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<li class="toctree-l2 has-children"><a class="reference internal" href="../python/nn/functions.html">Functions</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.elu.html">mlx.nn.elu</a></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="../python/nn/losses.html">Loss Functions</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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<li class="toctree-l2 has-children"><a class="reference internal" href="../python/nn/init.html">Initializers</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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<section id="quick-start-guide">
<h1>Quick Start Guide<a class="headerlink" href="#quick-start-guide" title="Link to this heading">#</a></h1>
<section id="basics">
<h2>Basics<a class="headerlink" href="#basics" title="Link to this heading">#</a></h2>
<p>Import <code class="docutils literal notranslate"><span class="pre">mlx.core</span></code> and make an <a class="reference internal" href="../python/_autosummary/mlx.core.array.html#mlx.core.array" title="mlx.core.array"><code class="xref py py-class docutils literal notranslate"><span class="pre">array</span></code></a>:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="o">&gt;&gt;</span> <span class="kn">import</span><span class="w"> </span><span class="nn">mlx.core</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">mx</span>
<span class="o">&gt;&gt;</span> <span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
<span class="o">&gt;&gt;</span> <span class="n">a</span><span class="o">.</span><span class="n">shape</span>
<span class="p">[</span><span class="mi">4</span><span class="p">]</span>
<span class="o">&gt;&gt;</span> <span class="n">a</span><span class="o">.</span><span class="n">dtype</span>
<span class="n">int32</span>
<span class="o">&gt;&gt;</span> <span class="n">b</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">])</span>
<span class="o">&gt;&gt;</span> <span class="n">b</span><span class="o">.</span><span class="n">dtype</span>
<span class="n">float32</span>
</pre></div>
</div>
<p>Operations in MLX are lazy. The outputs of MLX operations are not computed
until they are needed. To force an array to be evaluated use
<a class="reference internal" href="../python/_autosummary/mlx.core.eval.html#mlx.core.eval" title="mlx.core.eval"><code class="xref py py-func docutils literal notranslate"><span class="pre">eval()</span></code></a>. Arrays will automatically be evaluated in a few cases. For
example, inspecting a scalar with <a class="reference internal" href="../python/_autosummary/mlx.core.array.item.html#mlx.core.array.item" title="mlx.core.array.item"><code class="xref py py-meth docutils literal notranslate"><span class="pre">array.item()</span></code></a>, printing an array,
or converting an array from <a class="reference internal" href="../python/_autosummary/mlx.core.array.html#mlx.core.array" title="mlx.core.array"><code class="xref py py-class docutils literal notranslate"><span class="pre">array</span></code></a> to <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a> all
automatically evaluate the array.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="o">&gt;&gt;</span> <span class="n">c</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span> <span class="c1"># c not yet evaluated</span>
<span class="o">&gt;&gt;</span> <span class="n">mx</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> <span class="c1"># evaluates c</span>
<span class="o">&gt;&gt;</span> <span class="n">c</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="o">&gt;&gt;</span> <span class="nb">print</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> <span class="c1"># Also evaluates c</span>
<span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">float32</span><span class="p">)</span>
<span class="o">&gt;&gt;</span> <span class="n">c</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="o">&gt;&gt;</span> <span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="o">&gt;&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> <span class="c1"># Also evaluates c</span>
<span class="n">array</span><span class="p">([</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">float32</span><span class="p">)</span>
</pre></div>
</div>
<p>See the page on <a class="reference internal" href="lazy_evaluation.html#lazy-eval"><span class="std std-ref">Lazy Evaluation</span></a> for more details.</p>
</section>
<section id="function-and-graph-transformations">
<h2>Function and Graph Transformations<a class="headerlink" href="#function-and-graph-transformations" title="Link to this heading">#</a></h2>
<p>MLX has standard function transformations like <a class="reference internal" href="../python/_autosummary/mlx.core.grad.html#mlx.core.grad" title="mlx.core.grad"><code class="xref py py-func docutils literal notranslate"><span class="pre">grad()</span></code></a> and <a class="reference internal" href="../python/_autosummary/mlx.core.vmap.html#mlx.core.vmap" title="mlx.core.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">vmap()</span></code></a>.
Transformations can be composed arbitrarily. For example
<code class="docutils literal notranslate"><span class="pre">grad(vmap(grad(fn)))</span></code> (or any other composition) is allowed.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="o">&gt;&gt;</span> <span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)</span>
<span class="o">&gt;&gt;</span> <span class="n">mx</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">array</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">float32</span><span class="p">)</span>
<span class="o">&gt;&gt;</span> <span class="n">mx</span><span class="o">.</span><span class="n">grad</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">sin</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
<span class="n">array</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">float32</span><span class="p">)</span>
<span class="o">&gt;&gt;</span> <span class="n">mx</span><span class="o">.</span><span class="n">grad</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">grad</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">sin</span><span class="p">))(</span><span class="n">x</span><span class="p">)</span>
<span class="n">array</span><span class="p">(</span><span class="o">-</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">float32</span><span class="p">)</span>
</pre></div>
</div>
<p>Other gradient transformations include <a class="reference internal" href="../python/_autosummary/mlx.core.vjp.html#mlx.core.vjp" title="mlx.core.vjp"><code class="xref py py-func docutils literal notranslate"><span class="pre">vjp()</span></code></a> for vector-Jacobian products
and <a class="reference internal" href="../python/_autosummary/mlx.core.jvp.html#mlx.core.jvp" title="mlx.core.jvp"><code class="xref py py-func docutils literal notranslate"><span class="pre">jvp()</span></code></a> for Jacobian-vector products.</p>
<p>Use <a class="reference internal" href="../python/_autosummary/mlx.core.value_and_grad.html#mlx.core.value_and_grad" title="mlx.core.value_and_grad"><code class="xref py py-func docutils literal notranslate"><span class="pre">value_and_grad()</span></code></a> to efficiently compute both a function’s output and
gradient with respect to the function’s input.</p>
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