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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-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-l5"><a class="reference internal" href="../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</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>
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<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/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-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-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>
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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>
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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-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>
</ul>
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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-l4"><a class="reference internal" href="../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/image/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>
</ul>
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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>
</ul>
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<li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li>
</ul>
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</ul>
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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>
</ul>
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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>
</ul>
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</ul>
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<div class="section" id="gluon-symbolblock">
<h1>gluon.SymbolBlock<a class="headerlink" href="#gluon-symbolblock" title="Permalink to this headline"></a></h1>
<dl class="class">
<dt id="mxnet.gluon.SymbolBlock">
<em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.</code><code class="sig-name descname">SymbolBlock</code><span class="sig-paren">(</span><em class="sig-param">outputs</em>, <em class="sig-param">inputs</em>, <em class="sig-param">params=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/gluon/block.html#SymbolBlock"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.SymbolBlock" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.block.HybridBlock</span></code></p>
<p>Construct block from symbol. This is useful for using pre-trained models
as feature extractors. For example, you may want to extract the output
from fc2 layer in AlexNet.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>outputs</strong> (<a class="reference internal" href="../symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><em>list of Symbol</em>) – The desired output for SymbolBlock.</p></li>
<li><p><strong>inputs</strong> (<a class="reference internal" href="../symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><em>list of Symbol</em>) – The Variables in output’s argument that should be used as inputs.</p></li>
<li><p><strong>params</strong> (<a class="reference internal" href="parameter_dict.html#mxnet.gluon.ParameterDict" title="mxnet.gluon.ParameterDict"><em>ParameterDict</em></a>) – Parameter dictionary for arguments and auxililary states of outputs
that are not inputs.</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.gluon.SymbolBlock.apply" title="mxnet.gluon.SymbolBlock.apply"><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code></a>(fn)</p></td>
<td><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.SymbolBlock.cast" title="mxnet.gluon.SymbolBlock.cast"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cast</span></code></a>(dtype)</p></td>
<td><p>Cast this Block to use another data type.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.SymbolBlock.collect_params" title="mxnet.gluon.SymbolBlock.collect_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">collect_params</span></code></a>([select])</p></td>
<td><p>Returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">ParameterDict</span></code> containing this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and all of its children’s Parameters(default), also can returns the select <code class="xref py py-class docutils literal notranslate"><span class="pre">ParameterDict</span></code> which match some given regular expressions.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.SymbolBlock.export" title="mxnet.gluon.SymbolBlock.export"><code class="xref py py-obj docutils literal notranslate"><span class="pre">export</span></code></a>(path[, epoch, remove_amp_cast])</p></td>
<td><p>Export HybridBlock to json format that can be loaded by <cite>gluon.SymbolBlock.imports</cite>, <cite>mxnet.mod.Module</cite> or the C++ interface.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.SymbolBlock.forward" title="mxnet.gluon.SymbolBlock.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(x, *args)</p></td>
<td><p>Defines the forward computation.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.SymbolBlock.hybrid_forward" title="mxnet.gluon.SymbolBlock.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, x, *args, **kwargs)</p></td>
<td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.SymbolBlock.hybridize" title="mxnet.gluon.SymbolBlock.hybridize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybridize</span></code></a>([active, backend, clear, …])</p></td>
<td><p>Activates or deactivates <code class="xref py py-class docutils literal notranslate"><span class="pre">HybridBlock</span></code> s recursively.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.SymbolBlock.imports" title="mxnet.gluon.SymbolBlock.imports"><code class="xref py py-obj docutils literal notranslate"><span class="pre">imports</span></code></a>(symbol_file, input_names[, …])</p></td>
<td><p>Import model previously saved by <cite>gluon.HybridBlock.export</cite> or <cite>Module.save_checkpoint</cite> as a <cite>gluon.SymbolBlock</cite> for use in Gluon.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.SymbolBlock.infer_shape" title="mxnet.gluon.SymbolBlock.infer_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">infer_shape</span></code></a>(*args)</p></td>
<td><p>Infers shape of Parameters from inputs.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.SymbolBlock.infer_type" title="mxnet.gluon.SymbolBlock.infer_type"><code class="xref py py-obj docutils literal notranslate"><span class="pre">infer_type</span></code></a>(*args)</p></td>
<td><p>Infers data type of Parameters from inputs.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.SymbolBlock.initialize" title="mxnet.gluon.SymbolBlock.initialize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">initialize</span></code></a>([init, ctx, verbose, force_reinit])</p></td>
<td><p>Initializes <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> s of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and its children.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.SymbolBlock.load_parameters" title="mxnet.gluon.SymbolBlock.load_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_parameters</span></code></a>(filename[, ctx, …])</p></td>
<td><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.SymbolBlock.load_params" title="mxnet.gluon.SymbolBlock.load_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_params</span></code></a>(filename[, ctx, allow_missing, …])</p></td>
<td><p>[Deprecated] Please use load_parameters.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.SymbolBlock.name_scope" title="mxnet.gluon.SymbolBlock.name_scope"><code class="xref py py-obj docutils literal notranslate"><span class="pre">name_scope</span></code></a>()</p></td>
<td><p>Returns a name space object managing a child <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code> and parameter names.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.SymbolBlock.optimize_for" title="mxnet.gluon.SymbolBlock.optimize_for"><code class="xref py py-obj docutils literal notranslate"><span class="pre">optimize_for</span></code></a>(x, *args[, backend, clear, …])</p></td>
<td><p>Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.SymbolBlock.register_child" title="mxnet.gluon.SymbolBlock.register_child"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_child</span></code></a>(block[, name])</p></td>
<td><p>Registers block as a child of self.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.SymbolBlock.register_forward_hook" title="mxnet.gluon.SymbolBlock.register_forward_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code></a>(hook)</p></td>
<td><p>Registers a forward hook on the block.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.SymbolBlock.register_forward_pre_hook" title="mxnet.gluon.SymbolBlock.register_forward_pre_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code></a>(hook)</p></td>
<td><p>Registers a forward pre-hook on the block.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.SymbolBlock.register_op_hook" title="mxnet.gluon.SymbolBlock.register_op_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_op_hook</span></code></a>(callback[, monitor_all])</p></td>
<td><p>Install op hook for block recursively.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.SymbolBlock.reset_ctx" title="mxnet.gluon.SymbolBlock.reset_ctx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_ctx</span></code></a>(ctx)</p></td>
<td><p>Re-assign all Parameters to other contexts.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.SymbolBlock.save_parameters" title="mxnet.gluon.SymbolBlock.save_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_parameters</span></code></a>(filename[, deduplicate])</p></td>
<td><p>Save parameters to file.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.SymbolBlock.save_params" title="mxnet.gluon.SymbolBlock.save_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_params</span></code></a>(filename)</p></td>
<td><p>[Deprecated] Please use save_parameters. Note that if you want load</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.SymbolBlock.summary" title="mxnet.gluon.SymbolBlock.summary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">summary</span></code></a>(*inputs)</p></td>
<td><p>Print the summary of the model’s output and parameters.</p></td>
</tr>
</tbody>
</table>
<p><strong>Attributes</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.gluon.SymbolBlock.name" title="mxnet.gluon.SymbolBlock.name"><code class="xref py py-obj docutils literal notranslate"><span class="pre">name</span></code></a></p></td>
<td><p>Name of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code>, without ‘_’ in the end.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.SymbolBlock.params" title="mxnet.gluon.SymbolBlock.params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">params</span></code></a></p></td>
<td><p>Returns this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code>’s parameter dictionary (does not include its children’s parameters).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.SymbolBlock.prefix" title="mxnet.gluon.SymbolBlock.prefix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">prefix</span></code></a></p></td>
<td><p>Prefix of this <code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code>.</p></td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># To extract the feature from fc1 and fc2 layers of AlexNet:</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">alexnet</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">model_zoo</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">alexnet</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">(),</span>
<span class="go"> prefix=&#39;model_&#39;)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">inputs</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">&#39;data&#39;</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span> <span class="o">=</span> <span class="n">alexnet</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">internals</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">get_internals</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">internals</span><span class="o">.</span><span class="n">list_outputs</span><span class="p">())</span>
<span class="go">[&#39;data&#39;, ..., &#39;model_dense0_relu_fwd_output&#39;, ..., &#39;model_dense1_relu_fwd_output&#39;, ...]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">internals</span><span class="p">[</span><span class="s1">&#39;model_dense0_relu_fwd_output&#39;</span><span class="p">],</span>
<span class="go"> internals[&#39;model_dense1_relu_fwd_output&#39;]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Create SymbolBlock that shares parameters with alexnet</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">feat_model</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">SymbolBlock</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="n">alexnet</span><span class="o">.</span><span class="n">collect_params</span><span class="p">())</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">feat_model</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
</pre></div>
</div>
<dl class="method">
<dt id="mxnet.gluon.SymbolBlock.apply">
<code class="sig-name descname">apply</code><span class="sig-paren">(</span><em class="sig-param">fn</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.SymbolBlock.apply" title="Permalink to this definition"></a></dt>
<dd><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every child block as well as self.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>fn</strong> (<em>callable</em>) – Function to be applied to each submodule, of form <cite>fn(block)</cite>.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>this block</p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.SymbolBlock.cast">
<code class="sig-name descname">cast</code><span class="sig-paren">(</span><em class="sig-param">dtype</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/gluon/block.html#SymbolBlock.cast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.SymbolBlock.cast" title="Permalink to this definition"></a></dt>
<dd><p>Cast this Block to use another data type.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>dtype</strong> (<em>str</em><em> or </em><em>numpy.dtype</em>) – The new data type.</p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.SymbolBlock.collect_params">
<code class="sig-name descname">collect_params</code><span class="sig-paren">(</span><em class="sig-param">select=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.SymbolBlock.collect_params" title="Permalink to this definition"></a></dt>
<dd><p>Returns a <a class="reference internal" href="parameter_dict.html#mxnet.gluon.ParameterDict" title="mxnet.gluon.ParameterDict"><code class="xref py py-class docutils literal notranslate"><span class="pre">ParameterDict</span></code></a> containing this <a class="reference internal" href="block.html#mxnet.gluon.Block" title="mxnet.gluon.Block"><code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code></a> and all of its
children’s Parameters(default), also can returns the select <a class="reference internal" href="parameter_dict.html#mxnet.gluon.ParameterDict" title="mxnet.gluon.ParameterDict"><code class="xref py py-class docutils literal notranslate"><span class="pre">ParameterDict</span></code></a>
which match some given regular expressions.</p>
<p>For example, collect the specified parameters in [‘conv1_weight’, ‘conv1_bias’, ‘fc_weight’,
‘fc_bias’]:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">&#39;conv1_weight|conv1_bias|fc_weight|fc_bias&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done
using regular expressions:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(</span><span class="s1">&#39;.*weight|.*bias&#39;</span><span class="p">)</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>select</strong> (<em>str</em>) – regular expressions</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>The selected <a class="reference internal" href="parameter_dict.html#mxnet.gluon.ParameterDict" title="mxnet.gluon.ParameterDict"><code class="xref py py-class docutils literal notranslate"><span class="pre">ParameterDict</span></code></a></p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.SymbolBlock.export">
<code class="sig-name descname">export</code><span class="sig-paren">(</span><em class="sig-param">path</em>, <em class="sig-param">epoch=0</em>, <em class="sig-param">remove_amp_cast=True</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.SymbolBlock.export" title="Permalink to this definition"></a></dt>
<dd><p>Export HybridBlock to json format that can be loaded by
<cite>gluon.SymbolBlock.imports</cite>, <cite>mxnet.mod.Module</cite> or the C++ interface.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>When there are only one input, it will have name <cite>data</cite>. When there
Are more than one inputs, they will be named as <cite>data0</cite>, <cite>data1</cite>, etc.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>path</strong> (<em>str</em>) – Path to save model. Two files <cite>path-symbol.json</cite> and <cite>path-xxxx.params</cite>
will be created, where xxxx is the 4 digits epoch number.</p></li>
<li><p><strong>epoch</strong> (<em>int</em>) – Epoch number of saved model.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.SymbolBlock.forward">
<code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">x</em>, <em class="sig-param">*args</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/gluon/block.html#SymbolBlock.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.SymbolBlock.forward" title="Permalink to this definition"></a></dt>
<dd><p>Defines the forward computation. Arguments can be either
<code class="xref py py-class docutils literal notranslate"><span class="pre">NDArray</span></code> or <code class="xref py py-class docutils literal notranslate"><span class="pre">Symbol</span></code>.</p>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.SymbolBlock.hybrid_forward">
<code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">x</em>, <em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/gluon/block.html#SymbolBlock.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.SymbolBlock.hybrid_forward" title="Permalink to this definition"></a></dt>
<dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</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="../symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li>
<li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.SymbolBlock.hybridize">
<code class="sig-name descname">hybridize</code><span class="sig-paren">(</span><em class="sig-param">active=True</em>, <em class="sig-param">backend=None</em>, <em class="sig-param">clear=True</em>, <em class="sig-param">static_alloc=False</em>, <em class="sig-param">static_shape=False</em>, <em class="sig-param">inline_limit=2</em>, <em class="sig-param">forward_bulk_size=None</em>, <em class="sig-param">backward_bulk_size=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.SymbolBlock.hybridize" title="Permalink to this definition"></a></dt>
<dd><p>Activates or deactivates <a class="reference internal" href="hybrid_block.html#mxnet.gluon.HybridBlock" title="mxnet.gluon.HybridBlock"><code class="xref py py-class docutils literal notranslate"><span class="pre">HybridBlock</span></code></a> s recursively. Has no effect on
non-hybrid children.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>active</strong> (<em>bool</em><em>, </em><em>default True</em>) – Whether to turn hybrid on or off.</p></li>
<li><p><strong>backend</strong> (<em>str</em>) – The name of backend, as registered in <cite>SubgraphBackendRegistry</cite>, default None</p></li>
<li><p><strong>clear</strong> (<em>bool</em><em>, </em><em>default True</em>) – Clears any previous optimizations</p></li>
<li><p><strong>static_alloc</strong> (<em>optional bool</em><em>, </em><em>default False</em>) – Statically allocate memory to improve speed. Memory usage may increase.</p></li>
<li><p><strong>static_shape</strong> (<em>optional bool</em><em>, </em><em>default False</em>) – Optimize for invariant input shapes between iterations. Must also
set static_alloc to True. Change of input shapes is still allowed
but slower.</p></li>
<li><p><strong>inline_limit</strong> (<em>optional int</em><em>, </em><em>default 2</em>) – Maximum number of operators that can be inlined.</p></li>
<li><p><strong>forward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during forward pass.</p></li>
<li><p><strong>backward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during forward pass.</p></li>
<li><p><strong>**kwargs</strong> (<em>optional</em>) – Backend options.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.SymbolBlock.imports">
<em class="property">static </em><code class="sig-name descname">imports</code><span class="sig-paren">(</span><em class="sig-param">symbol_file</em>, <em class="sig-param">input_names</em>, <em class="sig-param">param_file=None</em>, <em class="sig-param">ctx=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/gluon/block.html#SymbolBlock.imports"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.SymbolBlock.imports" title="Permalink to this definition"></a></dt>
<dd><p>Import model previously saved by <cite>gluon.HybridBlock.export</cite> or
<cite>Module.save_checkpoint</cite> as a <cite>gluon.SymbolBlock</cite> for use in Gluon.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>symbol_file</strong> (<em>str</em>) – Path to symbol file.</p></li>
<li><p><strong>input_names</strong> (<em>list of str</em>) – List of input variable names</p></li>
<li><p><strong>param_file</strong> (<em>str</em><em>, </em><em>optional</em>) – Path to parameter file.</p></li>
<li><p><strong>ctx</strong> (<a class="reference internal" href="../mxnet/context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default None</em>) – The context to initialize <cite>gluon.SymbolBlock</cite> on.</p></li>
<li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represents in the file.</p></li>
<li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not
present in this Block.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><cite>gluon.SymbolBlock</cite> loaded from symbol and parameter files.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="#mxnet.gluon.SymbolBlock" title="mxnet.gluon.SymbolBlock">gluon.SymbolBlock</a></p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">net1</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">model_zoo</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">resnet18_v1</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">prefix</span><span class="o">=</span><span class="s1">&#39;resnet&#39;</span><span class="p">,</span> <span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">net1</span><span class="o">.</span><span class="n">hybridize</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">out1</span> <span class="o">=</span> <span class="n">net1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">net1</span><span class="o">.</span><span class="n">export</span><span class="p">(</span><span class="s1">&#39;net1&#39;</span><span class="p">,</span> <span class="n">epoch</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="go">&gt;&gt;&gt;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">net2</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">SymbolBlock</span><span class="o">.</span><span class="n">imports</span><span class="p">(</span>
<span class="gp">... </span> <span class="s1">&#39;net1-symbol.json&#39;</span><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;data&#39;</span><span class="p">],</span> <span class="s1">&#39;net1-0001.params&#39;</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">out2</span> <span class="o">=</span> <span class="n">net2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.SymbolBlock.infer_shape">
<code class="sig-name descname">infer_shape</code><span class="sig-paren">(</span><em class="sig-param">*args</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.SymbolBlock.infer_shape" title="Permalink to this definition"></a></dt>
<dd><p>Infers shape of Parameters from inputs.</p>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.SymbolBlock.infer_type">
<code class="sig-name descname">infer_type</code><span class="sig-paren">(</span><em class="sig-param">*args</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.SymbolBlock.infer_type" title="Permalink to this definition"></a></dt>
<dd><p>Infers data type of Parameters from inputs.</p>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.SymbolBlock.initialize">
<code class="sig-name descname">initialize</code><span class="sig-paren">(</span><em class="sig-param">init=&lt;mxnet.initializer.Uniform object&gt;</em>, <em class="sig-param">ctx=None</em>, <em class="sig-param">verbose=False</em>, <em class="sig-param">force_reinit=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.SymbolBlock.initialize" title="Permalink to this definition"></a></dt>
<dd><p>Initializes <a class="reference internal" href="parameter.html#mxnet.gluon.Parameter" title="mxnet.gluon.Parameter"><code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code></a> s of this <a class="reference internal" href="block.html#mxnet.gluon.Block" title="mxnet.gluon.Block"><code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code></a> and its children.
Equivalent to <code class="docutils literal notranslate"><span class="pre">block.collect_params().initialize(...)</span></code></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>init</strong> (<a class="reference internal" href="../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Global default Initializer to be used when <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>.
Otherwise, <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> takes precedence.</p></li>
<li><p><strong>ctx</strong> (<a class="reference internal" href="../mxnet/context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em> or </em><em>list of Context</em>) – Keeps a copy of Parameters on one or many context(s).</p></li>
<li><p><strong>verbose</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to verbosely print out details on initialization.</p></li>
<li><p><strong>force_reinit</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to force re-initialization if parameter is already initialized.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.SymbolBlock.load_parameters">
<code class="sig-name descname">load_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">ctx=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em>, <em class="sig-param">cast_dtype=False</em>, <em class="sig-param">dtype_source='current'</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.SymbolBlock.load_parameters" title="Permalink to this definition"></a></dt>
<dd><p>Load parameters from file previously saved by <cite>save_parameters</cite>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>filename</strong> (<em>str</em>) – Path to parameter file.</p></li>
<li><p><strong>ctx</strong> (<a class="reference internal" href="../mxnet/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>default cpu</em><em>(</em><em>)</em>) – Context(s) to initialize loaded parameters on.</p></li>
<li><p><strong>allow_missing</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently skip loading parameters not represents in the file.</p></li>
<li><p><strong>ignore_extra</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to silently ignore parameters from the file that are not
present in this Block.</p></li>
<li><p><strong>cast_dtype</strong> (<em>bool</em><em>, </em><em>default False</em>) – Cast the data type of the NDArray loaded from the checkpoint to the dtype
provided by the Parameter if any.</p></li>
<li><p><strong>dtype_source</strong> (<em>str</em><em>, </em><em>default 'current'</em>) – must be in {‘current’, ‘saved’}
Only valid if cast_dtype=True, specify the source of the dtype for casting
the parameters</p></li>
</ul>
</dd>
</dl>
<p class="rubric">References</p>
<p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.SymbolBlock.load_params">
<code class="sig-name descname">load_params</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">ctx=None</em>, <em class="sig-param">allow_missing=False</em>, <em class="sig-param">ignore_extra=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.SymbolBlock.load_params" title="Permalink to this definition"></a></dt>
<dd><p>[Deprecated] Please use load_parameters.</p>
<p>Load parameters from file.</p>
<dl class="simple">
<dt>filename<span class="classifier">str</span></dt><dd><p>Path to parameter file.</p>
</dd>
<dt>ctx<span class="classifier">Context or list of Context, default cpu()</span></dt><dd><p>Context(s) to initialize loaded parameters on.</p>
</dd>
<dt>allow_missing<span class="classifier">bool, default False</span></dt><dd><p>Whether to silently skip loading parameters not represents in the file.</p>
</dd>
<dt>ignore_extra<span class="classifier">bool, default False</span></dt><dd><p>Whether to silently ignore parameters from the file that are not
present in this Block.</p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.SymbolBlock.name">
<em class="property">property </em><code class="sig-name descname">name</code><a class="headerlink" href="#mxnet.gluon.SymbolBlock.name" title="Permalink to this definition"></a></dt>
<dd><p>Name of this <a class="reference internal" href="block.html#mxnet.gluon.Block" title="mxnet.gluon.Block"><code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code></a>, without ‘_’ in the end.</p>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.SymbolBlock.name_scope">
<code class="sig-name descname">name_scope</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.SymbolBlock.name_scope" title="Permalink to this definition"></a></dt>
<dd><p>Returns a name space object managing a child <a class="reference internal" href="block.html#mxnet.gluon.Block" title="mxnet.gluon.Block"><code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code></a> and parameter
names. Should be used within a <code class="docutils literal notranslate"><span class="pre">with</span></code> statement:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">name_scope</span><span class="p">():</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dense</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">20</span><span class="p">)</span>
</pre></div>
</div>
<p>Please refer to
<a class="reference external" href="/api/python/docs/tutorials/packages/gluon/blocks/naming.html">the naming tutorial</a>
for more info on prefix and naming.</p>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.SymbolBlock.optimize_for">
<code class="sig-name descname">optimize_for</code><span class="sig-paren">(</span><em class="sig-param">x</em>, <em class="sig-param">*args</em>, <em class="sig-param">backend=None</em>, <em class="sig-param">clear=False</em>, <em class="sig-param">static_alloc=False</em>, <em class="sig-param">static_shape=False</em>, <em class="sig-param">inline_limit=2</em>, <em class="sig-param">forward_bulk_size=None</em>, <em class="sig-param">backward_bulk_size=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.SymbolBlock.optimize_for" title="Permalink to this definition"></a></dt>
<dd><p>Partitions the current HybridBlock and optimizes it for a given backend
without executing a forward pass. Modifies the HybridBlock in-place.</p>
<p>Immediately partitions a HybridBlock using the specified backend. Combines
the work done in the hybridize API with part of the work done in the forward
pass without calling the CachedOp. Can be used in place of hybridize,
afterwards <cite>export</cite> can be called or inference can be run. See README.md in
example/extensions/lib_subgraph/README.md for more details.</p>
<p class="rubric">Examples</p>
<p># partition and then export to file
block.optimize_for(x, backend=’myPart’)
block.export(‘partitioned’)</p>
<p># partition and then run inference
block.optimize_for(x, backend=’myPart’)
block(x)</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="../ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – first input to model</p></li>
<li><p><strong>*args</strong> (<a class="reference internal" href="../ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – other inputs to model</p></li>
<li><p><strong>backend</strong> (<em>str</em>) – The name of backend, as registered in <cite>SubgraphBackendRegistry</cite>, default None</p></li>
<li><p><strong>clear</strong> (<em>bool</em><em>, </em><em>default False</em>) – Clears any previous optimizations</p></li>
<li><p><strong>static_alloc</strong> (<em>bool</em><em>, </em><em>default False</em>) – Statically allocate memory to improve speed. Memory usage may increase.</p></li>
<li><p><strong>static_shape</strong> (<em>bool</em><em>, </em><em>default False</em>) – Optimize for invariant input shapes between iterations. Must also
set static_alloc to True. Change of input shapes is still allowed
but slower.</p></li>
<li><p><strong>inline_limit</strong> (<em>optional int</em><em>, </em><em>default 2</em>) – Maximum number of operators that can be inlined.</p></li>
<li><p><strong>forward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during forward pass.</p></li>
<li><p><strong>backward_bulk_size</strong> (<em>optional int</em><em>, </em><em>default None</em>) – Segment size of bulk execution during forward pass.</p></li>
<li><p><strong>**kwargs</strong> (<em>The backend options</em><em>, </em><em>optional</em>) – Passed on to <cite>PrePartition</cite> and <cite>PostPartition</cite> functions of <cite>SubgraphProperty</cite></p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.SymbolBlock.params">
<em class="property">property </em><code class="sig-name descname">params</code><a class="headerlink" href="#mxnet.gluon.SymbolBlock.params" title="Permalink to this definition"></a></dt>
<dd><p>Returns this <a class="reference internal" href="block.html#mxnet.gluon.Block" title="mxnet.gluon.Block"><code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code></a>’s parameter dictionary (does not include its
children’s parameters).</p>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.SymbolBlock.prefix">
<em class="property">property </em><code class="sig-name descname">prefix</code><a class="headerlink" href="#mxnet.gluon.SymbolBlock.prefix" title="Permalink to this definition"></a></dt>
<dd><p>Prefix of this <a class="reference internal" href="block.html#mxnet.gluon.Block" title="mxnet.gluon.Block"><code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code></a>.</p>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.SymbolBlock.register_child">
<code class="sig-name descname">register_child</code><span class="sig-paren">(</span><em class="sig-param">block</em>, <em class="sig-param">name=None</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.SymbolBlock.register_child" title="Permalink to this definition"></a></dt>
<dd><p>Registers block as a child of self. <a class="reference internal" href="block.html#mxnet.gluon.Block" title="mxnet.gluon.Block"><code class="xref py py-class docutils literal notranslate"><span class="pre">Block</span></code></a> s assigned to self as
attributes will be registered automatically.</p>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.SymbolBlock.register_forward_hook">
<code class="sig-name descname">register_forward_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.SymbolBlock.register_forward_hook" title="Permalink to this definition"></a></dt>
<dd><p>Registers a forward hook on the block.</p>
<p>The hook function is called immediately after <a class="reference internal" href="#mxnet.gluon.SymbolBlock.forward" title="mxnet.gluon.SymbolBlock.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>.
It should not modify the input or output.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input, output) -&gt; None</cite>.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.SymbolBlock.register_forward_pre_hook">
<code class="sig-name descname">register_forward_pre_hook</code><span class="sig-paren">(</span><em class="sig-param">hook</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.SymbolBlock.register_forward_pre_hook" title="Permalink to this definition"></a></dt>
<dd><p>Registers a forward pre-hook on the block.</p>
<p>The hook function is called immediately before <a class="reference internal" href="#mxnet.gluon.SymbolBlock.forward" title="mxnet.gluon.SymbolBlock.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>.
It should not modify the input or output.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>hook</strong> (<em>callable</em>) – The forward hook function of form <cite>hook(block, input) -&gt; None</cite>.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.utils.HookHandle</span></code></p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.SymbolBlock.register_op_hook">
<code class="sig-name descname">register_op_hook</code><span class="sig-paren">(</span><em class="sig-param">callback</em>, <em class="sig-param">monitor_all=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.SymbolBlock.register_op_hook" title="Permalink to this definition"></a></dt>
<dd><p>Install op hook for block recursively.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>callback</strong> (<em>function</em>) – Takes a string and a NDArrayHandle.</p></li>
<li><p><strong>monitor_all</strong> (<em>bool</em><em>, </em><em>default False</em>) – If true, monitor both input and output, otherwise monitor output only.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.SymbolBlock.reset_ctx">
<code class="sig-name descname">reset_ctx</code><span class="sig-paren">(</span><em class="sig-param">ctx</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/gluon/block.html#SymbolBlock.reset_ctx"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.SymbolBlock.reset_ctx" title="Permalink to this definition"></a></dt>
<dd><p>Re-assign all Parameters to other contexts. If the Block is hybridized, it will reset the _cached_op_args.
:param ctx: Assign Parameter to given context. If ctx is a list of Context, a</p>
<blockquote>
<div><p>copy will be made for each context.</p>
</div></blockquote>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.SymbolBlock.save_parameters">
<code class="sig-name descname">save_parameters</code><span class="sig-paren">(</span><em class="sig-param">filename</em>, <em class="sig-param">deduplicate=False</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.SymbolBlock.save_parameters" title="Permalink to this definition"></a></dt>
<dd><p>Save parameters to file.</p>
<p>Saved parameters can only be loaded with <cite>load_parameters</cite>. Note that this
method only saves parameters, not model structure. If you want to save
model structures, please use <a class="reference internal" href="hybrid_block.html#mxnet.gluon.HybridBlock.export" title="mxnet.gluon.HybridBlock.export"><code class="xref py py-meth docutils literal notranslate"><span class="pre">HybridBlock.export()</span></code></a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>filename</strong> (<em>str</em>) – Path to file.</p></li>
<li><p><strong>deduplicate</strong> (<em>bool</em><em>, </em><em>default False</em>) – If True, save shared parameters only once. Otherwise, if a Block
contains multiple sub-blocks that share parameters, each of the
shared parameters will be separately saved for every sub-block.</p></li>
</ul>
</dd>
</dl>
<p class="rubric">References</p>
<p><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></p>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.SymbolBlock.save_params">
<code class="sig-name descname">save_params</code><span class="sig-paren">(</span><em class="sig-param">filename</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.SymbolBlock.save_params" title="Permalink to this definition"></a></dt>
<dd><p>[Deprecated] Please use save_parameters. Note that if you want load
from SymbolBlock later, please use export instead.</p>
<p>Save parameters to file.</p>
<dl class="simple">
<dt>filename<span class="classifier">str</span></dt><dd><p>Path to file.</p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="mxnet.gluon.SymbolBlock.summary">
<code class="sig-name descname">summary</code><span class="sig-paren">(</span><em class="sig-param">*inputs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.gluon.SymbolBlock.summary" title="Permalink to this definition"></a></dt>
<dd><p>Print the summary of the model’s output and parameters.</p>
<p>The network must have been initialized, and must not have been hybridized.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>inputs</strong> (<em>object</em>) – Any input that the model supports. For any tensor in the input, only
<a class="reference internal" href="../ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.ndarray.NDArray</span></code></a> is supported.</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
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