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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> |
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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-l4"><a class="reference internal" href="../../packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
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| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
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| <li class="toctree-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://prod-dcd-datasets-cache-zipfiles.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"> |
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| <div class="output_area docutils container"> |
| <div class="highlight"><pre> |
| Downloading hb74ynkjcn-1.zip from https://prod-dcd-datasets-cache-zipfiles.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> |
| [03:58:42] /work/mxnet/src/storage/storage.cc:202: Using Pooled (Naive) StorageManager for CPU |
| </pre></div></div> |
| </div> |
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| 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> |
| [03:58:49] /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="w"> </span><span class="o">+</span><span class="w"> </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="w"> </span><span class="o">+</span><span class="w"> </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="w"> </span><span class="o">/</span><span class="w"> </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="w"> </span><span class="o">-</span><span class="w"> </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="w"> </span><span class="o">+</span><span class="w"> </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="w"> </span><span class="o">+</span><span class="w"> </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="w"> </span><span class="o">-</span><span class="w"> </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="w"> </span><span class="o">+</span><span class="w"> </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> |
| [03:58:51] /work/mxnet/src/operator/cudnn_ops.cc:421: Auto-tuning cuDNN op, set MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable |
| [03:58:52] /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: 1.2117561585375851 samples/sec batch loss = 0.8407745957374573 | accuracy = 0.45 |
| Epoch[1] Batch[10] Speed: 1.2600317763951179 samples/sec batch loss = 0.6950667500495911 | accuracy = 0.6 |
| Epoch[1] Batch[15] Speed: 1.262876134951749 samples/sec batch loss = 0.16495956480503082 | accuracy = 0.6166666666666667 |
| Epoch[1] Batch[20] Speed: 1.2658993500361686 samples/sec batch loss = 0.9710271954536438 | accuracy = 0.5875 |
| Epoch[1] Batch[25] Speed: 1.2636251830319851 samples/sec batch loss = 0.8496341109275818 | accuracy = 0.6 |
| Epoch[1] Batch[30] Speed: 1.2626422335708292 samples/sec batch loss = 0.6572840809822083 | accuracy = 0.575 |
| Epoch[1] Batch[35] Speed: 1.262363394957624 samples/sec batch loss = 0.5903766751289368 | accuracy = 0.5428571428571428 |
| Epoch[1] Batch[40] Speed: 1.2612925085888125 samples/sec batch loss = 0.5169366002082825 | accuracy = 0.58125 |
| Epoch[1] Batch[45] Speed: 1.2586043407599812 samples/sec batch loss = 0.7783154249191284 | accuracy = 0.6 |
| Epoch[1] Batch[50] Speed: 1.2591356708160246 samples/sec batch loss = 0.8414345979690552 | accuracy = 0.59 |
| Epoch[1] Batch[55] Speed: 1.2622587316453076 samples/sec batch loss = 0.39347484707832336 | accuracy = 0.6045454545454545 |
| Epoch[1] Batch[60] Speed: 1.259024455843083 samples/sec batch loss = 0.6124871373176575 | accuracy = 0.6125 |
| Epoch[1] Batch[65] Speed: 1.2627991403712975 samples/sec batch loss = 0.6423971056938171 | accuracy = 0.6269230769230769 |
| Epoch[1] Batch[70] Speed: 1.2625957677509934 samples/sec batch loss = 0.6456457376480103 | accuracy = 0.6285714285714286 |
| Epoch[1] Batch[75] Speed: 1.2569808289103817 samples/sec batch loss = 0.48864680528640747 | accuracy = 0.63 |
| Epoch[1] Batch[80] Speed: 1.2614926159607853 samples/sec batch loss = 0.8163663148880005 | accuracy = 0.6375 |
| Epoch[1] Batch[85] Speed: 1.2603451845472566 samples/sec batch loss = 0.7628596425056458 | accuracy = 0.6352941176470588 |
| Epoch[1] Batch[90] Speed: 1.2587507069455706 samples/sec batch loss = 0.21568706631660461 | accuracy = 0.6361111111111111 |
| Epoch[1] Batch[95] Speed: 1.259375931974608 samples/sec batch loss = 0.7214741706848145 | accuracy = 0.6263157894736842 |
| Epoch[1] Batch[100] Speed: 1.2595401600226785 samples/sec batch loss = 0.28124821186065674 | accuracy = 0.6325 |
| Epoch[1] Batch[105] Speed: 1.2647009891708403 samples/sec batch loss = 0.662185788154602 | accuracy = 0.6309523809523809 |
| Epoch[1] Batch[110] Speed: 1.2561870552777334 samples/sec batch loss = 0.8813420534133911 | accuracy = 0.625 |
| Epoch[1] Batch[115] Speed: 1.2627784199764653 samples/sec batch loss = 1.1359597444534302 | accuracy = 0.6217391304347826 |
| Epoch[1] Batch[120] Speed: 1.263076269717103 samples/sec batch loss = 0.4332994520664215 | accuracy = 0.6291666666666667 |
| Epoch[1] Batch[125] Speed: 1.2575660245519422 samples/sec batch loss = 0.481771856546402 | accuracy = 0.63 |
| Epoch[1] Batch[130] Speed: 1.2607005247728935 samples/sec batch loss = 0.9104111194610596 | accuracy = 0.6230769230769231 |
| Epoch[1] Batch[135] Speed: 1.2556771940013876 samples/sec batch loss = 0.3003590703010559 | accuracy = 0.6259259259259259 |
| Epoch[1] Batch[140] Speed: 1.257888958558277 samples/sec batch loss = 0.44806337356567383 | accuracy = 0.6303571428571428 |
| Epoch[1] Batch[145] Speed: 1.2650251184593269 samples/sec batch loss = 0.9731339812278748 | accuracy = 0.6275862068965518 |
| Epoch[1] Batch[150] Speed: 1.2658260931105452 samples/sec batch loss = 0.7989111542701721 | accuracy = 0.625 |
| Epoch[1] Batch[155] Speed: 1.2566912121659506 samples/sec batch loss = 0.5889948010444641 | accuracy = 0.6241935483870967 |
| Epoch[1] Batch[160] Speed: 1.2580514782908117 samples/sec batch loss = 1.0139918327331543 | accuracy = 0.625 |
| Epoch[1] Batch[165] Speed: 1.2601242397253165 samples/sec batch loss = 0.45098966360092163 | accuracy = 0.6272727272727273 |
| Epoch[1] Batch[170] Speed: 1.2619692409018395 samples/sec batch loss = 1.2195454835891724 | accuracy = 0.6294117647058823 |
| Epoch[1] Batch[175] Speed: 1.257507866912004 samples/sec batch loss = 0.5146313905715942 | accuracy = 0.6342857142857142 |
| Epoch[1] Batch[180] Speed: 1.2595949122240246 samples/sec batch loss = 0.45457881689071655 | accuracy = 0.6388888888888888 |
| Epoch[1] Batch[185] Speed: 1.2587306858189538 samples/sec batch loss = 0.5231857895851135 | accuracy = 0.6364864864864865 |
| Epoch[1] Batch[190] Speed: 1.2630439396164814 samples/sec batch loss = 0.350179523229599 | accuracy = 0.6394736842105263 |
| Epoch[1] Batch[195] Speed: 1.2577441127545546 samples/sec batch loss = 1.007402777671814 | accuracy = 0.6397435897435897 |
| Epoch[1] Batch[200] Speed: 1.2592403839203896 samples/sec batch loss = 0.9171308279037476 | accuracy = 0.64 |
| Epoch[1] Batch[205] Speed: 1.2584326163652642 samples/sec batch loss = 0.8950124382972717 | accuracy = 0.6414634146341464 |
| Epoch[1] Batch[210] Speed: 1.2561466122924982 samples/sec batch loss = 0.28655946254730225 | accuracy = 0.638095238095238 |
| Epoch[1] Batch[215] Speed: 1.25593052159335 samples/sec batch loss = 0.6185159087181091 | accuracy = 0.6395348837209303 |
| Epoch[1] Batch[220] Speed: 1.2547902724004891 samples/sec batch loss = 0.6935330033302307 | accuracy = 0.6363636363636364 |
| Epoch[1] Batch[225] Speed: 1.2614158848236938 samples/sec batch loss = 0.5271984338760376 | accuracy = 0.6344444444444445 |
| Epoch[1] Batch[230] Speed: 1.259850769267276 samples/sec batch loss = 0.5704729557037354 | accuracy = 0.6347826086956522 |
| Epoch[1] Batch[235] Speed: 1.2586212419551717 samples/sec batch loss = 0.3812594413757324 | accuracy = 0.6361702127659574 |
| Epoch[1] Batch[240] Speed: 1.2599345956898882 samples/sec batch loss = 0.7227175831794739 | accuracy = 0.6385416666666667 |
| Epoch[1] Batch[245] Speed: 1.2605096654985357 samples/sec batch loss = 0.48598140478134155 | accuracy = 0.6428571428571429 |
| Epoch[1] Batch[250] Speed: 1.261392933689118 samples/sec batch loss = 0.3495320975780487 | accuracy = 0.645 |
| Epoch[1] Batch[255] Speed: 1.2570650273457014 samples/sec batch loss = 0.16238412261009216 | accuracy = 0.6460784313725491 |
| Epoch[1] Batch[260] Speed: 1.2601780961678446 samples/sec batch loss = 0.282853901386261 | accuracy = 0.6519230769230769 |
| Epoch[1] Batch[265] Speed: 1.2592809318483862 samples/sec batch loss = 0.5361605882644653 | accuracy = 0.6528301886792452 |
| Epoch[1] Batch[270] Speed: 1.2609684882943466 samples/sec batch loss = 0.4548198878765106 | accuracy = 0.6555555555555556 |
| Epoch[1] Batch[275] Speed: 1.2589706034861714 samples/sec batch loss = 0.8159052133560181 | accuracy = 0.6554545454545454 |
| Epoch[1] Batch[280] Speed: 1.264306231720377 samples/sec batch loss = 0.7456441521644592 | accuracy = 0.65625 |
| Epoch[1] Batch[285] Speed: 1.2561571460285408 samples/sec batch loss = 0.8854629397392273 | accuracy = 0.6578947368421053 |
| Epoch[1] Batch[290] Speed: 1.2583954266012083 samples/sec batch loss = 0.9764561653137207 | accuracy = 0.653448275862069 |
| Epoch[1] Batch[295] Speed: 1.2620161352744728 samples/sec batch loss = 0.6067806482315063 | accuracy = 0.652542372881356 |
| Epoch[1] Batch[300] Speed: 1.2585989589104276 samples/sec batch loss = 0.6901727914810181 | accuracy = 0.6525 |
| Epoch[1] Batch[305] Speed: 1.2596041799070306 samples/sec batch loss = 0.8663436770439148 | accuracy = 0.6516393442622951 |
| Epoch[1] Batch[310] Speed: 1.262342023996454 samples/sec batch loss = 0.3564245402812958 | accuracy = 0.6548387096774193 |
| Epoch[1] Batch[315] Speed: 1.2609800508113476 samples/sec batch loss = 0.4036303162574768 | accuracy = 0.6547619047619048 |
| Epoch[1] Batch[320] Speed: 1.258629456666479 samples/sec batch loss = 0.8869490623474121 | accuracy = 0.65625 |
| Epoch[1] Batch[325] Speed: 1.2576116495451684 samples/sec batch loss = 0.40886107087135315 | accuracy = 0.6576923076923077 |
| Epoch[1] Batch[330] Speed: 1.2596941213429396 samples/sec batch loss = 0.35565879940986633 | accuracy = 0.6568181818181819 |
| Epoch[1] Batch[335] Speed: 1.2696119488644395 samples/sec batch loss = 2.598857879638672 | accuracy = 0.6582089552238806 |
| Epoch[1] Batch[340] Speed: 1.2640666573541182 samples/sec batch loss = 0.4242265224456787 | accuracy = 0.6602941176470588 |
| Epoch[1] Batch[345] Speed: 1.2628317431031209 samples/sec batch loss = 0.5970653295516968 | accuracy = 0.6594202898550725 |
| Epoch[1] Batch[350] Speed: 1.2630304375350017 samples/sec batch loss = 0.9351227879524231 | accuracy = 0.6585714285714286 |
| Epoch[1] Batch[355] Speed: 1.2608853770969737 samples/sec batch loss = 0.2976606786251068 | accuracy = 0.6612676056338028 |
| Epoch[1] Batch[360] Speed: 1.2636916177206194 samples/sec batch loss = 0.604040801525116 | accuracy = 0.6604166666666667 |
| Epoch[1] Batch[365] Speed: 1.2661687639274317 samples/sec batch loss = 0.6232295632362366 | accuracy = 0.6616438356164384 |
| Epoch[1] Batch[370] Speed: 1.2707490839506919 samples/sec batch loss = 0.1412576287984848 | accuracy = 0.6648648648648648 |
| Epoch[1] Batch[375] Speed: 1.266353120553725 samples/sec batch loss = 0.45004159212112427 | accuracy = 0.664 |
| Epoch[1] Batch[380] Speed: 1.264753139980445 samples/sec batch loss = 0.5365153551101685 | accuracy = 0.6644736842105263 |
| Epoch[1] Batch[385] Speed: 1.2641402822755679 samples/sec batch loss = 0.4807123839855194 | accuracy = 0.6649350649350649 |
| Epoch[1] Batch[390] Speed: 1.265913486637907 samples/sec batch loss = 0.24441587924957275 | accuracy = 0.6666666666666666 |
| Epoch[1] Batch[395] Speed: 1.2643907474045544 samples/sec batch loss = 0.5377245545387268 | accuracy = 0.6670886075949367 |
| Epoch[1] Batch[400] Speed: 1.2631496842091157 samples/sec batch loss = 0.48492205142974854 | accuracy = 0.66625 |
| Epoch[1] Batch[405] Speed: 1.2696088743889655 samples/sec batch loss = 0.8884551525115967 | accuracy = 0.6641975308641975 |
| Epoch[1] Batch[410] Speed: 1.2613111889123927 samples/sec batch loss = 0.8871831893920898 | accuracy = 0.6646341463414634 |
| Epoch[1] Batch[415] Speed: 1.2609069830590305 samples/sec batch loss = 0.7476733922958374 | accuracy = 0.6650602409638554 |
| Epoch[1] Batch[420] Speed: 1.267477241306368 samples/sec batch loss = 0.502937912940979 | accuracy = 0.6648809523809524 |
| Epoch[1] Batch[425] Speed: 1.2642218225975328 samples/sec batch loss = 0.4086722433567047 | accuracy = 0.6664705882352941 |
| Epoch[1] Batch[430] Speed: 1.2626343464933505 samples/sec batch loss = 0.4604116976261139 | accuracy = 0.6662790697674419 |
| Epoch[1] Batch[435] Speed: 1.2690874858783026 samples/sec batch loss = 0.9654018878936768 | accuracy = 0.6655172413793103 |
| Epoch[1] Batch[440] Speed: 1.2662694892003932 samples/sec batch loss = 0.45917895436286926 | accuracy = 0.6659090909090909 |
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| Epoch[1] Batch[600] Speed: 1.2575934557109352 samples/sec batch loss = 0.2279791533946991 | accuracy = 0.6854166666666667 |
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| Epoch[1] Batch[720] Speed: 1.2660933738067468 samples/sec batch loss = 0.3373050391674042 | accuracy = 0.6954861111111111 |
| Epoch[1] Batch[725] Speed: 1.2608137415731089 samples/sec batch loss = 0.3431106209754944 | accuracy = 0.6951724137931035 |
| Epoch[1] Batch[730] Speed: 1.265837171840635 samples/sec batch loss = 0.45366260409355164 | accuracy = 0.6958904109589041 |
| Epoch[1] Batch[735] Speed: 1.2657961051071804 samples/sec batch loss = 0.5519353747367859 | accuracy = 0.6965986394557823 |
| Epoch[1] Batch[740] Speed: 1.2599134015001014 samples/sec batch loss = 0.3560154139995575 | accuracy = 0.6972972972972973 |
| Epoch[1] Batch[745] Speed: 1.261615936477399 samples/sec batch loss = 0.5393974184989929 | accuracy = 0.696979865771812 |
| Epoch[1] Batch[750] Speed: 1.2618694832937762 samples/sec batch loss = 0.529081404209137 | accuracy = 0.6956666666666667 |
| Epoch[1] Batch[755] Speed: 1.2665639205676777 samples/sec batch loss = 0.328561395406723 | accuracy = 0.6963576158940398 |
| Epoch[1] Batch[760] Speed: 1.262263290121354 samples/sec batch loss = 0.5322239398956299 | accuracy = 0.6967105263157894 |
| Epoch[1] Batch[765] Speed: 1.2613233266780846 samples/sec batch loss = 0.4723161458969116 | accuracy = 0.6964052287581699 |
| Epoch[1] Batch[770] Speed: 1.2640637049164642 samples/sec batch loss = 0.37635189294815063 | accuracy = 0.6957792207792208 |
| Epoch[1] Batch[775] Speed: 1.267898414962233 samples/sec batch loss = 0.7176620960235596 | accuracy = 0.6958064516129032 |
| Epoch[1] Batch[780] Speed: 1.2670247700644348 samples/sec batch loss = 0.5387542843818665 | accuracy = 0.6955128205128205 |
| Epoch[1] Batch[785] Speed: 1.2694301000345254 samples/sec batch loss = 0.9078068733215332 | accuracy = 0.6945859872611465 |
| [Epoch 1] training: accuracy=0.694479695431472 |
| [Epoch 1] time cost: 643.0942394733429 |
| [Epoch 1] validation: validation accuracy=0.7644444444444445 |
| Epoch[2] Batch[5] Speed: 1.2679289817762778 samples/sec batch loss = 0.5397403240203857 | accuracy = 0.75 |
| Epoch[2] Batch[10] Speed: 1.272866250465929 samples/sec batch loss = 0.948280394077301 | accuracy = 0.75 |
| Epoch[2] Batch[15] Speed: 1.268499193560338 samples/sec batch loss = 0.5206238627433777 | accuracy = 0.75 |
| Epoch[2] Batch[20] Speed: 1.2710240327799824 samples/sec batch loss = 0.6208543181419373 | accuracy = 0.7875 |
| Epoch[2] Batch[25] Speed: 1.2755901476774818 samples/sec batch loss = 0.10611335933208466 | accuracy = 0.8 |
| Epoch[2] Batch[30] Speed: 1.2712236762645583 samples/sec batch loss = 0.2262563407421112 | accuracy = 0.7916666666666666 |
| Epoch[2] Batch[35] Speed: 1.2711524985284233 samples/sec batch loss = 0.6930609345436096 | accuracy = 0.8142857142857143 |
| Epoch[2] Batch[40] Speed: 1.264101420963711 samples/sec batch loss = 0.3380635380744934 | accuracy = 0.825 |
| Epoch[2] Batch[45] Speed: 1.266135606456582 samples/sec batch loss = 0.4632367789745331 | accuracy = 0.8111111111111111 |
| Epoch[2] Batch[50] Speed: 1.2734662375633319 samples/sec batch loss = 0.8806115388870239 | accuracy = 0.81 |
| Epoch[2] Batch[55] Speed: 1.2723326311085987 samples/sec batch loss = 0.7208840847015381 | accuracy = 0.8 |
| Epoch[2] Batch[60] Speed: 1.2696818011162188 samples/sec batch loss = 0.07581078261137009 | accuracy = 0.8 |
| Epoch[2] Batch[65] Speed: 1.2702790777121855 samples/sec batch loss = 0.21203313767910004 | accuracy = 0.8 |
| Epoch[2] Batch[70] Speed: 1.270955476980942 samples/sec batch loss = 0.5215021371841431 | accuracy = 0.8 |
| Epoch[2] Batch[75] Speed: 1.2712049901332667 samples/sec batch loss = 1.070705771446228 | accuracy = 0.79 |
| Epoch[2] Batch[80] Speed: 1.265463276392116 samples/sec batch loss = 0.47908130288124084 | accuracy = 0.790625 |
| Epoch[2] Batch[85] Speed: 1.2663037050360229 samples/sec batch loss = 0.28132444620132446 | accuracy = 0.7852941176470588 |
| Epoch[2] Batch[90] Speed: 1.2675077878160201 samples/sec batch loss = 0.3856823742389679 | accuracy = 0.7833333333333333 |
| Epoch[2] Batch[95] Speed: 1.2655835557272783 samples/sec batch loss = 0.3750905990600586 | accuracy = 0.7868421052631579 |
| Epoch[2] Batch[100] Speed: 1.2727139765969901 samples/sec batch loss = 0.9113474488258362 | accuracy = 0.79 |
| Epoch[2] Batch[105] Speed: 1.2663812231679639 samples/sec batch loss = 0.4197746217250824 | accuracy = 0.7904761904761904 |
| Epoch[2] Batch[110] Speed: 1.275478528264064 samples/sec batch loss = 0.5012903213500977 | accuracy = 0.7909090909090909 |
| Epoch[2] Batch[115] Speed: 1.2657278256234725 samples/sec batch loss = 0.6082658767700195 | accuracy = 0.7891304347826087 |
| Epoch[2] Batch[120] Speed: 1.2717082626469367 samples/sec batch loss = 0.3968459367752075 | accuracy = 0.7875 |
| Epoch[2] Batch[125] Speed: 1.2674187378388255 samples/sec batch loss = 0.2526654005050659 | accuracy = 0.79 |
| Epoch[2] Batch[130] Speed: 1.269791062759885 samples/sec batch loss = 0.9633570313453674 | accuracy = 0.7846153846153846 |
| Epoch[2] Batch[135] Speed: 1.272482207890896 samples/sec batch loss = 0.6146144270896912 | accuracy = 0.7796296296296297 |
| Epoch[2] Batch[140] Speed: 1.267061227640173 samples/sec batch loss = 1.8042162656784058 | accuracy = 0.7767857142857143 |
| Epoch[2] Batch[145] Speed: 1.2666973200904503 samples/sec batch loss = 0.586370587348938 | accuracy = 0.7741379310344828 |
| Epoch[2] Batch[150] Speed: 1.2686924801465453 samples/sec batch loss = 0.32586824893951416 | accuracy = 0.7716666666666666 |
| Epoch[2] Batch[155] Speed: 1.2678804971702227 samples/sec batch loss = 0.24084599316120148 | accuracy = 0.7693548387096775 |
| Epoch[2] Batch[160] Speed: 1.266468502163054 samples/sec batch loss = 0.25556641817092896 | accuracy = 0.7703125 |
| Epoch[2] Batch[165] Speed: 1.2721240543452406 samples/sec batch loss = 0.7530019879341125 | accuracy = 0.7712121212121212 |
| Epoch[2] Batch[170] Speed: 1.2666229188982205 samples/sec batch loss = 0.318692147731781 | accuracy = 0.7691176470588236 |
| Epoch[2] Batch[175] Speed: 1.2728078278380586 samples/sec batch loss = 0.3775341212749481 | accuracy = 0.7714285714285715 |
| Epoch[2] Batch[180] Speed: 1.2651290008520302 samples/sec batch loss = 0.4126252830028534 | accuracy = 0.7694444444444445 |
| Epoch[2] Batch[185] Speed: 1.2677455072008634 samples/sec batch loss = 0.8102496266365051 | accuracy = 0.7662162162162162 |
| Epoch[2] Batch[190] Speed: 1.2713236660328695 samples/sec batch loss = 0.5861036777496338 | accuracy = 0.7671052631578947 |
| Epoch[2] Batch[195] Speed: 1.269096125777857 samples/sec batch loss = 0.17301079630851746 | accuracy = 0.7692307692307693 |
| Epoch[2] Batch[200] Speed: 1.267019985761407 samples/sec batch loss = 0.261074036359787 | accuracy = 0.76875 |
| Epoch[2] Batch[205] Speed: 1.268189003007044 samples/sec batch loss = 0.5001844763755798 | accuracy = 0.7695121951219512 |
| Epoch[2] Batch[210] Speed: 1.2661288222889537 samples/sec batch loss = 0.29652106761932373 | accuracy = 0.7714285714285715 |
| Epoch[2] Batch[215] Speed: 1.274298652961303 samples/sec batch loss = 0.6518102288246155 | accuracy = 0.7674418604651163 |
| Epoch[2] Batch[220] Speed: 1.2708700814355367 samples/sec batch loss = 0.34153062105178833 | accuracy = 0.7693181818181818 |
| Epoch[2] Batch[225] Speed: 1.2661095212519449 samples/sec batch loss = 0.31635040044784546 | accuracy = 0.7711111111111111 |
| Epoch[2] Batch[230] Speed: 1.2743250766962615 samples/sec batch loss = 0.6876500248908997 | accuracy = 0.7695652173913043 |
| Epoch[2] Batch[235] Speed: 1.2695987863709035 samples/sec batch loss = 0.6100889444351196 | accuracy = 0.7659574468085106 |
| Epoch[2] Batch[240] Speed: 1.2706704527518442 samples/sec batch loss = 0.19952930510044098 | accuracy = 0.7677083333333333 |
| Epoch[2] Batch[245] Speed: 1.2651874839374462 samples/sec batch loss = 0.45317843556404114 | accuracy = 0.7663265306122449 |
| Epoch[2] Batch[250] Speed: 1.2718971287502363 samples/sec batch loss = 0.6451046466827393 | accuracy = 0.768 |
| Epoch[2] Batch[255] Speed: 1.268740163363412 samples/sec batch loss = 0.25341954827308655 | accuracy = 0.7676470588235295 |
| Epoch[2] Batch[260] Speed: 1.267148791805761 samples/sec batch loss = 0.638837456703186 | accuracy = 0.7673076923076924 |
| Epoch[2] Batch[265] Speed: 1.2650846412693846 samples/sec batch loss = 0.35085564851760864 | accuracy = 0.7669811320754717 |
| Epoch[2] Batch[270] Speed: 1.2684251559408741 samples/sec batch loss = 0.868017852306366 | accuracy = 0.7666666666666667 |
| Epoch[2] Batch[275] Speed: 1.2658817752418383 samples/sec batch loss = 0.8266984820365906 | accuracy = 0.7654545454545455 |
| Epoch[2] Batch[280] Speed: 1.2733551828159906 samples/sec batch loss = 0.4120587408542633 | accuracy = 0.7669642857142858 |
| Epoch[2] Batch[285] Speed: 1.270725214455315 samples/sec batch loss = 1.014793872833252 | accuracy = 0.7675438596491229 |
| Epoch[2] Batch[290] Speed: 1.2671106065995847 samples/sec batch loss = 0.44852501153945923 | accuracy = 0.7681034482758621 |
| Epoch[2] Batch[295] Speed: 1.2704668460659576 samples/sec batch loss = 0.420880526304245 | accuracy = 0.7677966101694915 |
| Epoch[2] Batch[300] Speed: 1.2684345540198583 samples/sec batch loss = 0.654721200466156 | accuracy = 0.7641666666666667 |
| Epoch[2] Batch[305] Speed: 1.266592032538849 samples/sec batch loss = 0.5563826560974121 | accuracy = 0.7655737704918033 |
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| Epoch[2] Batch[320] Speed: 1.2721365940313851 samples/sec batch loss = 0.704727053642273 | accuracy = 0.7671875 |
| Epoch[2] Batch[325] Speed: 1.2688555965325603 samples/sec batch loss = 0.4216771423816681 | accuracy = 0.7676923076923077 |
| Epoch[2] Batch[330] Speed: 1.2693178274328 samples/sec batch loss = 0.451947420835495 | accuracy = 0.7681818181818182 |
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| Epoch[2] Batch[360] Speed: 1.2690933417973767 samples/sec batch loss = 0.5668888688087463 | accuracy = 0.7708333333333334 |
| Epoch[2] Batch[365] Speed: 1.2674707300150612 samples/sec batch loss = 0.36127129197120667 | accuracy = 0.7719178082191781 |
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| Epoch[2] Batch[400] Speed: 1.2701309799558544 samples/sec batch loss = 0.40795236825942993 | accuracy = 0.773125 |
| Epoch[2] Batch[405] Speed: 1.2662110016070265 samples/sec batch loss = 0.3847126066684723 | accuracy = 0.7734567901234568 |
| Epoch[2] Batch[410] Speed: 1.2687582973584197 samples/sec batch loss = 0.05686186999082565 | accuracy = 0.775 |
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| Epoch[2] Batch[445] Speed: 1.265879100859394 samples/sec batch loss = 0.25030258297920227 | accuracy = 0.7713483146067416 |
| Epoch[2] Batch[450] Speed: 1.2658895119118239 samples/sec batch loss = 0.33114463090896606 | accuracy = 0.77 |
| Epoch[2] Batch[455] Speed: 1.266133599864219 samples/sec batch loss = 0.41308751702308655 | accuracy = 0.7697802197802198 |
| Epoch[2] Batch[460] Speed: 1.2679493924131546 samples/sec batch loss = 0.3204061686992645 | accuracy = 0.7711956521739131 |
| Epoch[2] Batch[465] Speed: 1.2654435184020967 samples/sec batch loss = 0.6681140065193176 | accuracy = 0.7709677419354839 |
| Epoch[2] Batch[470] Speed: 1.2683076917029161 samples/sec batch loss = 0.32782962918281555 | accuracy = 0.7712765957446809 |
| Epoch[2] Batch[475] Speed: 1.2632513564511052 samples/sec batch loss = 0.6342236399650574 | accuracy = 0.7721052631578947 |
| Epoch[2] Batch[480] Speed: 1.2711998852547939 samples/sec batch loss = 0.23498670756816864 | accuracy = 0.7729166666666667 |
| Epoch[2] Batch[485] Speed: 1.266350348594733 samples/sec batch loss = 0.33923354744911194 | accuracy = 0.7716494845360825 |
| Epoch[2] Batch[490] Speed: 1.2670994098702277 samples/sec batch loss = 0.6169783473014832 | accuracy = 0.7704081632653061 |
| Epoch[2] Batch[495] Speed: 1.2624103186286535 samples/sec batch loss = 0.3935692608356476 | accuracy = 0.7717171717171717 |
| Epoch[2] Batch[500] Speed: 1.265530954570083 samples/sec batch loss = 0.22158144414424896 | accuracy = 0.772 |
| Epoch[2] Batch[505] Speed: 1.2690746222458194 samples/sec batch loss = 0.27750110626220703 | accuracy = 0.7722772277227723 |
| Epoch[2] Batch[510] Speed: 1.2684186349066855 samples/sec batch loss = 0.3431386649608612 | accuracy = 0.7725490196078432 |
| Epoch[2] Batch[515] Speed: 1.2664063636383536 samples/sec batch loss = 0.5514299273490906 | accuracy = 0.7733009708737864 |
| Epoch[2] Batch[520] Speed: 1.2738899505978216 samples/sec batch loss = 0.28555697202682495 | accuracy = 0.7740384615384616 |
| Epoch[2] Batch[525] Speed: 1.2643614943615418 samples/sec batch loss = 0.5617321729660034 | accuracy = 0.7738095238095238 |
| Epoch[2] Batch[530] Speed: 1.2669030686267202 samples/sec batch loss = 0.595504879951477 | accuracy = 0.7740566037735849 |
| Epoch[2] Batch[535] Speed: 1.2689652918975904 samples/sec batch loss = 0.671483039855957 | accuracy = 0.7738317757009345 |
| Epoch[2] Batch[540] Speed: 1.2708097242014063 samples/sec batch loss = 0.17383302748203278 | accuracy = 0.774074074074074 |
| Epoch[2] Batch[545] Speed: 1.2643524423912014 samples/sec batch loss = 0.5320215225219727 | accuracy = 0.7738532110091743 |
| Epoch[2] Batch[550] Speed: 1.2618272500484922 samples/sec batch loss = 1.0550318956375122 | accuracy = 0.7736363636363637 |
| Epoch[2] Batch[555] Speed: 1.26346845150029 samples/sec batch loss = 0.18514062464237213 | accuracy = 0.7747747747747747 |
| Epoch[2] Batch[560] Speed: 1.2623556063457515 samples/sec batch loss = 2.5367209911346436 | accuracy = 0.775 |
| Epoch[2] Batch[565] Speed: 1.2685481091640514 samples/sec batch loss = 0.2761897146701813 | accuracy = 0.7761061946902655 |
| Epoch[2] Batch[570] Speed: 1.2663014111791608 samples/sec batch loss = 0.3111046552658081 | accuracy = 0.7767543859649123 |
| Epoch[2] Batch[575] Speed: 1.2735179537379704 samples/sec batch loss = 0.4073621928691864 | accuracy = 0.7765217391304348 |
| Epoch[2] Batch[580] Speed: 1.273861126855634 samples/sec batch loss = 0.6206179261207581 | accuracy = 0.7754310344827586 |
| Epoch[2] Batch[585] Speed: 1.2631924815679196 samples/sec batch loss = 0.48506611585617065 | accuracy = 0.7756410256410257 |
| Epoch[2] Batch[590] Speed: 1.2598426332106674 samples/sec batch loss = 0.658402681350708 | accuracy = 0.7762711864406779 |
| Epoch[2] Batch[595] Speed: 1.2632882630533337 samples/sec batch loss = 0.19682744145393372 | accuracy = 0.7764705882352941 |
| Epoch[2] Batch[600] Speed: 1.2686770342392444 samples/sec batch loss = 0.1656409054994583 | accuracy = 0.7758333333333334 |
| Epoch[2] Batch[605] Speed: 1.2651506570901443 samples/sec batch loss = 0.713995099067688 | accuracy = 0.7760330578512397 |
| Epoch[2] Batch[610] Speed: 1.26925723345128 samples/sec batch loss = 0.5951725244522095 | accuracy = 0.7762295081967213 |
| Epoch[2] Batch[615] Speed: 1.2685644152036437 samples/sec batch loss = 0.696811854839325 | accuracy = 0.7764227642276422 |
| Epoch[2] Batch[620] Speed: 1.2648608874494123 samples/sec batch loss = 0.7674600481987 | accuracy = 0.7745967741935483 |
| Epoch[2] Batch[625] Speed: 1.2645833557888144 samples/sec batch loss = 0.28800585865974426 | accuracy = 0.774 |
| Epoch[2] Batch[630] Speed: 1.2628887781691442 samples/sec batch loss = 0.2308804988861084 | accuracy = 0.775 |
| Epoch[2] Batch[635] Speed: 1.2676663852101195 samples/sec batch loss = 0.4164401888847351 | accuracy = 0.7740157480314961 |
| Epoch[2] Batch[640] Speed: 1.2643104238893341 samples/sec batch loss = 0.6109464764595032 | accuracy = 0.774609375 |
| Epoch[2] Batch[645] Speed: 1.2693228211726706 samples/sec batch loss = 0.25409895181655884 | accuracy = 0.7748062015503876 |
| Epoch[2] Batch[650] Speed: 1.2663016023335818 samples/sec batch loss = 0.48241397738456726 | accuracy = 0.7742307692307693 |
| Epoch[2] Batch[655] Speed: 1.2708443783717904 samples/sec batch loss = 0.6689162254333496 | accuracy = 0.7713740458015267 |
| Epoch[2] Batch[660] Speed: 1.2626890829414308 samples/sec batch loss = 0.4472440481185913 | accuracy = 0.771969696969697 |
| Epoch[2] Batch[665] Speed: 1.2700302162922106 samples/sec batch loss = 0.27394452691078186 | accuracy = 0.7729323308270677 |
| Epoch[2] Batch[670] Speed: 1.27297963463671 samples/sec batch loss = 0.371197909116745 | accuracy = 0.7723880597014925 |
| Epoch[2] Batch[675] Speed: 1.2682980078671715 samples/sec batch loss = 0.5955603718757629 | accuracy = 0.7714814814814814 |
| Epoch[2] Batch[680] Speed: 1.263478061725949 samples/sec batch loss = 1.587622046470642 | accuracy = 0.7709558823529412 |
| Epoch[2] Batch[685] Speed: 1.2692849849688088 samples/sec batch loss = 0.9941589832305908 | accuracy = 0.7693430656934307 |
| Epoch[2] Batch[690] Speed: 1.2645051045341915 samples/sec batch loss = 0.10577966272830963 | accuracy = 0.7681159420289855 |
| Epoch[2] Batch[695] Speed: 1.2661786063928027 samples/sec batch loss = 0.7191821336746216 | accuracy = 0.7676258992805756 |
| Epoch[2] Batch[700] Speed: 1.2665697532210032 samples/sec batch loss = 0.6413692235946655 | accuracy = 0.7678571428571429 |
| Epoch[2] Batch[705] Speed: 1.2682728881113023 samples/sec batch loss = 0.5283220410346985 | accuracy = 0.7687943262411348 |
| Epoch[2] Batch[710] Speed: 1.2691752343276428 samples/sec batch loss = 0.4879496991634369 | accuracy = 0.7693661971830986 |
| Epoch[2] Batch[715] Speed: 1.2731947723873964 samples/sec batch loss = 0.3229662775993347 | accuracy = 0.7695804195804196 |
| Epoch[2] Batch[720] Speed: 1.2734630477312125 samples/sec batch loss = 1.2633661031723022 | accuracy = 0.7684027777777778 |
| Epoch[2] Batch[725] Speed: 1.2736015784956087 samples/sec batch loss = 0.7047713398933411 | accuracy = 0.7686206896551724 |
| Epoch[2] Batch[730] Speed: 1.2647476100746702 samples/sec batch loss = 0.4019237756729126 | accuracy = 0.7688356164383562 |
| Epoch[2] Batch[735] Speed: 1.2679527463365352 samples/sec batch loss = 0.3061882257461548 | accuracy = 0.7697278911564626 |
| Epoch[2] Batch[740] Speed: 1.2619609825372182 samples/sec batch loss = 0.23034590482711792 | accuracy = 0.7695945945945946 |
| Epoch[2] Batch[745] Speed: 1.2727598383857817 samples/sec batch loss = 0.735202968120575 | accuracy = 0.7697986577181208 |
| Epoch[2] Batch[750] Speed: 1.2696486515652794 samples/sec batch loss = 0.23050051927566528 | accuracy = 0.7696666666666667 |
| Epoch[2] Batch[755] Speed: 1.2685166492840463 samples/sec batch loss = 1.3658818006515503 | accuracy = 0.7685430463576159 |
| Epoch[2] Batch[760] Speed: 1.265403908831149 samples/sec batch loss = 0.3474471867084503 | accuracy = 0.7697368421052632 |
| Epoch[2] Batch[765] Speed: 1.2647665835737454 samples/sec batch loss = 0.24107645452022552 | accuracy = 0.7696078431372549 |
| Epoch[2] Batch[770] Speed: 1.2670440990165184 samples/sec batch loss = 0.6008711457252502 | accuracy = 0.7691558441558441 |
| Epoch[2] Batch[775] Speed: 1.269949559194579 samples/sec batch loss = 0.33554041385650635 | accuracy = 0.7690322580645161 |
| Epoch[2] Batch[780] Speed: 1.271180525616568 samples/sec batch loss = 0.21718209981918335 | accuracy = 0.7698717948717949 |
| Epoch[2] Batch[785] Speed: 1.2687251000685815 samples/sec batch loss = 0.5990146398544312 | accuracy = 0.7697452229299363 |
| [Epoch 2] training: accuracy=0.7706218274111675 |
| [Epoch 2] time cost: 639.0972635746002 |
| [Epoch 2] validation: validation accuracy=0.7733333333333333 |
| </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>0.7577777777777778 |
| </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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