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<li class="toctree-l4"><a class="reference internal" href="../../../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="../../../getting-started/crash-course/2-nn.html">Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li>
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<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-l5"><a class="reference internal" href="../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="../../ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../ndarray/sparse/index.html">Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../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="../../onnx/super_resolution.html">Importing an ONNX model into MXNet</a></li>
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<li class="toctree-l1 current"><a class="reference internal" href="../../../index.html">Python Tutorials</a><ul class="current">
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<li class="toctree-l3"><a class="reference internal" href="../../../getting-started/crash-course/index.html">Crash Course</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../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="../../../getting-started/crash-course/2-nn.html">Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../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="../../../getting-started/crash-course/4-train.html">Train the neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../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="../../../getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../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="../../../getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../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="../../autograd/index.html">Automatic Differentiation</a></li>
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<li class="toctree-l4 current"><a class="reference internal" href="index.html">Blocks</a><ul class="current">
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<li class="toctree-l5"><a class="reference internal" href="init.html">Initialization</a></li>
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<li class="toctree-l5"><a class="reference internal" href="parameters.html">Parameter Management</a></li>
<li class="toctree-l5"><a class="reference internal" href="save_load_params.html">Saving and Loading Gluon Models</a></li>
<li class="toctree-l5"><a class="reference internal" href="activations/activations.html">Activation Blocks</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../data/data_augmentation.html">Image Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li>
<li class="toctree-l5"><a class="reference internal" href="../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="../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="../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="../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="../image/info_gan.html">Image similarity search with InfoGAN</a></li>
<li class="toctree-l5"><a class="reference internal" href="../image/mnist.html">Handwritten Digit Recognition</a></li>
<li class="toctree-l5"><a class="reference internal" href="../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="../loss/index.html">Losses</a><ul>
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<li class="toctree-l5"><a class="reference internal" href="../loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li>
<li class="toctree-l5"><a class="reference internal" href="../loss/loss.html">Loss functions</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../text/transformer.html">Machine Translation with Transformer</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../training/learning_rates/index.html">Learning Rates</a><ul>
<li class="toctree-l6"><a class="reference internal" href="../training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li>
<li class="toctree-l6"><a class="reference internal" href="../training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li>
<li class="toctree-l6"><a class="reference internal" href="../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="../training/normalization/index.html">Normalization Blocks</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../kvstore/kvstore.html">Distributed Key-Value Store</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../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="../../ndarray/02-ndarray-operations.html">NDArray Operations</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../ndarray/sparse/index.html">Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../onnx/index.html">ONNX</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../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="../../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-l3"><a class="reference internal" href="../../viz/index.html">Visualization</a><ul>
<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../../performance/index.html">Performance</a><ul>
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<li class="toctree-l4"><a class="reference internal" href="../../../performance/compression/int8.html">Deploy with int-8</a></li>
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<!--- Licensed to the Apache Software Foundation (ASF) under one --><!--- or more contributor license agreements. See the NOTICE file --><!--- distributed with this work for additional information --><!--- regarding copyright ownership. The ASF licenses this file --><!--- to you under the Apache License, Version 2.0 (the --><!--- "License"); you may not use this file except in compliance --><!--- with the License. You may obtain a copy of the License at --><!--- http://www.apache.org/licenses/LICENSE-2.0 --><!--- Unless required by applicable law or agreed to in writing, --><!--- software distributed under the License is distributed on an --><!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY --><!--- KIND, either express or implied. See the License for the --><!--- specific language governing permissions and limitations --><!--- under the License. --><div class="section" id="Custom-Layers">
<h1>Custom Layers<a class="headerlink" href="#Custom-Layers" title="Permalink to this headline"></a></h1>
<!-- adapted from diveintodeeplearning --><p>One of the reasons for the success of deep learning can be found in the wide range of re-usable layers that can be used in a deep network. This allows for a tremendous degree of customization and adaptation. Sooner or later you will encounter a layer that doesn’t exist yet in Gluon or one that you want to create. This is when it’s time to build a custom layer. This section shows you how.</p>
<p>Defining a layer is as easy as subclassing <a class="reference external" href="/api/gluon/mxnet.gluon.nn.Block.html#mxnet.gluon.nn.Block">nn.Block</a> or <a class="reference external" href="/api/gluon/mxnet.gluon.nn.HybridBlock.html#mxnet.gluon.nn.HybridBlock">nn.HybridBlock</a> and implementing <code class="docutils literal notranslate"><span class="pre">forward</span></code> or <code class="docutils literal notranslate"><span class="pre">hybrid_forward</span></code>, respectively. To take advantage of the performance gains with <code class="docutils literal notranslate"><span class="pre">nn.HybridBlock</span></code> see the section on <a class="reference external" href="hybridize.html">Hybridization</a>.</p>
<p>Note that we’ve gone through rationale for defining layers, but <code class="docutils literal notranslate"><span class="pre">nn.Block</span></code>’s work even for non-sequential network. In fact, you can use a <code class="docutils literal notranslate"><span class="pre">Block</span></code> to encapsualte any re-usable architecture you want.</p>
<p>We will discuss making custom layers using <code class="docutils literal notranslate"><span class="pre">nn.Block</span></code> below.</p>
<div class="section" id="Layers-without-Parameters">
<h2>Layers without Parameters<a class="headerlink" href="#Layers-without-Parameters" title="Permalink to this headline"></a></h2>
<p>Since this is slightly intricate, we start with a custom layer that doesn’t have any inherent parameters. Our first step is very similar to when we <a class="reference internal" href="nn.html"><span class="doc">introduced blocks</span></a> previously. The following <code class="docutils literal notranslate"><span class="pre">CenteredLayer</span></code> class constructs a layer that subtracts the mean from the input. We build it by inheriting from the <code class="docutils literal notranslate"><span class="pre">Block</span></code> class and overriding the <code class="docutils literal notranslate"><span class="pre">forward</span></code> and <code class="docutils literal notranslate"><span class="pre">__init__</span></code> methods.</p>
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<span></span><span class="kn">from</span> <span class="nn">mxnet</span> <span class="kn">import</span> <span class="n">gluon</span><span class="p">,</span> <span class="n">nd</span>
<span class="kn">from</span> <span class="nn">mxnet.gluon</span> <span class="kn">import</span> <span class="n">nn</span>
<span class="k">class</span> <span class="nc">CenteredLayer</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Block</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">CenteredLayer</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">-</span> <span class="n">x</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
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</div>
<p>To see how it works let’s feed some data into the layer.</p>
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<span></span><span class="n">layer</span> <span class="o">=</span> <span class="n">CenteredLayer</span><span class="p">()</span>
<span class="n">layer</span><span class="p">(</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">layer</span><span class="p">)</span>
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<p>We can also use it to construct more complex models.</p>
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<span></span><span class="n">net</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span>
<span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">128</span><span class="p">),</span>
<span class="n">CenteredLayer</span><span class="p">())</span>
<span class="n">net</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">net</span><span class="p">)</span>
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<p>Let’s see whether the centering layer did its job. For that we send random data through the network and check whether the mean is <span class="math notranslate nohighlight">\(0\)</span>. Note that since we’re dealing with floating point numbers, we’re going to see a very small albeit typically nonzero number.</p>
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<span></span><span class="n">y</span> <span class="o">=</span> <span class="n">net</span><span class="p">(</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">)))</span>
<span class="n">y</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">asscalar</span><span class="p">()</span>
</pre></div>
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</div>
</div>
<div class="section" id="Layers-with-Parameters">
<h2>Layers with Parameters<a class="headerlink" href="#Layers-with-Parameters" title="Permalink to this headline"></a></h2>
<p>Now that we know how to define layers in principle, let’s define layers with parameters. These can be adjusted through training. In order to simplify things for an avid deep learning researcher, the <a class="reference external" href="/api/gluon/mxnet.gluon.parameter.html">Parameter</a> class and the <code class="docutils literal notranslate"><span class="pre">ParameterDict</span></code> dictionary provide some basic housekeeping functionality. In particular, they govern access, initialization, sharing, saving and loading model parameters. For instance, this way we don’t need to write custom
serialization routines for each new custom layer.</p>
<p>We can access the parameters via the <code class="docutils literal notranslate"><span class="pre">params</span></code> variable of the <code class="docutils literal notranslate"><span class="pre">ParameterDict</span></code> in <code class="docutils literal notranslate"><span class="pre">Block</span></code>. The parameter dictionary is just that - a dictionary that maps string type parameter names to model parameters in the <code class="docutils literal notranslate"><span class="pre">Parameter</span></code> type. We can create a <code class="docutils literal notranslate"><span class="pre">Parameter</span></code> instance from <code class="docutils literal notranslate"><span class="pre">ParameterDict</span></code> via the <code class="docutils literal notranslate"><span class="pre">get</span></code> function which attempts to retrieve a parameter, or create it if not found.</p>
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<span></span><span class="n">params</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">ParameterDict</span><span class="p">()</span>
<span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;param2&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">params</span><span class="p">)</span>
</pre></div>
</div>
</div>
<p>Let’s use this to implement our own version of the dense layer. It has two parameters - bias and weight. To make it a bit nonstandard, we bake in the ReLU activation as default. Next, we implement a fully connected layer with both weight and bias parameters. It uses ReLU as an activation function, where <code class="docutils literal notranslate"><span class="pre">in_units</span></code> and <code class="docutils literal notranslate"><span class="pre">units</span></code> are the number of inputs and the number of outputs, respectively.</p>
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<span></span><span class="k">class</span> <span class="nc">MyDense</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Block</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">units</span><span class="p">,</span> <span class="n">in_units</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="c1"># units: the number of outputs in this layer</span>
<span class="c1"># in_units: the number of inputs in this layer</span>
<span class="nb">super</span><span class="p">(</span><span class="n">MyDense</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;weight&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">in_units</span><span class="p">,</span> <span class="n">units</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;bias&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">units</span><span class="p">,))</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="n">linear</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">())</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="p">()</span>
<span class="k">return</span> <span class="n">nd</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">linear</span><span class="p">)</span>
</pre></div>
</div>
</div>
<p>Naming the parameters allows us to access them by name through dictionary lookup later. It’s a good idea to give them instructive names. Next, we instantiate the <code class="docutils literal notranslate"><span class="pre">MyDense</span></code> class and access its model parameters.</p>
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<span></span><span class="n">dense</span> <span class="o">=</span> <span class="n">MyDense</span><span class="p">(</span><span class="n">units</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">in_units</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="n">dense</span><span class="o">.</span><span class="n">params</span>
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</div>
</div>
<p>We can directly carry out forward calculations using custom layers.</p>
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<span></span><span class="n">dense</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span>
<span class="n">dense</span><span class="p">(</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">dense</span><span class="p">)</span>
</pre></div>
</div>
</div>
<p>We can also construct models using custom layers. Once we have that we can use it just like the built-in dense layer. The only exception is that in our case, shape inference is not automatic as we have explicitly defined the shape of the weight matrix during initialization.</p>
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<span></span><span class="n">net</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span>
<span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MyDense</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="n">in_units</span><span class="o">=</span><span class="mi">64</span><span class="p">),</span>
<span class="n">MyDense</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">in_units</span><span class="o">=</span><span class="mi">8</span><span class="p">))</span>
<span class="n">net</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span>
<span class="n">net</span><span class="p">(</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">64</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">net</span><span class="p">)</span>
</pre></div>
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</div>
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<li><a class="reference internal" href="#">Custom Layers</a><ul>
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<li><a class="reference internal" href="#Layers-with-Parameters">Layers with Parameters</a></li>
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