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<h1>Multi-Layer Perceptron</h1>
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<section id="multi-layer-perceptron">
<span id="mlp"></span><h1>Multi-Layer Perceptron<a class="headerlink" href="#multi-layer-perceptron" title="Link to this heading">#</a></h1>
<p>In this example we’ll learn to use <code class="docutils literal notranslate"><span class="pre">mlx.nn</span></code> by implementing a simple
multi-layer perceptron to classify MNIST.</p>
<p>As a first step import the MLX packages we need:</p>
<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="kn">import</span> <span class="nn">mlx.optimizers</span> <span class="k">as</span> <span class="nn">optim</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
</pre></div>
</div>
<p>The model is defined as the <code class="docutils literal notranslate"><span class="pre">MLP</span></code> class which inherits from
<a class="reference internal" href="../python/nn/module.html#mlx.nn.Module" title="mlx.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">mlx.nn.Module</span></code></a>. We follow the standard idiom to make a new module:</p>
<ol class="arabic simple">
<li><p>Define an <code class="docutils literal notranslate"><span class="pre">__init__</span></code> where the parameters and/or submodules are setup. See
the <a class="reference internal" href="../python/nn.html#module-class"><span class="std std-ref">Module class docs</span></a> for more information on how
<a class="reference internal" href="../python/nn/module.html#mlx.nn.Module" title="mlx.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">mlx.nn.Module</span></code></a> registers parameters.</p></li>
<li><p>Define a <code class="docutils literal notranslate"><span class="pre">__call__</span></code> where the computation is implemented.</p></li>
</ol>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></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">num_layers</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">input_dim</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">hidden_dim</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">output_dim</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="n">layer_sizes</span> <span class="o">=</span> <span class="p">[</span><span class="n">input_dim</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="n">hidden_dim</span><span class="p">]</span> <span class="o">*</span> <span class="n">num_layers</span> <span class="o">+</span> <span class="p">[</span><span class="n">output_dim</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">idim</span><span class="p">,</span> <span class="n">odim</span><span class="p">)</span>
<span class="k">for</span> <span class="n">idim</span><span class="p">,</span> <span class="n">odim</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">layer_sizes</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">layer_sizes</span><span class="p">[</span><span class="mi">1</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">l</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</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">l</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="mf">0.0</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">](</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
<p>We define the loss function which takes the mean of the per-example cross
entropy loss. The <code class="docutils literal notranslate"><span class="pre">mlx.nn.losses</span></code> sub-package has implementations of some
commonly used loss functions.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">loss_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="k">return</span> <span class="n">mx</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">losses</span><span class="o">.</span><span class="n">cross_entropy</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>
</pre></div>
</div>
<p>We also need a function to compute the accuracy of the model on the validation
set:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">eval_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="k">return</span> <span class="n">mx</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">argmax</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">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="n">y</span><span class="p">)</span>
</pre></div>
</div>
<p>Next, setup the problem parameters and load the data. To load the data, you need our
<a class="reference external" href="https://github.com/ml-explore/mlx-examples/blob/main/mnist/mnist.py">mnist data loader</a>, which
we will import as <cite>mnist</cite>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">num_layers</span> <span class="o">=</span> <span class="mi">2</span>
<span class="n">hidden_dim</span> <span class="o">=</span> <span class="mi">32</span>
<span class="n">num_classes</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">256</span>
<span class="n">num_epochs</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">1e-1</span>
<span class="c1"># Load the data</span>
<span class="kn">import</span> <span class="nn">mnist</span>
<span class="n">train_images</span><span class="p">,</span> <span class="n">train_labels</span><span class="p">,</span> <span class="n">test_images</span><span class="p">,</span> <span class="n">test_labels</span> <span class="o">=</span> <span class="nb">map</span><span class="p">(</span>
<span class="n">mx</span><span class="o">.</span><span class="n">array</span><span class="p">,</span> <span class="n">mnist</span><span class="o">.</span><span class="n">mnist</span><span class="p">()</span>
<span class="p">)</span>
</pre></div>
</div>
<p>Since we’re using SGD, we need an iterator which shuffles and constructs
minibatches of examples in the training set:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">batch_iterate</span><span class="p">(</span><span class="n">batch_size</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">perm</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="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">permutation</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">size</span><span class="p">))</span>
<span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">y</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
<span class="n">ids</span> <span class="o">=</span> <span class="n">perm</span><span class="p">[</span><span class="n">s</span> <span class="p">:</span> <span class="n">s</span> <span class="o">+</span> <span class="n">batch_size</span><span class="p">]</span>
<span class="k">yield</span> <span class="n">X</span><span class="p">[</span><span class="n">ids</span><span class="p">],</span> <span class="n">y</span><span class="p">[</span><span class="n">ids</span><span class="p">]</span>
</pre></div>
</div>
<p>Finally, we put it all together by instantiating the model, the
<a class="reference internal" href="../python/optimizers/_autosummary/mlx.optimizers.SGD.html#mlx.optimizers.SGD" title="mlx.optimizers.SGD"><code class="xref py py-class docutils literal notranslate"><span class="pre">mlx.optimizers.SGD</span></code></a> optimizer, and running the training loop:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Load the model</span>
<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>
<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"># Get a function which gives the loss and gradient of the</span>
<span class="c1"># loss with respect to the model&#39;s trainable parameters</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="c1"># Instantiate the optimizer</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>
<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 optimizer state and model parameters</span>
<span class="c1"># in a single call</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"># Force a graph evaluation</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>
<span class="n">accuracy</span> <span class="o">=</span> <span class="n">eval_fn</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">test_images</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Epoch </span><span class="si">{</span><span class="n">e</span><span class="si">}</span><span class="s2">: Test accuracy </span><span class="si">{</span><span class="n">accuracy</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The <a class="reference internal" href="../python/_autosummary/mlx.nn.value_and_grad.html#mlx.nn.value_and_grad" title="mlx.nn.value_and_grad"><code class="xref py py-func docutils literal notranslate"><span class="pre">mlx.nn.value_and_grad()</span></code></a> function is a convenience function to get
the gradient of a loss with respect to the trainable parameters of a model.
This should not be confused with <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">mlx.core.value_and_grad()</span></code></a>.</p>
</div>
<p>The model should train to a decent accuracy (about 95%) after just a few passes
over the training set. The <a class="reference external" href="https://github.com/ml-explore/mlx-examples/tree/main/mnist">full example</a>
is available in the MLX GitHub repo.</p>
</section>
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