epoch[1] Batch[665] Speed: 1.257771551683422 samples/sec batch loss = 0.6813418865203857 | accuracy = 0.6774436090225564 |
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| Epoch[1] Batch[685] Speed: 1.2565764757412035 samples/sec batch loss = 0.6850987672805786 | accuracy = 0.6773722627737226 |
| Epoch[1] Batch[690] Speed: 1.2548731451553972 samples/sec batch loss = 0.34183651208877563 | accuracy = 0.678623188405797 |
| Epoch[1] Batch[695] Speed: 1.257981390633022 samples/sec batch loss = 0.1967257261276245 | accuracy = 0.6776978417266187 |
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| Epoch[1] Batch[705] Speed: 1.265100572202365 samples/sec batch loss = 0.6225217580795288 | accuracy = 0.6794326241134752 |
| Epoch[1] Batch[710] Speed: 1.2579817679346323 samples/sec batch loss = 0.291201114654541 | accuracy = 0.6809859154929577 |
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| Epoch[1] Batch[720] Speed: 1.2561983421570078 samples/sec batch loss = 0.3133390545845032 | accuracy = 0.6822916666666666 |
| Epoch[1] Batch[725] Speed: 1.2566229701759428 samples/sec batch loss = 1.2970517873764038 | accuracy = 0.6817241379310345 |
| Epoch[1] Batch[730] Speed: 1.2577916366067308 samples/sec batch loss = 0.6946151852607727 | accuracy = 0.6821917808219178 |
| Epoch[1] Batch[735] Speed: 1.2574916553682256 samples/sec batch loss = 0.31625884771347046 | accuracy = 0.6819727891156463 |
| Epoch[1] Batch[740] Speed: 1.2551341297450989 samples/sec batch loss = 0.1379917711019516 | accuracy = 0.6827702702702703 |
| Epoch[1] Batch[745] Speed: 1.2547019681822686 samples/sec batch loss = 0.3315030038356781 | accuracy = 0.6828859060402684 |
| Epoch[1] Batch[750] Speed: 1.2515932673399983 samples/sec batch loss = 0.6848659515380859 | accuracy = 0.6833333333333333 |
| Epoch[1] Batch[755] Speed: 1.2544753056533808 samples/sec batch loss = 0.2777027487754822 | accuracy = 0.6841059602649007 |
| Epoch[1] Batch[760] Speed: 1.2559480091979396 samples/sec batch loss = 0.19758139550685883 | accuracy = 0.6842105263157895 |
| Epoch[1] Batch[765] Speed: 1.2537870015416368 samples/sec batch loss = 0.7407524585723877 | accuracy = 0.6849673202614379 |
| Epoch[1] Batch[770] Speed: 1.2512570395820077 samples/sec batch loss = 1.6041826009750366 | accuracy = 0.685064935064935 |
| Epoch[1] Batch[775] Speed: 1.2577724946232174 samples/sec batch loss = 0.21234939992427826 | accuracy = 0.6854838709677419 |
| Epoch[1] Batch[780] Speed: 1.2530577104927985 samples/sec batch loss = 0.4312506914138794 | accuracy = 0.6855769230769231 |
| Epoch[1] Batch[785] Speed: 1.2485686892593888 samples/sec batch loss = 0.28475242853164673 | accuracy = 0.6859872611464968 |
| [Epoch 1] training: accuracy=0.6865482233502538 |
| [Epoch 1] time cost: 646.671017408371 |
| [Epoch 1] validation: validation accuracy=0.7644444444444445 |
| Epoch[2] Batch[5] Speed: 1.2558296483063547 samples/sec batch loss = 0.18339750170707703 | accuracy = 0.8 |
| Epoch[2] Batch[10] Speed: 1.2607668417705005 samples/sec batch loss = 0.30458471179008484 | accuracy = 0.775 |
| Epoch[2] Batch[15] Speed: 1.2555370852382486 samples/sec batch loss = 0.4829060733318329 | accuracy = 0.75 |
| Epoch[2] Batch[20] Speed: 1.2588524278836613 samples/sec batch loss = 0.8271627426147461 | accuracy = 0.775 |
| Epoch[2] Batch[25] Speed: 1.2568147254808733 samples/sec batch loss = 0.4423753023147583 | accuracy = 0.75 |
| Epoch[2] Batch[30] Speed: 1.2597228750474914 samples/sec batch loss = 1.4454216957092285 | accuracy = 0.7333333333333333 |
| Epoch[2] Batch[35] Speed: 1.2580777042135949 samples/sec batch loss = 0.5120770931243896 | accuracy = 0.75 |
| Epoch[2] Batch[40] Speed: 1.2586435257889566 samples/sec batch loss = 0.39711442589759827 | accuracy = 0.7625 |
| Epoch[2] Batch[45] Speed: 1.2593505026753253 samples/sec batch loss = 0.2529049515724182 | accuracy = 0.75 |
| Epoch[2] Batch[50] Speed: 1.2604665762406049 samples/sec batch loss = 0.4544322192668915 | accuracy = 0.74 |
| Epoch[2] Batch[55] Speed: 1.2523276782127348 samples/sec batch loss = 0.3910548686981201 | accuracy = 0.7454545454545455 |
| Epoch[2] Batch[60] Speed: 1.2576665170409858 samples/sec batch loss = 0.4778536260128021 | accuracy = 0.7333333333333333 |
| Epoch[2] Batch[65] Speed: 1.2552733972257297 samples/sec batch loss = 0.3742055296897888 | accuracy = 0.7461538461538462 |
| Epoch[2] Batch[70] Speed: 1.2553871443037607 samples/sec batch loss = 0.25441986322402954 | accuracy = 0.7428571428571429 |
| Epoch[2] Batch[75] Speed: 1.2627208246528823 samples/sec batch loss = 0.4777924120426178 | accuracy = 0.7533333333333333 |
| Epoch[2] Batch[80] Speed: 1.2572678468360678 samples/sec batch loss = 0.6392672657966614 | accuracy = 0.753125 |
| Epoch[2] Batch[85] Speed: 1.2593175122895781 samples/sec batch loss = 0.44576403498649597 | accuracy = 0.7588235294117647 |
| Epoch[2] Batch[90] Speed: 1.2610271560434307 samples/sec batch loss = 1.0588587522506714 | accuracy = 0.7555555555555555 |
| Epoch[2] Batch[95] Speed: 1.2604862738215465 samples/sec batch loss = 0.23690365254878998 | accuracy = 0.7552631578947369 |
| Epoch[2] Batch[100] Speed: 1.2626812903101503 samples/sec batch loss = 0.9404551386833191 | accuracy = 0.7475 |
| Epoch[2] Batch[105] Speed: 1.260788443670378 samples/sec batch loss = 0.28834500908851624 | accuracy = 0.75 |
| Epoch[2] Batch[110] Speed: 1.2583074636239673 samples/sec batch loss = 0.5393657088279724 | accuracy = 0.7545454545454545 |
| Epoch[2] Batch[115] Speed: 1.2592793250043477 samples/sec batch loss = 0.7605298161506653 | accuracy = 0.7521739130434782 |
| Epoch[2] Batch[120] Speed: 1.2576937640197465 samples/sec batch loss = 0.28566932678222656 | accuracy = 0.7520833333333333 |
| Epoch[2] Batch[125] Speed: 1.257231479585857 samples/sec batch loss = 0.616927981376648 | accuracy = 0.754 |
| Epoch[2] Batch[130] Speed: 1.2587936789668015 samples/sec batch loss = 0.5888569951057434 | accuracy = 0.7538461538461538 |
| Epoch[2] Batch[135] Speed: 1.2566582668316224 samples/sec batch loss = 0.25657621026039124 | accuracy = 0.7555555555555555 |
| Epoch[2] Batch[140] Speed: 1.2593619410062922 samples/sec batch loss = 0.1320473849773407 | accuracy = 0.7535714285714286 |
| Epoch[2] Batch[145] Speed: 1.2589106154955891 samples/sec batch loss = 0.9257393479347229 | accuracy = 0.7517241379310344 |
| Epoch[2] Batch[150] Speed: 1.258332662061215 samples/sec batch loss = 0.7331487536430359 | accuracy = 0.7516666666666667 |
| Epoch[2] Batch[155] Speed: 1.2598283480631807 samples/sec batch loss = 0.13664911687374115 | accuracy = 0.7516129032258064 |
| Epoch[2] Batch[160] Speed: 1.2564239341596504 samples/sec batch loss = 0.33907896280288696 | accuracy = 0.7515625 |
| Epoch[2] Batch[165] Speed: 1.2604807811649617 samples/sec batch loss = 0.21885526180267334 | accuracy = 0.7515151515151515 |
| Epoch[2] Batch[170] Speed: 1.2572520183442375 samples/sec batch loss = 0.21169845759868622 | accuracy = 0.7514705882352941 |
| Epoch[2] Batch[175] Speed: 1.262631400742698 samples/sec batch loss = 0.1914394348859787 | accuracy = 0.7528571428571429 |
| Epoch[2] Batch[180] Speed: 1.2588041626962987 samples/sec batch loss = 0.2655162215232849 | accuracy = 0.7569444444444444 |
| Epoch[2] Batch[185] Speed: 1.2597953326374025 samples/sec batch loss = 0.37275657057762146 | accuracy = 0.7581081081081081 |
| Epoch[2] Batch[190] Speed: 1.2615829221605013 samples/sec batch loss = 0.426242470741272 | accuracy = 0.7578947368421053 |
| Epoch[2] Batch[195] Speed: 1.25853466353066 samples/sec batch loss = 0.4779931604862213 | accuracy = 0.7538461538461538 |
| Epoch[2] Batch[200] Speed: 1.2584476250397925 samples/sec batch loss = 0.20349042117595673 | accuracy = 0.75875 |
| Epoch[2] Batch[205] Speed: 1.2555540920792672 samples/sec batch loss = 0.2419997602701187 | accuracy = 0.7560975609756098 |
| Epoch[2] Batch[210] Speed: 1.257322967016175 samples/sec batch loss = 0.2783071994781494 | accuracy = 0.7559523809523809 |
| Epoch[2] Batch[215] Speed: 1.258320676161839 samples/sec batch loss = 0.48919516801834106 | accuracy = 0.7558139534883721 |
| Epoch[2] Batch[220] Speed: 1.2614535378740965 samples/sec batch loss = 0.3060440719127655 | accuracy = 0.7602272727272728 |
| Epoch[2] Batch[225] Speed: 1.2591651550549947 samples/sec batch loss = 0.2102556973695755 | accuracy = 0.7577777777777778 |
| Epoch[2] Batch[230] Speed: 1.2574295464033272 samples/sec batch loss = 0.1689767837524414 | accuracy = 0.7597826086956522 |
| Epoch[2] Batch[235] Speed: 1.2594534551347347 samples/sec batch loss = 0.19581563770771027 | accuracy = 0.7585106382978724 |
| Epoch[2] Batch[240] Speed: 1.25932876096616 samples/sec batch loss = 0.07194840162992477 | accuracy = 0.7572916666666667 |
| Epoch[2] Batch[245] Speed: 1.256564899726633 samples/sec batch loss = 0.2636943757534027 | accuracy = 0.7591836734693878 |
| Epoch[2] Batch[250] Speed: 1.2579597904931945 samples/sec batch loss = 0.7458643913269043 | accuracy = 0.759 |
| Epoch[2] Batch[255] Speed: 1.2573821441443374 samples/sec batch loss = 0.9508602023124695 | accuracy = 0.7578431372549019 |
| Epoch[2] Batch[260] Speed: 1.2538271990233172 samples/sec batch loss = 0.380630224943161 | accuracy = 0.7596153846153846 |
| Epoch[2] Batch[265] Speed: 1.254111090969911 samples/sec batch loss = 0.28281038999557495 | accuracy = 0.7575471698113208 |
| Epoch[2] Batch[270] Speed: 1.2614394058611929 samples/sec batch loss = 1.314603567123413 | accuracy = 0.7574074074074074 |
| Epoch[2] Batch[275] Speed: 1.263586734054079 samples/sec batch loss = 0.24300628900527954 | accuracy = 0.759090909090909 |
| Epoch[2] Batch[280] Speed: 1.2611614774064293 samples/sec batch loss = 2.6359310150146484 | accuracy = 0.7553571428571428 |
| Epoch[2] Batch[285] Speed: 1.2602211657042177 samples/sec batch loss = 0.2676393389701843 | accuracy = 0.756140350877193 |
| Epoch[2] Batch[290] Speed: 1.263303482898749 samples/sec batch loss = 0.2976229786872864 | accuracy = 0.7551724137931034 |
| Epoch[2] Batch[295] Speed: 1.260747988085683 samples/sec batch loss = 0.42395544052124023 | accuracy = 0.7567796610169492 |
| Epoch[2] Batch[300] Speed: 1.2648649879369305 samples/sec batch loss = 0.7860664129257202 | accuracy = 0.7566666666666667 |
| Epoch[2] Batch[305] Speed: 1.26642835041924 samples/sec batch loss = 0.4435337781906128 | accuracy = 0.7573770491803279 |
| Epoch[2] Batch[310] Speed: 1.2661320710362112 samples/sec batch loss = 0.6711947917938232 | accuracy = 0.7524193548387097 |
| Epoch[2] Batch[315] Speed: 1.2620306599469557 samples/sec batch loss = 0.3077999949455261 | accuracy = 0.7531746031746032 |
| Epoch[2] Batch[320] Speed: 1.2617177414994205 samples/sec batch loss = 0.2644447684288025 | accuracy = 0.75234375 |
| Epoch[2] Batch[325] Speed: 1.2596504257919507 samples/sec batch loss = 0.3218878507614136 | accuracy = 0.7530769230769231 |
| Epoch[2] Batch[330] Speed: 1.2612233867212035 samples/sec batch loss = 0.6277299523353577 | accuracy = 0.7515151515151515 |
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| Epoch[2] Batch[360] Speed: 1.2615770404807842 samples/sec batch loss = 0.20681031048297882 | accuracy = 0.7555555555555555 |
| Epoch[2] Batch[365] Speed: 1.2573305994220416 samples/sec batch loss = 0.22308968007564545 | accuracy = 0.7534246575342466 |
| Epoch[2] Batch[370] Speed: 1.2538899834635437 samples/sec batch loss = 1.048492193222046 | accuracy = 0.7527027027027027 |
| Epoch[2] Batch[375] Speed: 1.259738765815799 samples/sec batch loss = 0.6793473958969116 | accuracy = 0.7486666666666667 |
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| Epoch[2] Batch[400] Speed: 1.2575662130781604 samples/sec batch loss = 0.5212681293487549 | accuracy = 0.75 |
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| Epoch[2] Batch[410] Speed: 1.2635104140550977 samples/sec batch loss = 0.4094090461730957 | accuracy = 0.75 |
| Epoch[2] Batch[415] Speed: 1.2628432447600622 samples/sec batch loss = 0.6479608416557312 | accuracy = 0.75 |
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| Epoch[2] Batch[435] Speed: 1.2593241291338189 samples/sec batch loss = 0.5339330434799194 | accuracy = 0.75 |
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| Epoch[2] Batch[450] Speed: 1.2537524281472427 samples/sec batch loss = 0.1473374217748642 | accuracy = 0.7555555555555555 |
| Epoch[2] Batch[455] Speed: 1.2540395669541768 samples/sec batch loss = 0.7678239345550537 | accuracy = 0.7538461538461538 |
| Epoch[2] Batch[460] Speed: 1.2548533410442082 samples/sec batch loss = 0.34159019589424133 | accuracy = 0.7554347826086957 |
| Epoch[2] Batch[465] Speed: 1.256998439954429 samples/sec batch loss = 0.030263230204582214 | accuracy = 0.7575268817204301 |
| Epoch[2] Batch[470] Speed: 1.25665572540617 samples/sec batch loss = 0.2530921995639801 | accuracy = 0.7569148936170212 |
| Epoch[2] Batch[475] Speed: 1.2563036020711633 samples/sec batch loss = 0.40490850806236267 | accuracy = 0.7563157894736842 |
| Epoch[2] Batch[480] Speed: 1.2604124110672055 samples/sec batch loss = 0.5530958771705627 | accuracy = 0.75625 |
| Epoch[2] Batch[485] Speed: 1.2538967308223234 samples/sec batch loss = 0.26859432458877563 | accuracy = 0.7572164948453608 |
| Epoch[2] Batch[490] Speed: 1.2577680628184722 samples/sec batch loss = 0.2706452012062073 | accuracy = 0.7556122448979592 |
| Epoch[2] Batch[495] Speed: 1.2594824815051315 samples/sec batch loss = 0.43799901008605957 | accuracy = 0.7570707070707071 |
| Epoch[2] Batch[500] Speed: 1.256651301467867 samples/sec batch loss = 0.497590035200119 | accuracy = 0.756 |
| Epoch[2] Batch[505] Speed: 1.257911310784143 samples/sec batch loss = 0.15296950936317444 | accuracy = 0.7564356435643564 |
| Epoch[2] Batch[510] Speed: 1.2562165897075128 samples/sec batch loss = 0.5471617579460144 | accuracy = 0.7558823529411764 |
| Epoch[2] Batch[515] Speed: 1.2562932540234637 samples/sec batch loss = 0.26938995718955994 | accuracy = 0.7567961165048543 |
| Epoch[2] Batch[520] Speed: 1.2568407115558111 samples/sec batch loss = 0.3936767280101776 | accuracy = 0.75625 |
| Epoch[2] Batch[525] Speed: 1.2485700830474096 samples/sec batch loss = 0.2390352338552475 | accuracy = 0.7571428571428571 |
| Epoch[2] Batch[530] Speed: 1.2546426677560987 samples/sec batch loss = 0.3204687237739563 | accuracy = 0.7566037735849057 |
| Epoch[2] Batch[535] Speed: 1.2620542988542511 samples/sec batch loss = 1.0941858291625977 | accuracy = 0.7574766355140187 |
| Epoch[2] Batch[540] Speed: 1.2566594904845803 samples/sec batch loss = 0.27888843417167664 | accuracy = 0.7574074074074074 |
| Epoch[2] Batch[545] Speed: 1.2520944904596285 samples/sec batch loss = 0.6460736393928528 | accuracy = 0.7568807339449541 |
| Epoch[2] Batch[550] Speed: 1.2556261648557479 samples/sec batch loss = 0.4329454302787781 | accuracy = 0.7563636363636363 |
| Epoch[2] Batch[555] Speed: 1.2517938578652896 samples/sec batch loss = 0.24152816832065582 | accuracy = 0.7572072072072072 |
| Epoch[2] Batch[560] Speed: 1.2573526491150682 samples/sec batch loss = 0.13197359442710876 | accuracy = 0.7575892857142857 |
| Epoch[2] Batch[565] Speed: 1.2615348268083189 samples/sec batch loss = 0.13147814571857452 | accuracy = 0.7588495575221239 |
| Epoch[2] Batch[570] Speed: 1.2606899147007011 samples/sec batch loss = 0.29318124055862427 | accuracy = 0.7592105263157894 |
| Epoch[2] Batch[575] Speed: 1.2582207396385803 samples/sec batch loss = 0.6722040176391602 | accuracy = 0.7591304347826087 |
| Epoch[2] Batch[580] Speed: 1.259415921388596 samples/sec batch loss = 0.1287207156419754 | accuracy = 0.7586206896551724 |
| Epoch[2] Batch[585] Speed: 1.2568243289051266 samples/sec batch loss = 0.5849408507347107 | accuracy = 0.7572649572649572 |
| Epoch[2] Batch[590] Speed: 1.2599224846369836 samples/sec batch loss = 0.4599897563457489 | accuracy = 0.7576271186440678 |
| Epoch[2] Batch[595] Speed: 1.2601727955052888 samples/sec batch loss = 0.6214470863342285 | accuracy = 0.7584033613445378 |
| Epoch[2] Batch[600] Speed: 1.25981321183655 samples/sec batch loss = 0.830505907535553 | accuracy = 0.7558333333333334 |
| Epoch[2] Batch[605] Speed: 1.2603399771583692 samples/sec batch loss = 0.2462722212076187 | accuracy = 0.7557851239669422 |
| Epoch[2] Batch[610] Speed: 1.2605341945457746 samples/sec batch loss = 0.3044840395450592 | accuracy = 0.7553278688524591 |
| Epoch[2] Batch[615] Speed: 1.26564370396947 samples/sec batch loss = 0.13677074015140533 | accuracy = 0.7556910569105691 |
| Epoch[2] Batch[620] Speed: 1.256666832452372 samples/sec batch loss = 0.5176256895065308 | accuracy = 0.7556451612903226 |
| Epoch[2] Batch[625] Speed: 1.259141340754744 samples/sec batch loss = 0.09790021926164627 | accuracy = 0.7564 |
| Epoch[2] Batch[630] Speed: 1.2583782483153272 samples/sec batch loss = 0.5038169622421265 | accuracy = 0.7563492063492063 |
| Epoch[2] Batch[635] Speed: 1.2565555826015316 samples/sec batch loss = 0.2728288769721985 | accuracy = 0.7562992125984253 |
| Epoch[2] Batch[640] Speed: 1.2595544386353683 samples/sec batch loss = 0.4914361536502838 | accuracy = 0.75703125 |
| Epoch[2] Batch[645] Speed: 1.25988634209773 samples/sec batch loss = 0.2587648332118988 | accuracy = 0.7581395348837209 |
| Epoch[2] Batch[650] Speed: 1.2584992613903894 samples/sec batch loss = 0.392534077167511 | accuracy = 0.7580769230769231 |
| Epoch[2] Batch[655] Speed: 1.2594024967631439 samples/sec batch loss = 0.7154847979545593 | accuracy = 0.7576335877862596 |
| Epoch[2] Batch[660] Speed: 1.2564657123781287 samples/sec batch loss = 0.4397771656513214 | accuracy = 0.7583333333333333 |
| Epoch[2] Batch[665] Speed: 1.26324545920418 samples/sec batch loss = 0.5614414811134338 | accuracy = 0.7567669172932331 |
| Epoch[2] Batch[670] Speed: 1.2613066373104842 samples/sec batch loss = 0.44550132751464844 | accuracy = 0.7567164179104477 |
| Epoch[2] Batch[675] Speed: 1.2569991933790854 samples/sec batch loss = 0.49533766508102417 | accuracy = 0.7570370370370371 |
| Epoch[2] Batch[680] Speed: 1.2579440388938663 samples/sec batch loss = 0.43000584840774536 | accuracy = 0.7573529411764706 |
| Epoch[2] Batch[685] Speed: 1.2589243130370262 samples/sec batch loss = 0.5172191262245178 | accuracy = 0.7583941605839416 |
| Epoch[2] Batch[690] Speed: 1.2579596961710666 samples/sec batch loss = 0.12420417368412018 | accuracy = 0.758695652173913 |
| Epoch[2] Batch[695] Speed: 1.2625950076024144 samples/sec batch loss = 0.1800249218940735 | accuracy = 0.7586330935251798 |
| Epoch[2] Batch[700] Speed: 1.260750356608082 samples/sec batch loss = 1.2589502334594727 | accuracy = 0.7592857142857142 |
| Epoch[2] Batch[705] Speed: 1.2623414541140614 samples/sec batch loss = 0.4674840271472931 | accuracy = 0.7599290780141844 |
| Epoch[2] Batch[710] Speed: 1.2616016110874533 samples/sec batch loss = 0.27949342131614685 | accuracy = 0.7612676056338028 |
| Epoch[2] Batch[715] Speed: 1.2564834971925891 samples/sec batch loss = 0.5487675070762634 | accuracy = 0.7611888111888112 |
| Epoch[2] Batch[720] Speed: 1.2654313012097282 samples/sec batch loss = 0.47928252816200256 | accuracy = 0.7607638888888889 |
| Epoch[2] Batch[725] Speed: 1.2596535468022052 samples/sec batch loss = 0.36400875449180603 | accuracy = 0.7610344827586207 |
| Epoch[2] Batch[730] Speed: 1.266866141839307 samples/sec batch loss = 0.11860589683055878 | accuracy = 0.761986301369863 |
| Epoch[2] Batch[735] Speed: 1.2615857681542464 samples/sec batch loss = 0.8347565531730652 | accuracy = 0.7619047619047619 |
| Epoch[2] Batch[740] Speed: 1.2632639120634466 samples/sec batch loss = 0.12294736504554749 | accuracy = 0.7618243243243243 |
| Epoch[2] Batch[745] Speed: 1.2538205461152578 samples/sec batch loss = 0.5129509568214417 | accuracy = 0.7624161073825504 |
| Epoch[2] Batch[750] Speed: 1.2614979277322955 samples/sec batch loss = 0.47692441940307617 | accuracy = 0.7623333333333333 |
| Epoch[2] Batch[755] Speed: 1.256926303726783 samples/sec batch loss = 0.18672022223472595 | accuracy = 0.7625827814569537 |
| Epoch[2] Batch[760] Speed: 1.2574401016671866 samples/sec batch loss = 0.315335214138031 | accuracy = 0.7634868421052632 |
| Epoch[2] Batch[765] Speed: 1.2511710980736375 samples/sec batch loss = 0.4859278202056885 | accuracy = 0.7627450980392156 |
| Epoch[2] Batch[770] Speed: 1.2538648689329364 samples/sec batch loss = 0.5746912360191345 | accuracy = 0.7623376623376623 |
| Epoch[2] Batch[775] Speed: 1.2568914628224277 samples/sec batch loss = 0.8297697901725769 | accuracy = 0.762258064516129 |
| Epoch[2] Batch[780] Speed: 1.2534043643938828 samples/sec batch loss = 0.664438009262085 | accuracy = 0.7612179487179487 |
| Epoch[2] Batch[785] Speed: 1.2519160366685718 samples/sec batch loss = 0.4396113455295563 | accuracy = 0.7627388535031847 |
| [Epoch 2] training: accuracy=0.7636421319796954 |
| [Epoch 2] time cost: 643.4995687007904 |
| [Epoch 2] validation: validation accuracy=0.78 |
| </pre></div></div> |
| </div> |
| </div> |
| <div class="section" id="5.-Test-on-the-test-set"> |
| <h2>5. Test on the test set<a class="headerlink" href="#5.-Test-on-the-test-set" title="Permalink to this headline">¶</a></h2> |
| <p>Now that your network is trained and has reached a decent accuracy, you can evaluate the performance on the test set. For that, you can use the <code class="docutils literal notranslate"><span class="pre">test_loader</span></code> data loader and the test function you created previously.</p> |
| <div class="nbinput 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="n">test</span><span class="p">(</span><span class="n">test_loader</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>[17]: |
| </pre></div> |
| </div> |
| <div class="output_area highlight-none notranslate"><div class="highlight"><pre> |
| <span></span>0.7622222222222222 |
| </pre></div> |
| </div> |
| </div> |
| <p>You have a trained network that can confidently discriminate between plants that are healthy and the ones that are diseased. You can now start your garden and set cameras to automatically detect plants in distress! Or change your classification problem to create a model that classify the species of the plants! Either way you might be able to impress your botanist friends.</p> |
| </div> |
| <div class="section" id="6.-Save-the-parameters"> |
| <h2>6. Save the parameters<a class="headerlink" href="#6.-Save-the-parameters" title="Permalink to this headline">¶</a></h2> |
| <p>If you want to preserve the trained weights of the network you can save the parameters in a file. Later, when you want to use the network to make predictions you can load the parameters back!</p> |
| <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="c1"># Save parameters in the</span> |
| <span class="n">model</span><span class="o">.</span><span class="n">save_parameters</span><span class="p">(</span><span class="s1">'leaf_models.params'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>This is the end of this tutorial, to see how you can speed up the training by using GPU hardware continue to the <a class="reference internal" href="7-use-gpus.html"><span class="doc">next tutorial</span></a></p> |
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| <p class="caption"> |
| <span class="caption-text">Table Of Contents</span> |
| </p> |
| <ul> |
| <li><a class="reference internal" href="#">Step 6: Train a Neural Network</a><ul> |
| <li><a class="reference internal" href="#1.-Data-preparation">1. Data preparation</a><ul> |
| <li><a class="reference internal" href="#Data-inspection">Data inspection</a></li> |
| </ul> |
| </li> |
| <li><a class="reference internal" href="#2.-Create-Neural-Network">2. Create Neural Network</a></li> |
| <li><a class="reference internal" href="#3.-Choose-Optimizer-and-Loss-function">3. Choose Optimizer and Loss function</a></li> |
| <li><a class="reference internal" href="#4.-Training-Loop">4. Training Loop</a></li> |
| <li><a class="reference internal" href="#5.-Test-on-the-test-set">5. Test on the test set</a></li> |
| <li><a class="reference internal" href="#6.-Save-the-parameters">6. Save the parameters</a></li> |
| </ul> |
| </li> |
| </ul> |
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| <div>Step 5: Datasets and DataLoader</div> |
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| <div>Step 7: Load and Run a NN using GPU</div> |
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