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<div id="jb-print-docs-body" class="onlyprint">
<h1>Neural Networks</h1>
<!-- Table of contents -->
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#quick-start-with-neural-networks">Quick Start with Neural Networks</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#the-module-class">The Module Class</a><ul class="visible nav section-nav flex-column">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#parameters">Parameters</a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#updating-the-parameters">Updating the Parameters</a></li>
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<section id="neural-networks">
<span id="nn"></span><h1>Neural Networks<a class="headerlink" href="#neural-networks" title="Link to this heading">#</a></h1>
<p>Writing arbitrarily complex neural networks in MLX can be done using only
<a class="reference internal" href="_autosummary/mlx.core.array.html#mlx.core.array" title="mlx.core.array"><code class="xref py py-class docutils literal notranslate"><span class="pre">mlx.core.array</span></code></a> and <a class="reference internal" href="_autosummary/mlx.core.value_and_grad.html#mlx.core.value_and_grad" title="mlx.core.value_and_grad"><code class="xref py py-meth docutils literal notranslate"><span class="pre">mlx.core.value_and_grad()</span></code></a>. However, this requires the
user to write again and again the same simple neural network operations as well
as handle all the parameter state and initialization manually and explicitly.</p>
<p>The module <code class="xref py py-mod docutils literal notranslate"><span class="pre">mlx.nn</span></code> solves this problem by providing an intuitive way of
composing neural network layers, initializing their parameters, freezing them
for finetuning and more.</p>
<section id="quick-start-with-neural-networks">
<h2>Quick Start with Neural Networks<a class="headerlink" href="#quick-start-with-neural-networks" title="Link to this heading">#</a></h2>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mlx.core</span> <span class="k">as</span> <span class="nn">mx</span>
<span class="kn">import</span> <span class="nn">mlx.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="k">class</span> <span class="nc">MLP</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</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">in_dims</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">out_dims</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">layers</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">in_dims</span><span class="p">,</span> <span class="mi">128</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="n">out_dims</span><span class="p">),</span>
<span class="p">]</span>
<span class="k">def</span> <span class="fm">__call__</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">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">l</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">x</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">l</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="c1"># The model is created with all its parameters but nothing is initialized</span>
<span class="c1"># yet because MLX is lazily evaluated</span>
<span class="n">mlp</span> <span class="o">=</span> <span class="n">MLP</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="c1"># We can access its parameters by calling mlp.parameters()</span>
<span class="n">params</span> <span class="o">=</span> <span class="n">mlp</span><span class="o">.</span><span class="n">parameters</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="s2">&quot;layers&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="s2">&quot;weight&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="c1"># Printing a parameter will cause it to be evaluated and thus initialized</span>
<span class="nb">print</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="s2">&quot;layers&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
<span class="c1"># We can also force evaluate all parameters to initialize the model</span>
<span class="n">mx</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">mlp</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span>
<span class="c1"># A simple loss function.</span>
<span class="c1"># NOTE: It doesn&#39;t matter how it uses the mlp model. It currently captures</span>
<span class="c1"># it from the local scope. It could be a positional argument or a</span>
<span class="c1"># keyword argument.</span>
<span class="k">def</span> <span class="nf">l2_loss</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">y_hat</span> <span class="o">=</span> <span class="n">mlp</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="p">(</span><span class="n">y_hat</span> <span class="o">-</span> <span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">square</span><span class="p">()</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="c1"># Calling `nn.value_and_grad` instead of `mx.value_and_grad` returns the</span>
<span class="c1"># gradient with respect to `mlp.trainable_parameters()`</span>
<span class="n">loss_and_grad</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">value_and_grad</span><span class="p">(</span><span class="n">mlp</span><span class="p">,</span> <span class="n">l2_loss</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="the-module-class">
<span id="module-class"></span><h2>The Module Class<a class="headerlink" href="#the-module-class" title="Link to this heading">#</a></h2>
<p>The workhorse of any neural network library is the <a class="reference internal" href="nn/module.html#mlx.nn.Module" title="mlx.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a> class. In
MLX the <a class="reference internal" href="nn/module.html#mlx.nn.Module" title="mlx.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a> class is a container of <a class="reference internal" href="_autosummary/mlx.core.array.html#mlx.core.array" title="mlx.core.array"><code class="xref py py-class docutils literal notranslate"><span class="pre">mlx.core.array</span></code></a> or
<a class="reference internal" href="nn/module.html#mlx.nn.Module" title="mlx.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a> instances. Its main function is to provide a way to
recursively <strong>access</strong> and <strong>update</strong> its parameters and those of its
submodules.</p>
<section id="parameters">
<h3>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">#</a></h3>
<p>A parameter of a module is any public member of type <a class="reference internal" href="_autosummary/mlx.core.array.html#mlx.core.array" title="mlx.core.array"><code class="xref py py-class docutils literal notranslate"><span class="pre">mlx.core.array</span></code></a> (its
name should not start with <code class="docutils literal notranslate"><span class="pre">_</span></code>). It can be arbitrarily nested in other
<a class="reference internal" href="nn/module.html#mlx.nn.Module" title="mlx.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a> instances or lists and dictionaries.</p>
<p><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.parameters.html#mlx.nn.Module.parameters" title="mlx.nn.Module.parameters"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Module.parameters()</span></code></a> can be used to extract a nested dictionary with all
the parameters of a module and its submodules.</p>
<p>A <a class="reference internal" href="nn/module.html#mlx.nn.Module" title="mlx.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a> can also keep track of “frozen” parameters. See the
<a class="reference internal" href="nn/_autosummary/mlx.nn.Module.freeze.html#mlx.nn.Module.freeze" title="mlx.nn.Module.freeze"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Module.freeze()</span></code></a> method for more details. <a class="reference internal" href="_autosummary/mlx.nn.value_and_grad.html#mlx.nn.value_and_grad" title="mlx.nn.value_and_grad"><code class="xref py py-meth docutils literal notranslate"><span class="pre">mlx.nn.value_and_grad()</span></code></a>
the gradients returned will be with respect to these trainable parameters.</p>
</section>
<section id="updating-the-parameters">
<h3>Updating the Parameters<a class="headerlink" href="#updating-the-parameters" title="Link to this heading">#</a></h3>
<p>MLX modules allow accessing and updating individual parameters. However, most
times we need to update large subsets of a module’s parameters. This action is
performed by <a class="reference internal" href="nn/_autosummary/mlx.nn.Module.update.html#mlx.nn.Module.update" title="mlx.nn.Module.update"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Module.update()</span></code></a>.</p>
</section>
<section id="inspecting-modules">
<h3>Inspecting Modules<a class="headerlink" href="#inspecting-modules" title="Link to this heading">#</a></h3>
<p>The simplest way to see the model architecture is to print it. Following along with
the above example, you can print the <code class="docutils literal notranslate"><span class="pre">MLP</span></code> with:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">mlp</span><span class="p">)</span>
</pre></div>
</div>
<p>This will display:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>MLP<span class="o">(</span>
<span class="w"> </span><span class="o">(</span>layers.0<span class="o">)</span>:<span class="w"> </span>Linear<span class="o">(</span><span class="nv">input_dims</span><span class="o">=</span><span class="m">2</span>,<span class="w"> </span><span class="nv">output_dims</span><span class="o">=</span><span class="m">128</span>,<span class="w"> </span><span class="nv">bias</span><span class="o">=</span>True<span class="o">)</span>
<span class="w"> </span><span class="o">(</span>layers.1<span class="o">)</span>:<span class="w"> </span>Linear<span class="o">(</span><span class="nv">input_dims</span><span class="o">=</span><span class="m">128</span>,<span class="w"> </span><span class="nv">output_dims</span><span class="o">=</span><span class="m">128</span>,<span class="w"> </span><span class="nv">bias</span><span class="o">=</span>True<span class="o">)</span>
<span class="w"> </span><span class="o">(</span>layers.2<span class="o">)</span>:<span class="w"> </span>Linear<span class="o">(</span><span class="nv">input_dims</span><span class="o">=</span><span class="m">128</span>,<span class="w"> </span><span class="nv">output_dims</span><span class="o">=</span><span class="m">10</span>,<span class="w"> </span><span class="nv">bias</span><span class="o">=</span>True<span class="o">)</span>
<span class="o">)</span>
</pre></div>
</div>
<p>To get more detailed information on the arrays in a <a class="reference internal" href="nn/module.html#mlx.nn.Module" title="mlx.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a> you can use
<a class="reference internal" href="_autosummary/mlx.utils.tree_map.html#mlx.utils.tree_map" title="mlx.utils.tree_map"><code class="xref py py-func docutils literal notranslate"><span class="pre">mlx.utils.tree_map()</span></code></a> on the parameters. For example, to see the shapes of
all the parameters in a <a class="reference internal" href="nn/module.html#mlx.nn.Module" title="mlx.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a> do:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">mlx.utils</span> <span class="kn">import</span> <span class="n">tree_map</span>
<span class="n">shapes</span> <span class="o">=</span> <span class="n">tree_map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">p</span><span class="p">:</span> <span class="n">p</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">mlp</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span>
</pre></div>
</div>
<p>As another example, you can count the number of parameters in a <a class="reference internal" href="nn/module.html#mlx.nn.Module" title="mlx.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a>
with:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">mlx.utils</span> <span class="kn">import</span> <span class="n">tree_flatten</span>
<span class="n">num_params</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">size</span> <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">tree_flatten</span><span class="p">(</span><span class="n">mlp</span><span class="o">.</span><span class="n">parameters</span><span class="p">()))</span>
</pre></div>
</div>
</section>
</section>
<section id="value-and-grad">
<h2>Value and Grad<a class="headerlink" href="#value-and-grad" title="Link to this heading">#</a></h2>
<p>Using a <a class="reference internal" href="nn/module.html#mlx.nn.Module" title="mlx.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a> does not preclude using MLX’s high order function
transformations (<a class="reference internal" href="_autosummary/mlx.core.value_and_grad.html#mlx.core.value_and_grad" title="mlx.core.value_and_grad"><code class="xref py py-meth docutils literal notranslate"><span class="pre">mlx.core.value_and_grad()</span></code></a>, <a class="reference internal" href="_autosummary/mlx.core.grad.html#mlx.core.grad" title="mlx.core.grad"><code class="xref py py-meth docutils literal notranslate"><span class="pre">mlx.core.grad()</span></code></a>, etc.). However,
these function transformations assume pure functions, namely the parameters
should be passed as an argument to the function being transformed.</p>
<p>There is an easy pattern to achieve that with MLX modules</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="o">...</span>
<span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">other_inputs</span><span class="p">):</span>
<span class="n">model</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">params</span><span class="p">)</span> <span class="c1"># &lt;---- Necessary to make the model use the passed parameters</span>
<span class="k">return</span> <span class="n">model</span><span class="p">(</span><span class="n">other_inputs</span><span class="p">)</span>
<span class="n">f</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">trainable_parameters</span><span class="p">(),</span> <span class="n">mx</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">10</span><span class="p">,)))</span>
</pre></div>
</div>
<p>However, <a class="reference internal" href="_autosummary/mlx.nn.value_and_grad.html#mlx.nn.value_and_grad" title="mlx.nn.value_and_grad"><code class="xref py py-meth docutils literal notranslate"><span class="pre">mlx.nn.value_and_grad()</span></code></a> provides precisely this pattern and only
computes the gradients with respect to the trainable parameters of the model.</p>
<p>In detail:</p>
<ul class="simple">
<li><p>it wraps the passed function with a function that calls <a class="reference internal" href="nn/_autosummary/mlx.nn.Module.update.html#mlx.nn.Module.update" title="mlx.nn.Module.update"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Module.update()</span></code></a>
to make sure the model is using the provided parameters.</p></li>
<li><p>it calls <a class="reference internal" href="_autosummary/mlx.core.value_and_grad.html#mlx.core.value_and_grad" title="mlx.core.value_and_grad"><code class="xref py py-meth docutils literal notranslate"><span class="pre">mlx.core.value_and_grad()</span></code></a> to transform the function into a function
that also computes the gradients with respect to the passed parameters.</p></li>
<li><p>it wraps the returned function with a function that passes the trainable
parameters as the first argument to the function returned by
<a class="reference internal" href="_autosummary/mlx.core.value_and_grad.html#mlx.core.value_and_grad" title="mlx.core.value_and_grad"><code class="xref py py-meth docutils literal notranslate"><span class="pre">mlx.core.value_and_grad()</span></code></a></p></li>
</ul>
<table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.value_and_grad.html#mlx.nn.value_and_grad" title="mlx.nn.value_and_grad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">value_and_grad</span></code></a>(model, fn)</p></td>
<td><p>Transform the passed function <code class="docutils literal notranslate"><span class="pre">fn</span></code> to a function that computes the gradients of <code class="docutils literal notranslate"><span class="pre">fn</span></code> wrt the model's trainable parameters and also its value.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.quantize.html#mlx.nn.quantize" title="mlx.nn.quantize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantize</span></code></a>(model[, group_size, bits, ...])</p></td>
<td><p>Quantize the sub-modules of a module according to a predicate.</p></td>
</tr>
</tbody>
</table>
<div class="toctree-wrapper compound">
<ul>
<li class="toctree-l1"><a class="reference internal" href="nn/module.html">Module</a><ul>
<li class="toctree-l2"><a class="reference internal" href="nn/module.html#mlx.nn.Module"><code class="docutils literal notranslate"><span class="pre">Module</span></code></a></li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.training.html">mlx.nn.Module.training</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.training.html#mlx.nn.Module.training"><code class="docutils literal notranslate"><span class="pre">Module.training</span></code></a></li>
</ul>
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<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.apply_to_modules.html#mlx.nn.Module.apply_to_modules"><code class="docutils literal notranslate"><span class="pre">Module.apply_to_modules()</span></code></a></li>
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<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.children.html#mlx.nn.Module.children"><code class="docutils literal notranslate"><span class="pre">Module.children()</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.eval.html">mlx.nn.Module.eval</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.eval.html#mlx.nn.Module.eval"><code class="docutils literal notranslate"><span class="pre">Module.eval()</span></code></a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.filter_and_map.html">mlx.nn.Module.filter_and_map</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.filter_and_map.html#mlx.nn.Module.filter_and_map"><code class="docutils literal notranslate"><span class="pre">Module.filter_and_map()</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.freeze.html">mlx.nn.Module.freeze</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.freeze.html#mlx.nn.Module.freeze"><code class="docutils literal notranslate"><span class="pre">Module.freeze()</span></code></a></li>
</ul>
</li>
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<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.leaf_modules.html#mlx.nn.Module.leaf_modules"><code class="docutils literal notranslate"><span class="pre">Module.leaf_modules()</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.load_weights.html">mlx.nn.Module.load_weights</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.load_weights.html#mlx.nn.Module.load_weights"><code class="docutils literal notranslate"><span class="pre">Module.load_weights()</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.modules.html">mlx.nn.Module.modules</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.modules.html#mlx.nn.Module.modules"><code class="docutils literal notranslate"><span class="pre">Module.modules()</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.named_modules.html">mlx.nn.Module.named_modules</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.named_modules.html#mlx.nn.Module.named_modules"><code class="docutils literal notranslate"><span class="pre">Module.named_modules()</span></code></a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.parameters.html">mlx.nn.Module.parameters</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.parameters.html#mlx.nn.Module.parameters"><code class="docutils literal notranslate"><span class="pre">Module.parameters()</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.save_weights.html">mlx.nn.Module.save_weights</a><ul>
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</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.set_dtype.html">mlx.nn.Module.set_dtype</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.set_dtype.html#mlx.nn.Module.set_dtype"><code class="docutils literal notranslate"><span class="pre">Module.set_dtype()</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.train.html">mlx.nn.Module.train</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.train.html#mlx.nn.Module.train"><code class="docutils literal notranslate"><span class="pre">Module.train()</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.trainable_parameters.html">mlx.nn.Module.trainable_parameters</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.trainable_parameters.html#mlx.nn.Module.trainable_parameters"><code class="docutils literal notranslate"><span class="pre">Module.trainable_parameters()</span></code></a></li>
</ul>
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<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.unfreeze.html#mlx.nn.Module.unfreeze"><code class="docutils literal notranslate"><span class="pre">Module.unfreeze()</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.update.html">mlx.nn.Module.update</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.update.html#mlx.nn.Module.update"><code class="docutils literal notranslate"><span class="pre">Module.update()</span></code></a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.update_modules.html">mlx.nn.Module.update_modules</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.update_modules.html#mlx.nn.Module.update_modules"><code class="docutils literal notranslate"><span class="pre">Module.update_modules()</span></code></a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="nn/layers.html">Layers</a><ul>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.ALiBi.html">mlx.nn.ALiBi</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.ALiBi.html#mlx.nn.ALiBi"><code class="docutils literal notranslate"><span class="pre">ALiBi</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.AvgPool1d.html">mlx.nn.AvgPool1d</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.AvgPool1d.html#mlx.nn.AvgPool1d"><code class="docutils literal notranslate"><span class="pre">AvgPool1d</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.AvgPool2d.html">mlx.nn.AvgPool2d</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.AvgPool2d.html#mlx.nn.AvgPool2d"><code class="docutils literal notranslate"><span class="pre">AvgPool2d</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.BatchNorm.html">mlx.nn.BatchNorm</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.BatchNorm.html#mlx.nn.BatchNorm"><code class="docutils literal notranslate"><span class="pre">BatchNorm</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Conv1d.html">mlx.nn.Conv1d</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Conv1d.html#mlx.nn.Conv1d"><code class="docutils literal notranslate"><span class="pre">Conv1d</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Conv2d.html">mlx.nn.Conv2d</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Conv2d.html#mlx.nn.Conv2d"><code class="docutils literal notranslate"><span class="pre">Conv2d</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Conv3d.html">mlx.nn.Conv3d</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Conv3d.html#mlx.nn.Conv3d"><code class="docutils literal notranslate"><span class="pre">Conv3d</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Dropout.html">mlx.nn.Dropout</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Dropout.html#mlx.nn.Dropout"><code class="docutils literal notranslate"><span class="pre">Dropout</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Dropout2d.html">mlx.nn.Dropout2d</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Dropout2d.html#mlx.nn.Dropout2d"><code class="docutils literal notranslate"><span class="pre">Dropout2d</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Dropout3d.html">mlx.nn.Dropout3d</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Dropout3d.html#mlx.nn.Dropout3d"><code class="docutils literal notranslate"><span class="pre">Dropout3d</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Embedding.html">mlx.nn.Embedding</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Embedding.html#mlx.nn.Embedding"><code class="docutils literal notranslate"><span class="pre">Embedding</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.GELU.html">mlx.nn.GELU</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.GELU.html#mlx.nn.GELU"><code class="docutils literal notranslate"><span class="pre">GELU</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.GLU.html">mlx.nn.GLU</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.GLU.html#mlx.nn.GLU"><code class="docutils literal notranslate"><span class="pre">GLU</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.GroupNorm.html">mlx.nn.GroupNorm</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.GroupNorm.html#mlx.nn.GroupNorm"><code class="docutils literal notranslate"><span class="pre">GroupNorm</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.GRU.html">mlx.nn.GRU</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.GRU.html#mlx.nn.GRU"><code class="docutils literal notranslate"><span class="pre">GRU</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.HardShrink.html">mlx.nn.HardShrink</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.HardShrink.html#mlx.nn.HardShrink"><code class="docutils literal notranslate"><span class="pre">HardShrink</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.HardTanh.html">mlx.nn.HardTanh</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.HardTanh.html#mlx.nn.HardTanh"><code class="docutils literal notranslate"><span class="pre">HardTanh</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Hardswish.html">mlx.nn.Hardswish</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Hardswish.html#mlx.nn.Hardswish"><code class="docutils literal notranslate"><span class="pre">Hardswish</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.InstanceNorm.html">mlx.nn.InstanceNorm</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.InstanceNorm.html#mlx.nn.InstanceNorm"><code class="docutils literal notranslate"><span class="pre">InstanceNorm</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.LayerNorm.html">mlx.nn.LayerNorm</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.LayerNorm.html#mlx.nn.LayerNorm"><code class="docutils literal notranslate"><span class="pre">LayerNorm</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.LeakyReLU.html">mlx.nn.LeakyReLU</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.LeakyReLU.html#mlx.nn.LeakyReLU"><code class="docutils literal notranslate"><span class="pre">LeakyReLU</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Linear.html">mlx.nn.Linear</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Linear.html#mlx.nn.Linear"><code class="docutils literal notranslate"><span class="pre">Linear</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.LSTM.html">mlx.nn.LSTM</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.LSTM.html#mlx.nn.LSTM"><code class="docutils literal notranslate"><span class="pre">LSTM</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.MaxPool1d.html">mlx.nn.MaxPool1d</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.MaxPool1d.html#mlx.nn.MaxPool1d"><code class="docutils literal notranslate"><span class="pre">MaxPool1d</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.MaxPool2d.html">mlx.nn.MaxPool2d</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.MaxPool2d.html#mlx.nn.MaxPool2d"><code class="docutils literal notranslate"><span class="pre">MaxPool2d</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Mish.html">mlx.nn.Mish</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Mish.html#mlx.nn.Mish"><code class="docutils literal notranslate"><span class="pre">Mish</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.MultiHeadAttention.html">mlx.nn.MultiHeadAttention</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.MultiHeadAttention.html#mlx.nn.MultiHeadAttention"><code class="docutils literal notranslate"><span class="pre">MultiHeadAttention</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.PReLU.html">mlx.nn.PReLU</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.PReLU.html#mlx.nn.PReLU"><code class="docutils literal notranslate"><span class="pre">PReLU</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.QuantizedEmbedding.html">mlx.nn.QuantizedEmbedding</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.QuantizedEmbedding.html#mlx.nn.QuantizedEmbedding"><code class="docutils literal notranslate"><span class="pre">QuantizedEmbedding</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.QuantizedLinear.html">mlx.nn.QuantizedLinear</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.QuantizedLinear.html#mlx.nn.QuantizedLinear"><code class="docutils literal notranslate"><span class="pre">QuantizedLinear</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.RMSNorm.html">mlx.nn.RMSNorm</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.RMSNorm.html#mlx.nn.RMSNorm"><code class="docutils literal notranslate"><span class="pre">RMSNorm</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.ReLU.html">mlx.nn.ReLU</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.ReLU.html#mlx.nn.ReLU"><code class="docutils literal notranslate"><span class="pre">ReLU</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.ReLU6.html">mlx.nn.ReLU6</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.ReLU6.html#mlx.nn.ReLU6"><code class="docutils literal notranslate"><span class="pre">ReLU6</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.RNN.html">mlx.nn.RNN</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.RNN.html#mlx.nn.RNN"><code class="docutils literal notranslate"><span class="pre">RNN</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.RoPE.html">mlx.nn.RoPE</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.RoPE.html#mlx.nn.RoPE"><code class="docutils literal notranslate"><span class="pre">RoPE</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.SELU.html">mlx.nn.SELU</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.SELU.html#mlx.nn.SELU"><code class="docutils literal notranslate"><span class="pre">SELU</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Sequential.html">mlx.nn.Sequential</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Sequential.html#mlx.nn.Sequential"><code class="docutils literal notranslate"><span class="pre">Sequential</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.SiLU.html">mlx.nn.SiLU</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.SiLU.html#mlx.nn.SiLU"><code class="docutils literal notranslate"><span class="pre">SiLU</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.SinusoidalPositionalEncoding.html">mlx.nn.SinusoidalPositionalEncoding</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.SinusoidalPositionalEncoding.html#mlx.nn.SinusoidalPositionalEncoding"><code class="docutils literal notranslate"><span class="pre">SinusoidalPositionalEncoding</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Softmin.html">mlx.nn.Softmin</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Softmin.html#mlx.nn.Softmin"><code class="docutils literal notranslate"><span class="pre">Softmin</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Softshrink.html">mlx.nn.Softshrink</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Softshrink.html#mlx.nn.Softshrink"><code class="docutils literal notranslate"><span class="pre">Softshrink</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Softsign.html">mlx.nn.Softsign</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Softsign.html#mlx.nn.Softsign"><code class="docutils literal notranslate"><span class="pre">Softsign</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Softmax.html">mlx.nn.Softmax</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Softmax.html#mlx.nn.Softmax"><code class="docutils literal notranslate"><span class="pre">Softmax</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Softplus.html">mlx.nn.Softplus</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Softplus.html#mlx.nn.Softplus"><code class="docutils literal notranslate"><span class="pre">Softplus</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Step.html">mlx.nn.Step</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Step.html#mlx.nn.Step"><code class="docutils literal notranslate"><span class="pre">Step</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Tanh.html">mlx.nn.Tanh</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Tanh.html#mlx.nn.Tanh"><code class="docutils literal notranslate"><span class="pre">Tanh</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Transformer.html">mlx.nn.Transformer</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Transformer.html#mlx.nn.Transformer"><code class="docutils literal notranslate"><span class="pre">Transformer</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Upsample.html">mlx.nn.Upsample</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.Upsample.html#mlx.nn.Upsample"><code class="docutils literal notranslate"><span class="pre">Upsample</span></code></a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="nn/functions.html">Functions</a><ul>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.elu.html">mlx.nn.elu</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.elu.html#mlx.nn.elu"><code class="docutils literal notranslate"><span class="pre">elu</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.gelu.html">mlx.nn.gelu</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.gelu.html#mlx.nn.gelu"><code class="docutils literal notranslate"><span class="pre">gelu</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.gelu_approx.html">mlx.nn.gelu_approx</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.gelu_approx.html#mlx.nn.gelu_approx"><code class="docutils literal notranslate"><span class="pre">gelu_approx</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.gelu_fast_approx.html">mlx.nn.gelu_fast_approx</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.gelu_fast_approx.html#mlx.nn.gelu_fast_approx"><code class="docutils literal notranslate"><span class="pre">gelu_fast_approx</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.glu.html">mlx.nn.glu</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.glu.html#mlx.nn.glu"><code class="docutils literal notranslate"><span class="pre">glu</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.hard_shrink.html">mlx.nn.hard_shrink</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.hard_shrink.html#mlx.nn.hard_shrink"><code class="docutils literal notranslate"><span class="pre">hard_shrink</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.hard_tanh.html">mlx.nn.hard_tanh</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.hard_tanh.html#mlx.nn.hard_tanh"><code class="docutils literal notranslate"><span class="pre">hard_tanh</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.hardswish.html">mlx.nn.hardswish</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.hardswish.html#mlx.nn.hardswish"><code class="docutils literal notranslate"><span class="pre">hardswish</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.leaky_relu.html">mlx.nn.leaky_relu</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.leaky_relu.html#mlx.nn.leaky_relu"><code class="docutils literal notranslate"><span class="pre">leaky_relu</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.log_sigmoid.html">mlx.nn.log_sigmoid</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.log_sigmoid.html#mlx.nn.log_sigmoid"><code class="docutils literal notranslate"><span class="pre">log_sigmoid</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.log_softmax.html">mlx.nn.log_softmax</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.log_softmax.html#mlx.nn.log_softmax"><code class="docutils literal notranslate"><span class="pre">log_softmax</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.mish.html">mlx.nn.mish</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.mish.html#mlx.nn.mish"><code class="docutils literal notranslate"><span class="pre">mish</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.prelu.html">mlx.nn.prelu</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.prelu.html#mlx.nn.prelu"><code class="docutils literal notranslate"><span class="pre">prelu</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.relu.html">mlx.nn.relu</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.relu.html#mlx.nn.relu"><code class="docutils literal notranslate"><span class="pre">relu</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.relu6.html">mlx.nn.relu6</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.relu6.html#mlx.nn.relu6"><code class="docutils literal notranslate"><span class="pre">relu6</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.selu.html">mlx.nn.selu</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.selu.html#mlx.nn.selu"><code class="docutils literal notranslate"><span class="pre">selu</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.sigmoid.html">mlx.nn.sigmoid</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.sigmoid.html#mlx.nn.sigmoid"><code class="docutils literal notranslate"><span class="pre">sigmoid</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.silu.html">mlx.nn.silu</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.silu.html#mlx.nn.silu"><code class="docutils literal notranslate"><span class="pre">silu</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.softmax.html">mlx.nn.softmax</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.softmax.html#mlx.nn.softmax"><code class="docutils literal notranslate"><span class="pre">softmax</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.softmin.html">mlx.nn.softmin</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.softmin.html#mlx.nn.softmin"><code class="docutils literal notranslate"><span class="pre">softmin</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.softplus.html">mlx.nn.softplus</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.softplus.html#mlx.nn.softplus"><code class="docutils literal notranslate"><span class="pre">softplus</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.softshrink.html">mlx.nn.softshrink</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.softshrink.html#mlx.nn.softshrink"><code class="docutils literal notranslate"><span class="pre">softshrink</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.step.html">mlx.nn.step</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.step.html#mlx.nn.step"><code class="docutils literal notranslate"><span class="pre">step</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.tanh.html">mlx.nn.tanh</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.tanh.html#mlx.nn.tanh"><code class="docutils literal notranslate"><span class="pre">tanh</span></code></a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="nn/losses.html">Loss Functions</a><ul>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.binary_cross_entropy.html">mlx.nn.losses.binary_cross_entropy</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.binary_cross_entropy.html#mlx.nn.losses.binary_cross_entropy"><code class="docutils literal notranslate"><span class="pre">binary_cross_entropy</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.cosine_similarity_loss.html">mlx.nn.losses.cosine_similarity_loss</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.cosine_similarity_loss.html#mlx.nn.losses.cosine_similarity_loss"><code class="docutils literal notranslate"><span class="pre">cosine_similarity_loss</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.cross_entropy.html">mlx.nn.losses.cross_entropy</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.cross_entropy.html#mlx.nn.losses.cross_entropy"><code class="docutils literal notranslate"><span class="pre">cross_entropy</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.gaussian_nll_loss.html">mlx.nn.losses.gaussian_nll_loss</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.gaussian_nll_loss.html#mlx.nn.losses.gaussian_nll_loss"><code class="docutils literal notranslate"><span class="pre">gaussian_nll_loss</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.hinge_loss.html">mlx.nn.losses.hinge_loss</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.hinge_loss.html#mlx.nn.losses.hinge_loss"><code class="docutils literal notranslate"><span class="pre">hinge_loss</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.huber_loss.html">mlx.nn.losses.huber_loss</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.huber_loss.html#mlx.nn.losses.huber_loss"><code class="docutils literal notranslate"><span class="pre">huber_loss</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.kl_div_loss.html">mlx.nn.losses.kl_div_loss</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.kl_div_loss.html#mlx.nn.losses.kl_div_loss"><code class="docutils literal notranslate"><span class="pre">kl_div_loss</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.l1_loss.html">mlx.nn.losses.l1_loss</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.l1_loss.html#mlx.nn.losses.l1_loss"><code class="docutils literal notranslate"><span class="pre">l1_loss</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.log_cosh_loss.html">mlx.nn.losses.log_cosh_loss</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.log_cosh_loss.html#mlx.nn.losses.log_cosh_loss"><code class="docutils literal notranslate"><span class="pre">log_cosh_loss</span></code></a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.margin_ranking_loss.html">mlx.nn.losses.margin_ranking_loss</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.margin_ranking_loss.html#mlx.nn.losses.margin_ranking_loss"><code class="docutils literal notranslate"><span class="pre">margin_ranking_loss</span></code></a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.mse_loss.html">mlx.nn.losses.mse_loss</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.mse_loss.html#mlx.nn.losses.mse_loss"><code class="docutils literal notranslate"><span class="pre">mse_loss</span></code></a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.nll_loss.html">mlx.nn.losses.nll_loss</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.nll_loss.html#mlx.nn.losses.nll_loss"><code class="docutils literal notranslate"><span class="pre">nll_loss</span></code></a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.smooth_l1_loss.html">mlx.nn.losses.smooth_l1_loss</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.smooth_l1_loss.html#mlx.nn.losses.smooth_l1_loss"><code class="docutils literal notranslate"><span class="pre">smooth_l1_loss</span></code></a></li>
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</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.triplet_loss.html">mlx.nn.losses.triplet_loss</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.losses.triplet_loss.html#mlx.nn.losses.triplet_loss"><code class="docutils literal notranslate"><span class="pre">triplet_loss</span></code></a></li>
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<li class="toctree-l1"><a class="reference internal" href="nn/init.html">Initializers</a><ul>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.init.constant.html">mlx.nn.init.constant</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.init.constant.html#mlx.nn.init.constant"><code class="docutils literal notranslate"><span class="pre">constant()</span></code></a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.init.normal.html">mlx.nn.init.normal</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.init.normal.html#mlx.nn.init.normal"><code class="docutils literal notranslate"><span class="pre">normal()</span></code></a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.init.uniform.html">mlx.nn.init.uniform</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.init.uniform.html#mlx.nn.init.uniform"><code class="docutils literal notranslate"><span class="pre">uniform()</span></code></a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.init.identity.html">mlx.nn.init.identity</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.init.identity.html#mlx.nn.init.identity"><code class="docutils literal notranslate"><span class="pre">identity()</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.init.glorot_normal.html">mlx.nn.init.glorot_normal</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.init.glorot_normal.html#mlx.nn.init.glorot_normal"><code class="docutils literal notranslate"><span class="pre">glorot_normal()</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.init.glorot_uniform.html">mlx.nn.init.glorot_uniform</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.init.glorot_uniform.html#mlx.nn.init.glorot_uniform"><code class="docutils literal notranslate"><span class="pre">glorot_uniform()</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.init.he_normal.html">mlx.nn.init.he_normal</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.init.he_normal.html#mlx.nn.init.he_normal"><code class="docutils literal notranslate"><span class="pre">he_normal()</span></code></a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.init.he_uniform.html">mlx.nn.init.he_uniform</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn/_autosummary/mlx.nn.init.he_uniform.html#mlx.nn.init.he_uniform"><code class="docutils literal notranslate"><span class="pre">he_uniform()</span></code></a></li>
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