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<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Train the neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Use GPUs</a></li>
</ul>
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<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li>
<li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/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="../../../tutorials/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="../../../tutorials/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="../../../tutorials/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="../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul>
<li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li>
<li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li>
<li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/kvstore/index.html">KVStore</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/ndarray/index.html">NDArray</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/02-ndarray-operations.html">NDArray Operations</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/index.html">Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/onnx/index.html">ONNX</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/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 internal" href="../../../tutorials/packages/onnx/super_resolution.html">Importing an ONNX model into MXNet</a></li>
<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li>
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<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/performance/index.html">Performance</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/compression/index.html">Compression</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/compression/int8.html">Deploy with int-8</a></li>
<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li>
<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li>
<li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/backend/index.html">Accelerated Backend Tools</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/mkldnn/index.html">Intel MKL-DNN</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/mkldnn/mkldnn_quantization.html">Quantize with MKL-DNN backend</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/mkldnn/mkldnn_readme.html">Install MXNet with MKL-DNN</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/tensorrt/index.html">TensorRT</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/tensorrt/tensorrt.html">Optimizing Deep Learning Computation Graphs with TensorRT</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/tvm.html">Use TVM</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/profiler.html">Profiling MXNet Models</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/deploy/index.html">Deployment</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/export/index.html">Export</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/export/onnx.html">Exporting to ONNX format</a></li>
<li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/export_network.html">Export Gluon CV Models</a></li>
<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Save / Load Parameters</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Train the neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Use GPUs</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li>
<li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/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="../../../tutorials/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="../../../tutorials/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="../../../tutorials/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="../../../tutorials/packages/gluon/image/image-augmentation.html">Image Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/training/index.html">Training</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul>
<li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li>
<li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li>
<li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/kvstore/index.html">KVStore</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/ndarray/index.html">NDArray</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/02-ndarray-operations.html">NDArray Operations</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/index.html">Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/onnx/index.html">ONNX</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/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 internal" href="../../../tutorials/packages/onnx/super_resolution.html">Importing an ONNX model into MXNet</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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<div class="section" id="module-mxnet.model">
<span id="mxnet-model"></span><h1>mxnet.model<a class="headerlink" href="#module-mxnet.model" title="Permalink to this headline"></a></h1>
<p>MXNet model module</p>
<p><strong>Classes</strong></p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.model.BatchEndParam" title="mxnet.model.BatchEndParam"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BatchEndParam</span></code></a></p></td>
<td><p>BatchEndParams(epoch, nbatch, eval_metric, locals)</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#mxnet.model.FeedForward" title="mxnet.model.FeedForward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FeedForward</span></code></a>(symbol[, ctx, num_epoch, …])</p></td>
<td><p>Model class of MXNet for training and predicting feedforward nets.</p></td>
</tr>
</tbody>
</table>
<p><strong>Functions</strong></p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.model.load_checkpoint" title="mxnet.model.load_checkpoint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_checkpoint</span></code></a>(prefix, epoch)</p></td>
<td><p>Load model checkpoint from file.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#mxnet.model.load_params" title="mxnet.model.load_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_params</span></code></a>(prefix, epoch)</p></td>
<td><p>Load params from a file</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.model.save_checkpoint" title="mxnet.model.save_checkpoint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_checkpoint</span></code></a>(prefix, epoch, symbol, …)</p></td>
<td><p>Checkpoint the model data into file.</p></td>
</tr>
</tbody>
</table>
<dl class="attribute">
<dt id="mxnet.model.BatchEndParam">
<code class="sig-prename descclassname">mxnet.model.</code><code class="sig-name descname">BatchEndParam</code><a class="headerlink" href="#mxnet.model.BatchEndParam" title="Permalink to this definition"></a></dt>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">epoch</span></code></p></td>
<td><p>Alias for field number 0</p></td>
</tr>
<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval_metric</span></code></p></td>
<td><p>Alias for field number 2</p></td>
</tr>
<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">locals</span></code></p></td>
<td><p>Alias for field number 3</p></td>
</tr>
<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">nbatch</span></code></p></td>
<td><p>Alias for field number 1</p></td>
</tr>
</tbody>
</table>
<p><strong>Attributes</strong></p>
<dd><p>alias of <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.model.BatchEndParams</span></code></p>
</dd></dl>
<dl class="class">
<dt id="mxnet.model.FeedForward">
<em class="property">class </em><code class="sig-prename descclassname">mxnet.model.</code><code class="sig-name descname">FeedForward</code><span class="sig-paren">(</span><em class="sig-param">symbol</em>, <em class="sig-param">ctx=None</em>, <em class="sig-param">num_epoch=None</em>, <em class="sig-param">epoch_size=None</em>, <em class="sig-param">optimizer='sgd'</em>, <em class="sig-param">initializer=&lt;mxnet.initializer.Uniform object&gt;</em>, <em class="sig-param">numpy_batch_size=128</em>, <em class="sig-param">arg_params=None</em>, <em class="sig-param">aux_params=None</em>, <em class="sig-param">allow_extra_params=False</em>, <em class="sig-param">begin_epoch=0</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/model.html#FeedForward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.model.FeedForward" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Model class of MXNet for training and predicting feedforward nets.
This class is designed for a single-data single output supervised network.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>symbol</strong> (<a class="reference internal" href="../../symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The symbol configuration of computation network.</p></li>
<li><p><strong>ctx</strong> (<a class="reference internal" href="../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em> or </em><em>list of Context</em><em>, </em><em>optional</em>) – The device context of training and prediction.
To use multi GPU training, pass in a list of gpu contexts.</p></li>
<li><p><strong>num_epoch</strong> (<em>int</em><em>, </em><em>optional</em>) – Training parameter, number of training epochs(epochs).</p></li>
<li><p><strong>epoch_size</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of batches in a epoch. In default, it is set to
<code class="docutils literal notranslate"><span class="pre">ceil(num_train_examples</span> <span class="pre">/</span> <span class="pre">batch_size)</span></code>.</p></li>
<li><p><strong>optimizer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../optimizer/index.html#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><em>Optimizer</em></a><em>, </em><em>optional</em>) – Training parameter, name or optimizer object for training.</p></li>
<li><p><strong>initializer</strong> (<em>initializer function</em><em>, </em><em>optional</em>) – Training parameter, the initialization scheme used.</p></li>
<li><p><strong>numpy_batch_size</strong> (<em>int</em><em>, </em><em>optional</em>) – The batch size of training data.
Only needed when input array is numpy.</p></li>
<li><p><strong>arg_params</strong> (<em>dict of str to NDArray</em><em>, </em><em>optional</em>) – Model parameter, dict of name to NDArray of net’s weights.</p></li>
<li><p><strong>aux_params</strong> (<em>dict of str to NDArray</em><em>, </em><em>optional</em>) – Model parameter, dict of name to NDArray of net’s auxiliary states.</p></li>
<li><p><strong>allow_extra_params</strong> (<em>boolean</em><em>, </em><em>optional</em>) – Whether allow extra parameters that are not needed by symbol
to be passed by aux_params and <code class="docutils literal notranslate"><span class="pre">arg_params</span></code>.
If this is True, no error will be thrown when <code class="docutils literal notranslate"><span class="pre">aux_params</span></code> and <code class="docutils literal notranslate"><span class="pre">arg_params</span></code>
contain more parameters than needed.</p></li>
<li><p><strong>begin_epoch</strong> (<em>int</em><em>, </em><em>optional</em>) – The begining training epoch.</p></li>
<li><p><strong>kwargs</strong> (<em>dict</em>) – The additional keyword arguments passed to optimizer.</p></li>
</ul>
</dd>
</dl>
<p><strong>Methods</strong></p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.model.FeedForward.create" title="mxnet.model.FeedForward.create"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create</span></code></a>(symbol, X[, y, ctx, num_epoch, …])</p></td>
<td><p>Functional style to create a model.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#mxnet.model.FeedForward.fit" title="mxnet.model.FeedForward.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(X[, y, eval_data, eval_metric, …])</p></td>
<td><p>Fit the model.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.model.FeedForward.load" title="mxnet.model.FeedForward.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(prefix, epoch[, ctx])</p></td>
<td><p>Load model checkpoint from file.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#mxnet.model.FeedForward.predict" title="mxnet.model.FeedForward.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(X[, num_batch, return_data, reset])</p></td>
<td><p>Run the prediction, always only use one device.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.model.FeedForward.save" title="mxnet.model.FeedForward.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>(prefix[, epoch, remove_amp_cast])</p></td>
<td><p>Checkpoint the model checkpoint into file.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#mxnet.model.FeedForward.score" title="mxnet.model.FeedForward.score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">score</span></code></a>(X[, eval_metric, num_batch, …])</p></td>
<td><p>Run the model given an input and calculate the score as assessed by an evaluation metric.</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="mxnet.model.FeedForward.create">
<em class="property">static </em><code class="sig-name descname">create</code><span class="sig-paren">(</span><em class="sig-param">symbol</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em>, <em class="sig-param">ctx=None</em>, <em class="sig-param">num_epoch=None</em>, <em class="sig-param">epoch_size=None</em>, <em class="sig-param">optimizer='sgd'</em>, <em class="sig-param">initializer=&lt;mxnet.initializer.Uniform object&gt;</em>, <em class="sig-param">eval_data=None</em>, <em class="sig-param">eval_metric='acc'</em>, <em class="sig-param">epoch_end_callback=None</em>, <em class="sig-param">batch_end_callback=None</em>, <em class="sig-param">kvstore='local'</em>, <em class="sig-param">logger=None</em>, <em class="sig-param">work_load_list=None</em>, <em class="sig-param">eval_end_callback=&lt;mxnet.callback.LogValidationMetricsCallback object&gt;</em>, <em class="sig-param">eval_batch_end_callback=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/model.html#FeedForward.create"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.model.FeedForward.create" title="Permalink to this definition"></a></dt>
<dd><p>Functional style to create a model.
This function is more consistent with functional
languages such as R, where mutation is not allowed.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>symbol</strong> (<a class="reference internal" href="../../symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The symbol configuration of a computation network.</p></li>
<li><p><strong>X</strong> (<a class="reference internal" href="../io/index.html#mxnet.io.DataIter" title="mxnet.io.DataIter"><em>DataIter</em></a>) – Training data.</p></li>
<li><p><strong>y</strong> (<em>numpy.ndarray</em><em>, </em><em>optional</em>) – If <cite>X</cite> is a <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code>, <cite>y</cite> must be set.</p></li>
<li><p><strong>ctx</strong> (<a class="reference internal" href="../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em> or </em><em>list of Context</em><em>, </em><em>optional</em>) – The device context of training and prediction.
To use multi-GPU training, pass in a list of GPU contexts.</p></li>
<li><p><strong>num_epoch</strong> (<em>int</em><em>, </em><em>optional</em>) – The number of training epochs(epochs).</p></li>
<li><p><strong>epoch_size</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of batches in a epoch. In default, it is set to
<code class="docutils literal notranslate"><span class="pre">ceil(num_train_examples</span> <span class="pre">/</span> <span class="pre">batch_size)</span></code>.</p></li>
<li><p><strong>optimizer</strong> (<em>str</em><em> or </em><a class="reference internal" href="../../optimizer/index.html#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><em>Optimizer</em></a><em>, </em><em>optional</em>) – The name of the chosen optimizer, or an optimizer object, used for training.</p></li>
<li><p><strong>initializer</strong> (<em>initializer function</em><em>, </em><em>optional</em>) – The initialization scheme used.</p></li>
<li><p><strong>eval_data</strong> (<a class="reference internal" href="../io/index.html#mxnet.io.DataIter" title="mxnet.io.DataIter"><em>DataIter</em></a><em> or </em><em>numpy.ndarray pair</em>) – If <cite>eval_set</cite> is <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code> pair, it should
be (<cite>valid_data</cite>, <cite>valid_label</cite>).</p></li>
<li><p><strong>eval_metric</strong> (<a class="reference internal" href="../../metric/index.html#mxnet.metric.EvalMetric" title="mxnet.metric.EvalMetric"><em>metric.EvalMetric</em></a><em> or </em><em>str</em><em> or </em><em>callable</em>) – The evaluation metric. Can be the name of an evaluation metric
or a custom evaluation function that returns statistics
based on a minibatch.</p></li>
<li><p><strong>epoch_end_callback</strong> (<em>callable</em><em>(</em><em>epoch</em><em>, </em><em>symbol</em><em>, </em><em>arg_params</em><em>, </em><em>aux_states</em><em>)</em>) – A callback that is invoked at end of each epoch.
This can be used to checkpoint model each epoch.</p></li>
<li><p><strong>batch_end_callback</strong> (<em>callable</em><em>(</em><em>epoch</em><em>)</em>) – A callback that is invoked at end of each batch for print purposes.</p></li>
<li><p><strong>kvstore</strong> (<em>KVStore</em><em> or </em><em>str</em><em>, </em><em>optional</em>) – The KVStore or a string kvstore type: ‘local’, ‘dist_sync’, ‘dis_async’.
Defaults to ‘local’, often no need to change for single machine.</p></li>
<li><p><strong>logger</strong> (<em>logging logger</em><em>, </em><em>optional</em>) – When not specified, default logger will be used.</p></li>
<li><p><strong>work_load_list</strong> (<em>list of float</em><em> or </em><em>int</em><em>, </em><em>optional</em>) – The list of work load for different devices,
in the same order as <cite>ctx</cite>.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="mxnet.model.FeedForward.fit">
<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param">X</em>, <em class="sig-param">y=None</em>, <em class="sig-param">eval_data=None</em>, <em class="sig-param">eval_metric='acc'</em>, <em class="sig-param">epoch_end_callback=None</em>, <em class="sig-param">batch_end_callback=None</em>, <em class="sig-param">kvstore='local'</em>, <em class="sig-param">logger=None</em>, <em class="sig-param">work_load_list=None</em>, <em class="sig-param">monitor=None</em>, <em class="sig-param">eval_end_callback=&lt;mxnet.callback.LogValidationMetricsCallback object&gt;</em>, <em class="sig-param">eval_batch_end_callback=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/model.html#FeedForward.fit"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.model.FeedForward.fit" title="Permalink to this definition"></a></dt>
<dd><p>Fit the model.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>X</strong> (<a class="reference internal" href="../io/index.html#mxnet.io.DataIter" title="mxnet.io.DataIter"><em>DataIter</em></a><em>, or </em><em>numpy.ndarray/NDArray</em>) – Training data. If <cite>X</cite> is a <cite>DataIter</cite>, the name or (if name not available)
the position of its outputs should match the corresponding variable
names defined in the symbolic graph.</p></li>
<li><p><strong>y</strong> (<em>numpy.ndarray/NDArray</em><em>, </em><em>optional</em>) – Training set label.
If X is <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code> or <cite>NDArray</cite>, <cite>y</cite> is required to be set.
While y can be 1D or 2D (with 2nd dimension as 1), its first dimension must be
the same as <cite>X</cite>, i.e. the number of data points and labels should be equal.</p></li>
<li><p><strong>eval_data</strong> (<a class="reference internal" href="../io/index.html#mxnet.io.DataIter" title="mxnet.io.DataIter"><em>DataIter</em></a><em> or </em><em>numpy.ndarray/list/NDArray pair</em>) – If eval_data is numpy.ndarray/list/NDArray pair,
it should be <code class="docutils literal notranslate"><span class="pre">(valid_data,</span> <span class="pre">valid_label)</span></code>.</p></li>
<li><p><strong>eval_metric</strong> (<a class="reference internal" href="../../metric/index.html#mxnet.metric.EvalMetric" title="mxnet.metric.EvalMetric"><em>metric.EvalMetric</em></a><em> or </em><em>str</em><em> or </em><em>callable</em>) – The evaluation metric. This could be the name of evaluation metric
or a custom evaluation function that returns statistics
based on a minibatch.</p></li>
<li><p><strong>epoch_end_callback</strong> (<em>callable</em><em>(</em><em>epoch</em><em>, </em><em>symbol</em><em>, </em><em>arg_params</em><em>, </em><em>aux_states</em><em>)</em>) – A callback that is invoked at end of each epoch.
This can be used to checkpoint model each epoch.</p></li>
<li><p><strong>batch_end_callback</strong> (<em>callable</em><em>(</em><em>epoch</em><em>)</em>) – A callback that is invoked at end of each batch for purposes of printing.</p></li>
<li><p><strong>kvstore</strong> (<em>KVStore</em><em> or </em><em>str</em><em>, </em><em>optional</em>) – The KVStore or a string kvstore type: ‘local’, ‘dist_sync’, ‘dist_async’
In default uses ‘local’, often no need to change for single machiine.</p></li>
<li><p><strong>logger</strong> (<em>logging logger</em><em>, </em><em>optional</em>) – When not specified, default logger will be used.</p></li>
<li><p><strong>work_load_list</strong> (<em>float</em><em> or </em><em>int</em><em>, </em><em>optional</em>) – The list of work load for different devices,
in the same order as <cite>ctx</cite>.</p></li>
</ul>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>KVStore behavior
- ‘local’, multi-devices on a single machine, will automatically choose best type.
- ‘dist_sync’, multiple machines communicating via BSP.
- ‘dist_async’, multiple machines with asynchronous communication.</p>
</div>
</dd></dl>
<dl class="method">
<dt id="mxnet.model.FeedForward.load">
<em class="property">static </em><code class="sig-name descname">load</code><span class="sig-paren">(</span><em class="sig-param">prefix</em>, <em class="sig-param">epoch</em>, <em class="sig-param">ctx=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/model.html#FeedForward.load"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.model.FeedForward.load" title="Permalink to this definition"></a></dt>
<dd><p>Load model checkpoint from file.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>prefix</strong> (<em>str</em>) – Prefix of model name.</p></li>
<li><p><strong>epoch</strong> (<em>int</em>) – epoch number of model we would like to load.</p></li>
<li><p><strong>ctx</strong> (<a class="reference internal" href="../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em> or </em><em>list of Context</em><em>, </em><em>optional</em>) – The device context of training and prediction.</p></li>
<li><p><strong>kwargs</strong> (<em>dict</em>) – Other parameters for model, including <cite>num_epoch</cite>, optimizer and <cite>numpy_batch_size</cite>.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><strong>model</strong> – The loaded model that can be used for prediction.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="#mxnet.model.FeedForward" title="mxnet.model.FeedForward">FeedForward</a></p>
</dd>
</dl>
<p class="rubric">Notes</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">prefix-symbol.json</span></code> will be saved for symbol.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">prefix-epoch.params</span></code> will be saved for parameters.</p></li>
</ul>
</dd></dl>
<dl class="method">
<dt id="mxnet.model.FeedForward.predict">
<code class="sig-name descname">predict</code><span class="sig-paren">(</span><em class="sig-param">X</em>, <em class="sig-param">num_batch=None</em>, <em class="sig-param">return_data=False</em>, <em class="sig-param">reset=True</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/model.html#FeedForward.predict"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.model.FeedForward.predict" title="Permalink to this definition"></a></dt>
<dd><p>Run the prediction, always only use one device.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>X</strong> (<em>mxnet.DataIter</em>) – </p></li>
<li><p><strong>num_batch</strong> (<em>int</em><em> or </em><em>None</em>) – The number of batch to run. Go though all batches if <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><strong>y</strong> – The predicted value of the output.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>numpy.ndarray or a list of numpy.ndarray if the network has multiple outputs.</p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="mxnet.model.FeedForward.save">
<code class="sig-name descname">save</code><span class="sig-paren">(</span><em class="sig-param">prefix</em>, <em class="sig-param">epoch=None</em>, <em class="sig-param">remove_amp_cast=True</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/model.html#FeedForward.save"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.model.FeedForward.save" title="Permalink to this definition"></a></dt>
<dd><p>Checkpoint the model checkpoint into file.
You can also use <cite>pickle</cite> to do the job if you only work on Python.
The advantage of <cite>load</cite> and <cite>save</cite> (as compared to <cite>pickle</cite>) is that
the resulting file can be loaded from other MXNet language bindings.
One can also directly <cite>load</cite>/<cite>save</cite> from/to cloud storage(S3, HDFS)</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>prefix</strong> (<em>str</em>) – Prefix of model name.</p></li>
<li><p><strong>remove_amp_cast</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.</p></li>
</ul>
</dd>
</dl>
<p class="rubric">Notes</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">prefix-symbol.json</span></code> will be saved for symbol.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">prefix-epoch.params</span></code> will be saved for parameters.</p></li>
</ul>
</dd></dl>
<dl class="method">
<dt id="mxnet.model.FeedForward.score">
<code class="sig-name descname">score</code><span class="sig-paren">(</span><em class="sig-param">X</em>, <em class="sig-param">eval_metric='acc'</em>, <em class="sig-param">num_batch=None</em>, <em class="sig-param">batch_end_callback=None</em>, <em class="sig-param">reset=True</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/model.html#FeedForward.score"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.model.FeedForward.score" title="Permalink to this definition"></a></dt>
<dd><p>Run the model given an input and calculate the score
as assessed by an evaluation metric.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>X</strong> (<em>mxnet.DataIter</em>) – </p></li>
<li><p><strong>eval_metric</strong> (<em>metric.metric</em>) – The metric for calculating score.</p></li>
<li><p><strong>num_batch</strong> (<em>int</em><em> or </em><em>None</em>) – The number of batches to run. Go though all batches if <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><strong>s</strong> – The final score.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>float</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="function">
<dt id="mxnet.model.load_checkpoint">
<code class="sig-prename descclassname">mxnet.model.</code><code class="sig-name descname">load_checkpoint</code><span class="sig-paren">(</span><em class="sig-param">prefix</em>, <em class="sig-param">epoch</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/model.html#load_checkpoint"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.model.load_checkpoint" title="Permalink to this definition"></a></dt>
<dd><p>Load model checkpoint from file.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>prefix</strong> (<em>str</em>) – Prefix of model name.</p></li>
<li><p><strong>epoch</strong> (<em>int</em>) – Epoch number of model we would like to load.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><ul class="simple">
<li><p><strong>symbol</strong> (<em>Symbol</em>) – The symbol configuration of computation network.</p></li>
<li><p><strong>arg_params</strong> (<em>dict of str to NDArray</em>) – Model parameter, dict of name to NDArray of net’s weights.</p></li>
<li><p><strong>aux_params</strong> (<em>dict of str to NDArray</em>) – Model parameter, dict of name to NDArray of net’s auxiliary states.</p></li>
</ul>
</p>
</dd>
</dl>
<p class="rubric">Notes</p>
<ul class="simple">
<li><p>Symbol will be loaded from <code class="docutils literal notranslate"><span class="pre">prefix-symbol.json</span></code>.</p></li>
<li><p>Parameters will be loaded from <code class="docutils literal notranslate"><span class="pre">prefix-epoch.params</span></code>.</p></li>
</ul>
</dd></dl>
<dl class="function">
<dt id="mxnet.model.load_params">
<code class="sig-prename descclassname">mxnet.model.</code><code class="sig-name descname">load_params</code><span class="sig-paren">(</span><em class="sig-param">prefix</em>, <em class="sig-param">epoch</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/model.html#load_params"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.model.load_params" title="Permalink to this definition"></a></dt>
<dd><p>Load params from a file</p>
</dd></dl>
<dl class="function">
<dt id="mxnet.model.save_checkpoint">
<code class="sig-prename descclassname">mxnet.model.</code><code class="sig-name descname">save_checkpoint</code><span class="sig-paren">(</span><em class="sig-param">prefix</em>, <em class="sig-param">epoch</em>, <em class="sig-param">symbol</em>, <em class="sig-param">arg_params</em>, <em class="sig-param">aux_params</em>, <em class="sig-param">remove_amp_cast=True</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/model.html#save_checkpoint"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.model.save_checkpoint" title="Permalink to this definition"></a></dt>
<dd><p>Checkpoint the model data into file.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>prefix</strong> (<em>str</em>) – Prefix of model name.</p></li>
<li><p><strong>epoch</strong> (<em>int</em>) – The epoch number of the model.</p></li>
<li><p><strong>symbol</strong> (<a class="reference internal" href="../../symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input Symbol.</p></li>
<li><p><strong>arg_params</strong> (<em>dict of str to NDArray</em>) – Model parameter, dict of name to NDArray of net’s weights.</p></li>
<li><p><strong>aux_params</strong> (<em>dict of str to NDArray</em>) – Model parameter, dict of name to NDArray of net’s auxiliary states.</p></li>
<li><p><strong>remove_amp_cast</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.</p></li>
</ul>
</dd>
</dl>
<p class="rubric">Notes</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">prefix-symbol.json</span></code> will be saved for symbol.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">prefix-epoch.params</span></code> will be saved for parameters.</p></li>
</ul>
</dd></dl>
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