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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.simplify.html">mlx.core.simplify</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../python/fft.html">FFT</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-6"><i class="fa-solid fa-chevron-down"></i></label><ul>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../python/nn.html">Neural Networks</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.nn.value_and_grad.html">mlx.nn.value_and_grad</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.nn.Embedding.html">mlx.nn.Embedding</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.nn.ReLU.html">mlx.nn.ReLU</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.nn.PReLU.html">mlx.nn.PReLU</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.nn.GELU.html">mlx.nn.GELU</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.nn.SiLU.html">mlx.nn.SiLU</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.nn.Step.html">mlx.nn.Step</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.nn.SELU.html">mlx.nn.SELU</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.nn.Mish.html">mlx.nn.Mish</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.nn.Linear.html">mlx.nn.Linear</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.nn.Conv1d.html">mlx.nn.Conv1d</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.nn.Conv2d.html">mlx.nn.Conv2d</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.nn.LayerNorm.html">mlx.nn.LayerNorm</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.nn.Sequential.html">mlx.nn.Sequential</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary_functions/mlx.nn.relu.html">mlx.nn.relu</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary_functions/mlx.nn.losses.binary_cross_entropy.html">mlx.nn.losses.binary_cross_entropy</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary_functions/mlx.nn.losses.mse_loss.html">mlx.nn.losses.mse_loss</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Conv1d.html">mlx.nn.Conv1d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Conv2d.html">mlx.nn.Conv2d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.LayerNorm.html">mlx.nn.LayerNorm</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.RMSNorm.html">mlx.nn.RMSNorm</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.GroupNorm.html">mlx.nn.GroupNorm</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.RoPE.html">mlx.nn.RoPE</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.MultiHeadAttention.html">mlx.nn.MultiHeadAttention</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Sequential.html">mlx.nn.Sequential</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../python/optimizers.html">Optimizers</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
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<li class="toctree-l2 has-children"><a class="reference internal" href="../python/nn/functions.html">Functions</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.gelu_fast_approx.html">mlx.nn.gelu_fast_approx</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.relu.html">mlx.nn.relu</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.prelu.html">mlx.nn.prelu</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.silu.html">mlx.nn.silu</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.step.html">mlx.nn.step</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.selu.html">mlx.nn.selu</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.mish.html">mlx.nn.mish</a></li>
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</ul>
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<li class="toctree-l2 has-children"><a class="reference internal" href="../python/nn/losses.html">Loss Functions</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-10"><i class="fa-solid fa-chevron-down"></i></label><ul>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.cross_entropy.html">mlx.nn.losses.cross_entropy</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.binary_cross_entropy.html">mlx.nn.losses.binary_cross_entropy</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.l1_loss.html">mlx.nn.losses.l1_loss</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.mse_loss.html">mlx.nn.losses.mse_loss</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.nll_loss.html">mlx.nn.losses.nll_loss</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.kl_div_loss.html">mlx.nn.losses.kl_div_loss</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../python/optimizers.html">Optimizers</a><input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-11"><i class="fa-solid fa-chevron-down"></i></label><ul>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.OptimizerState.html">mlx.optimizers.OptimizerState</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.Optimizer.html">mlx.optimizers.Optimizer</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.SGD.html">mlx.optimizers.SGD</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.RMSprop.html">mlx.optimizers.RMSprop</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.Adagrad.html">mlx.optimizers.Adagrad</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.Adam.html">mlx.optimizers.Adam</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.AdamW.html">mlx.optimizers.AdamW</a></li>
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||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.Adamax.html">mlx.optimizers.Adamax</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l1 has-children"><a class="reference internal" href="../python/tree_utils.html">Tree Utils</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
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||||
<li class="toctree-l1 has-children"><a class="reference internal" href="../python/tree_utils.html">Tree Utils</a><input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-12"><i class="fa-solid fa-chevron-down"></i></label><ul>
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||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.utils.tree_flatten.html">mlx.utils.tree_flatten</a></li>
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||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.utils.tree_unflatten.html">mlx.utils.tree_unflatten</a></li>
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||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.utils.tree_map.html">mlx.utils.tree_map</a></li>
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||||
@@ -568,11 +593,11 @@ multi-layer perceptron to classify MNIST.</p>
|
||||
</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>
|
||||
<a class="reference internal" href="../python/_autosummary/mlx.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>
|
||||
<a class="reference internal" href="../python/_autosummary/mlx.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>
|
||||
@@ -605,7 +630,9 @@ set:</p>
|
||||
<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:</p>
|
||||
<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>
|
||||
|
||||
Reference in New Issue
Block a user