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<h1>Neural Networks</h1>
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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>
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<section id="neural-networks">
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<span id="nn"></span><h1>Neural Networks<a class="headerlink" href="#neural-networks" title="Permalink 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="Permalink 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">></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">"layers"</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="s2">"weight"</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">"layers"</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'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="Permalink 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="Permalink 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="Permalink 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="Permalink 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="o">(</span>layers.0<span class="o">)</span>: Linear<span class="o">(</span><span class="nv">input_dims</span><span class="o">=</span><span class="m">2</span>, <span class="nv">output_dims</span><span class="o">=</span><span class="m">128</span>, <span class="nv">bias</span><span class="o">=</span>True<span class="o">)</span>
|
||
<span class="o">(</span>layers.1<span class="o">)</span>: Linear<span class="o">(</span><span class="nv">input_dims</span><span class="o">=</span><span class="m">128</span>, <span class="nv">output_dims</span><span class="o">=</span><span class="m">128</span>, <span class="nv">bias</span><span class="o">=</span>True<span class="o">)</span>
|
||
<span class="o">(</span>layers.2<span class="o">)</span>: Linear<span class="o">(</span><span class="nv">input_dims</span><span class="o">=</span><span class="m">128</span>, <span class="nv">output_dims</span><span class="o">=</span><span class="m">10</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="Permalink 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"># <---- 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>
|
||
</tbody>
|
||
</table>
|
||
<div class="toctree-wrapper compound">
|
||
<ul>
|
||
<li class="toctree-l1"><a class="reference internal" href="nn/module.html">Module</a><ul>
|
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.training.html">mlx.nn.Module.training</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.state.html">mlx.nn.Module.state</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.apply.html">mlx.nn.Module.apply</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.apply_to_modules.html">mlx.nn.Module.apply_to_modules</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.children.html">mlx.nn.Module.children</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.freeze.html">mlx.nn.Module.freeze</a></li>
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||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.leaf_modules.html">mlx.nn.Module.leaf_modules</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.load_weights.html">mlx.nn.Module.load_weights</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.modules.html">mlx.nn.Module.modules</a></li>
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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></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.save_weights.html">mlx.nn.Module.save_weights</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.train.html">mlx.nn.Module.train</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.trainable_parameters.html">mlx.nn.Module.trainable_parameters</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.unfreeze.html">mlx.nn.Module.unfreeze</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Module.update.html">mlx.nn.Module.update</a></li>
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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></li>
|
||
</ul>
|
||
</li>
|
||
<li class="toctree-l1"><a class="reference internal" href="nn/layers.html">Layers</a><ul>
|
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.ALiBi.html">mlx.nn.ALiBi</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.AvgPool1d.html">mlx.nn.AvgPool1d</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.AvgPool2d.html">mlx.nn.AvgPool2d</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.BatchNorm.html">mlx.nn.BatchNorm</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Conv1d.html">mlx.nn.Conv1d</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Conv2d.html">mlx.nn.Conv2d</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Dropout.html">mlx.nn.Dropout</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Dropout2d.html">mlx.nn.Dropout2d</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Dropout3d.html">mlx.nn.Dropout3d</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Embedding.html">mlx.nn.Embedding</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.GELU.html">mlx.nn.GELU</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.GroupNorm.html">mlx.nn.GroupNorm</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.GRU.html">mlx.nn.GRU</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.InstanceNorm.html">mlx.nn.InstanceNorm</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.LayerNorm.html">mlx.nn.LayerNorm</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Linear.html">mlx.nn.Linear</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.LSTM.html">mlx.nn.LSTM</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.MaxPool1d.html">mlx.nn.MaxPool1d</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.MaxPool2d.html">mlx.nn.MaxPool2d</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Mish.html">mlx.nn.Mish</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.MultiHeadAttention.html">mlx.nn.MultiHeadAttention</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.PReLU.html">mlx.nn.PReLU</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.QuantizedLinear.html">mlx.nn.QuantizedLinear</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.RMSNorm.html">mlx.nn.RMSNorm</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.ReLU.html">mlx.nn.ReLU</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.RNN.html">mlx.nn.RNN</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.RoPE.html">mlx.nn.RoPE</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.SELU.html">mlx.nn.SELU</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Sequential.html">mlx.nn.Sequential</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.SiLU.html">mlx.nn.SiLU</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.SinusoidalPositionalEncoding.html">mlx.nn.SinusoidalPositionalEncoding</a></li>
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||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Softshrink.html">mlx.nn.Softshrink</a></li>
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||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Step.html">mlx.nn.Step</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Transformer.html">mlx.nn.Transformer</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary/mlx.nn.Upsample.html">mlx.nn.Upsample</a></li>
|
||
</ul>
|
||
</li>
|
||
<li class="toctree-l1"><a class="reference internal" href="nn/functions.html">Functions</a><ul>
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||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.elu.html">mlx.nn.elu</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.gelu.html">mlx.nn.gelu</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.gelu_approx.html">mlx.nn.gelu_approx</a></li>
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||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.gelu_fast_approx.html">mlx.nn.gelu_fast_approx</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.glu.html">mlx.nn.glu</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.hardswish.html">mlx.nn.hardswish</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.leaky_relu.html">mlx.nn.leaky_relu</a></li>
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||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.log_sigmoid.html">mlx.nn.log_sigmoid</a></li>
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||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.log_softmax.html">mlx.nn.log_softmax</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.mish.html">mlx.nn.mish</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.prelu.html">mlx.nn.prelu</a></li>
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||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.relu.html">mlx.nn.relu</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.relu6.html">mlx.nn.relu6</a></li>
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||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.selu.html">mlx.nn.selu</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.sigmoid.html">mlx.nn.sigmoid</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.silu.html">mlx.nn.silu</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.softmax.html">mlx.nn.softmax</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.softplus.html">mlx.nn.softplus</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.softshrink.html">mlx.nn.softshrink</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.step.html">mlx.nn.step</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="nn/_autosummary_functions/mlx.nn.tanh.html">mlx.nn.tanh</a></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></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></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></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></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></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></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></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></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></li>
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