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<li class="toctree-l2 current active has-children"><a class="current reference internal" href="#">Loss Functions</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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<h1>Loss Functions</h1>
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<section id="loss-functions">
<span id="losses"></span><h1>Loss Functions<a class="headerlink" href="#loss-functions" title="Link to this heading">#</a></h1>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary_functions/mlx.nn.losses.binary_cross_entropy.html#mlx.nn.losses.binary_cross_entropy" title="mlx.nn.losses.binary_cross_entropy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">binary_cross_entropy</span></code></a>(inputs, targets[, ...])</p></td>
<td><p>Computes the binary cross entropy loss.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary_functions/mlx.nn.losses.cosine_similarity_loss.html#mlx.nn.losses.cosine_similarity_loss" title="mlx.nn.losses.cosine_similarity_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cosine_similarity_loss</span></code></a>(x1, x2[, axis, eps, ...])</p></td>
<td><p>Computes the cosine similarity between the two inputs.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary_functions/mlx.nn.losses.cross_entropy.html#mlx.nn.losses.cross_entropy" title="mlx.nn.losses.cross_entropy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cross_entropy</span></code></a>(logits, targets[, weights, ...])</p></td>
<td><p>Computes the cross entropy loss.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary_functions/mlx.nn.losses.gaussian_nll_loss.html#mlx.nn.losses.gaussian_nll_loss" title="mlx.nn.losses.gaussian_nll_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gaussian_nll_loss</span></code></a>(inputs, targets, vars[, ...])</p></td>
<td><p>Computes the negative log likelihood loss for a Gaussian distribution.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary_functions/mlx.nn.losses.hinge_loss.html#mlx.nn.losses.hinge_loss" title="mlx.nn.losses.hinge_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hinge_loss</span></code></a>(inputs, targets[, reduction])</p></td>
<td><p>Computes the hinge loss between inputs and targets.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary_functions/mlx.nn.losses.huber_loss.html#mlx.nn.losses.huber_loss" title="mlx.nn.losses.huber_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">huber_loss</span></code></a>(inputs, targets[, delta, reduction])</p></td>
<td><p>Computes the Huber loss between inputs and targets.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary_functions/mlx.nn.losses.kl_div_loss.html#mlx.nn.losses.kl_div_loss" title="mlx.nn.losses.kl_div_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">kl_div_loss</span></code></a>(inputs, targets[, axis, reduction])</p></td>
<td><p>Computes the Kullback-Leibler divergence loss.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary_functions/mlx.nn.losses.l1_loss.html#mlx.nn.losses.l1_loss" title="mlx.nn.losses.l1_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">l1_loss</span></code></a>(predictions, targets[, reduction])</p></td>
<td><p>Computes the L1 loss.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary_functions/mlx.nn.losses.log_cosh_loss.html#mlx.nn.losses.log_cosh_loss" title="mlx.nn.losses.log_cosh_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">log_cosh_loss</span></code></a>(inputs, targets[, reduction])</p></td>
<td><p>Computes the log cosh loss between inputs and targets.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary_functions/mlx.nn.losses.margin_ranking_loss.html#mlx.nn.losses.margin_ranking_loss" title="mlx.nn.losses.margin_ranking_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">margin_ranking_loss</span></code></a>(inputs1, inputs2, targets)</p></td>
<td><p>Calculate the margin ranking loss that loss given inputs <span class="math notranslate nohighlight">\(x_1\)</span>, <span class="math notranslate nohighlight">\(x_2\)</span> and a label <span class="math notranslate nohighlight">\(y\)</span> (containing 1 or -1).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary_functions/mlx.nn.losses.mse_loss.html#mlx.nn.losses.mse_loss" title="mlx.nn.losses.mse_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mse_loss</span></code></a>(predictions, targets[, reduction])</p></td>
<td><p>Computes the mean squared error loss.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary_functions/mlx.nn.losses.nll_loss.html#mlx.nn.losses.nll_loss" title="mlx.nn.losses.nll_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nll_loss</span></code></a>(inputs, targets[, axis, reduction])</p></td>
<td><p>Computes the negative log likelihood loss.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary_functions/mlx.nn.losses.smooth_l1_loss.html#mlx.nn.losses.smooth_l1_loss" title="mlx.nn.losses.smooth_l1_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">smooth_l1_loss</span></code></a>(predictions, targets[, beta, ...])</p></td>
<td><p>Computes the smooth L1 loss.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary_functions/mlx.nn.losses.triplet_loss.html#mlx.nn.losses.triplet_loss" title="mlx.nn.losses.triplet_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">triplet_loss</span></code></a>(anchors, positives, negatives)</p></td>
<td><p>Computes the triplet loss for a set of anchor, positive, and negative samples.</p></td>
</tr>
</tbody>
</table>
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