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<li class="toctree-l1 current active has-children"><a class="reference internal" href="../nn.html">Neural Networks</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
<li class="toctree-l2 current active"><a class="current reference internal" href="#">mlx.nn.value_and_grad</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../nn/_autosummary/mlx.nn.ALiBi.html">mlx.nn.ALiBi</a></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="../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="../nn/_autosummary_functions/mlx.nn.elu.html">mlx.nn.elu</a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn/_autosummary_functions/mlx.nn.celu.html">mlx.nn.celu</a></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="../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>
<li class="toctree-l3"><a class="reference internal" href="../nn/_autosummary_functions/mlx.nn.losses.binary_cross_entropy.html">mlx.nn.losses.binary_cross_entropy</a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn/_autosummary_functions/mlx.nn.losses.cosine_similarity_loss.html">mlx.nn.losses.cosine_similarity_loss</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../nn/_autosummary_functions/mlx.nn.losses.gaussian_nll_loss.html">mlx.nn.losses.gaussian_nll_loss</a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn/_autosummary_functions/mlx.nn.losses.hinge_loss.html">mlx.nn.losses.hinge_loss</a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn/_autosummary_functions/mlx.nn.losses.huber_loss.html">mlx.nn.losses.huber_loss</a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn/_autosummary_functions/mlx.nn.losses.kl_div_loss.html">mlx.nn.losses.kl_div_loss</a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn/_autosummary_functions/mlx.nn.losses.l1_loss.html">mlx.nn.losses.l1_loss</a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn/_autosummary_functions/mlx.nn.losses.log_cosh_loss.html">mlx.nn.losses.log_cosh_loss</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../nn/_autosummary_functions/mlx.nn.losses.smooth_l1_loss.html">mlx.nn.losses.smooth_l1_loss</a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn/_autosummary_functions/mlx.nn.losses.triplet_loss.html">mlx.nn.losses.triplet_loss</a></li>
</ul>
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<li class="toctree-l2 has-children"><a class="reference internal" href="../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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<li class="toctree-l3"><a class="reference internal" href="../nn/_autosummary/mlx.nn.init.identity.html">mlx.nn.init.identity</a></li>
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</details></li>
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<h1>mlx.nn.value_and_grad</h1>
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<h1>mlx.nn.value_and_grad<a class="headerlink" href="#mlx-nn-value-and-grad" title="Link to this heading">#</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="mlx.nn.value_and_grad">
<span class="sig-name descname"><span class="pre">value_and_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="../nn/module.html#mlx.nn.Module" title="mlx.nn.layers.base.Module"><span class="pre">Module</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#mlx.nn.value_and_grad" title="Link to this definition">#</a></dt>
<dd><p>Transform the passed function <code class="docutils literal notranslate"><span class="pre">fn</span></code> to a function that computes the
gradients of <code class="docutils literal notranslate"><span class="pre">fn</span></code> wrt the model’s trainable parameters and also its
value.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model</strong> (<a class="reference internal" href="../nn/module.html#mlx.nn.Module" title="mlx.nn.Module"><em>Module</em></a>) – The model whose trainable parameters to compute
gradients for</p></li>
<li><p><strong>fn</strong> (<em>Callable</em>) – The scalar function to compute gradients for</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>A callable that returns the value of <code class="docutils literal notranslate"><span class="pre">fn</span></code> and the gradients wrt the
trainable parameters of <code class="docutils literal notranslate"><span class="pre">model</span></code></p>
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