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<h1>Optimizers</h1>
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<section id="optimizers">
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<span id="id1"></span><h1>Optimizers<a class="headerlink" href="#optimizers" title="Permalink to this heading">#</a></h1>
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<p>The optimizers in MLX can be used both with <code class="xref py py-mod docutils literal notranslate"><span class="pre">mlx.nn</span></code> but also with pure
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<code class="xref py py-mod docutils literal notranslate"><span class="pre">mlx.core</span></code> functions. A typical example involves calling
|
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<code class="xref py py-meth docutils literal notranslate"><span class="pre">Optimizer.update()</span></code> to update a model’s parameters based on the loss
|
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gradients and subsequently calling <a class="reference internal" href="_autosummary/mlx.core.eval.html#mlx.core.eval" title="mlx.core.eval"><code class="xref py py-func docutils literal notranslate"><span class="pre">mlx.core.eval()</span></code></a> to evaluate both the
|
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model’s parameters and the <strong>optimizer state</strong>.</p>
|
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create a model</span>
|
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<span class="n">model</span> <span class="o">=</span> <span class="n">MLP</span><span class="p">(</span><span class="n">num_layers</span><span class="p">,</span> <span class="n">train_images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">hidden_dim</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">)</span>
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<span class="n">mx</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span>
|
||
|
||
<span class="c1"># Create the gradient function and the optimizer</span>
|
||
<span class="n">loss_and_grad_fn</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">model</span><span class="p">,</span> <span class="n">loss_fn</span><span class="p">)</span>
|
||
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">learning_rate</span><span class="o">=</span><span class="n">learning_rate</span><span class="p">)</span>
|
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<span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_epochs</span><span class="p">):</span>
|
||
<span class="k">for</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">batch_iterate</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">train_images</span><span class="p">,</span> <span class="n">train_labels</span><span class="p">):</span>
|
||
<span class="n">loss</span><span class="p">,</span> <span class="n">grads</span> <span class="o">=</span> <span class="n">loss_and_grad_fn</span><span class="p">(</span><span class="n">model</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="c1"># Update the model with the gradients. So far no computation has happened.</span>
|
||
<span class="n">optimizer</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">grads</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Compute the new parameters but also the optimizer state.</span>
|
||
<span class="n">mx</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">state</span><span class="p">)</span>
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