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<section id="mlx-nn-conv1d">
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<h1>mlx.nn.Conv1d<a class="headerlink" href="#mlx-nn-conv1d" title="Permalink to this heading">#</a></h1>
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<dl class="py class">
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<dt class="sig sig-object py" id="mlx.nn.Conv1d">
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<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">mlx.nn.</span></span><span class="sig-name descname"><span class="pre">Conv1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</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/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</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/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</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/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</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/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</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/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</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/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#mlx.nn.Conv1d" title="Permalink to this definition">#</a></dt>
|
||||
<dd><p>Applies a 1-dimensional convolution over the multi-channel input sequence.</p>
|
||||
<dl class="simple">
|
||||
<dt>The channels are expected to be last i.e. the input shape should be <code class="docutils literal notranslate"><span class="pre">NLC</span></code> where:</dt><dd><ul class="simple">
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">N</span></code> is the batch dimension</p></li>
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">L</span></code> is the sequence length</p></li>
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">C</span></code> is the number of input channels</p></li>
|
||||
</ul>
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||||
</dd>
|
||||
</dl>
|
||||
<dl class="field-list simple">
|
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>in_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – The number of input channels</p></li>
|
||||
<li><p><strong>out_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – The number of output channels</p></li>
|
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<li><p><strong>kernel_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – The size of the convolution filters</p></li>
|
||||
<li><p><strong>stride</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – The stride when applying the filter.
|
||||
Default: 1.</p></li>
|
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<li><p><strong>padding</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – How many positions to 0-pad the input with.
|
||||
Default: 0.</p></li>
|
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<li><p><strong>bias</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code> add a learnable bias to the output.
|
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Default: <code class="docutils literal notranslate"><span class="pre">True</span></code></p></li>
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<section id="mlx-nn-conv2d">
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<h1>mlx.nn.Conv2d<a class="headerlink" href="#mlx-nn-conv2d" title="Permalink to this heading">#</a></h1>
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<dl class="py class">
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<dt class="sig sig-object py" id="mlx.nn.Conv2d">
|
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<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">mlx.nn.</span></span><span class="sig-name descname"><span class="pre">Conv2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</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/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</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/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</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.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</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.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</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.Union" title="(in Python v3.12)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</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/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#mlx.nn.Conv2d" title="Permalink to this definition">#</a></dt>
|
||||
<dd><p>Applies a 2-dimensional convolution over the multi-channel input image.</p>
|
||||
<dl class="simple">
|
||||
<dt>The channels are expected to be last i.e. the input shape should be <code class="docutils literal notranslate"><span class="pre">NHWC</span></code> where:</dt><dd><ul class="simple">
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">N</span></code> is the batch dimension</p></li>
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">H</span></code> is the input image height</p></li>
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">W</span></code> is the input image width</p></li>
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">C</span></code> is the number of input channels</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
<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>in_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – The number of input channels.</p></li>
|
||||
<li><p><strong>out_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – The number of output channels.</p></li>
|
||||
<li><p><strong>kernel_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a>) – The size of the convolution filters.</p></li>
|
||||
<li><p><strong>stride</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em>, </em><em>optional</em>) – The size of the stride when
|
||||
applying the filter. Default: 1.</p></li>
|
||||
<li><p><strong>padding</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em>, </em><em>optional</em>) – How many positions to 0-pad
|
||||
the input with. Default: 0.</p></li>
|
||||
<li><p><strong>bias</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code> add a learnable bias to the
|
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<h1>mlx.nn.Embedding<a class="headerlink" href="#mlx-nn-embedding" title="Permalink to this heading">#</a></h1>
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<dl class="py class">
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<dt class="sig sig-object py" id="mlx.nn.Embedding">
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<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">mlx.nn.</span></span><span class="sig-name descname"><span class="pre">Embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_embeddings</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/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">dims</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/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#mlx.nn.Embedding" title="Permalink to this definition">#</a></dt>
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<dd><p>Implements a simple lookup table that maps each input integer to a
|
||||
high-dimensional vector.</p>
|
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<p>Typically used to embed discrete tokens for processing by neural networks.</p>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<li><p><strong>num_embeddings</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – How many possible discrete tokens can we embed.
|
||||
Usually called the vocabulary size.</p></li>
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<li><p><strong>dims</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – The dimensionality of the embeddings.</p></li>
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<h1>mlx.nn.GELU</h1>
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<section id="mlx-nn-gelu">
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<h1>mlx.nn.GELU<a class="headerlink" href="#mlx-nn-gelu" title="Permalink to this heading">#</a></h1>
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<dl class="py class">
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<dt class="sig sig-object py" id="mlx.nn.GELU">
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<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">mlx.nn.</span></span><span class="sig-name descname"><span class="pre">GELU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">approx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'none'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#mlx.nn.GELU" title="Permalink to this definition">#</a></dt>
|
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<dd><p>Applies the Gaussian Error Linear Units.</p>
|
||||
<div class="math notranslate nohighlight">
|
||||
\[\textrm{GELU}(x) = x * \Phi(x)\]</div>
|
||||
<p>where <span class="math notranslate nohighlight">\(\Phi(x)\)</span> is the Gaussian CDF.</p>
|
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<p>However, if <code class="docutils literal notranslate"><span class="pre">approx</span></code> is set to ‘precise’ or ‘fast’ it applies</p>
|
||||
<div class="math notranslate nohighlight">
|
||||
\[\begin{split}\textrm{GELUApprox}(x) &= x * \sigma\left(1.60033 * x \left(1 + 0.0433603 * x^2\right)\right) \\
|
||||
\textrm{GELUFast}(x) &= x * \sigma\left(1.773 * x\right)\end{split}\]</div>
|
||||
<p>respectively.</p>
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<p>See <a class="reference internal" href="../_autosummary_functions/mlx.nn.gelu.html#mlx.nn.gelu" title="mlx.nn.gelu"><code class="xref py py-func docutils literal notranslate"><span class="pre">gelu()</span></code></a>, <a class="reference internal" href="../_autosummary_functions/mlx.nn.gelu_approx.html#mlx.nn.gelu_approx" title="mlx.nn.gelu_approx"><code class="xref py py-func docutils literal notranslate"><span class="pre">gelu_approx()</span></code></a> and <a class="reference internal" href="../_autosummary_functions/mlx.nn.gelu_fast_approx.html#mlx.nn.gelu_fast_approx" title="mlx.nn.gelu_fast_approx"><code class="xref py py-func docutils literal notranslate"><span class="pre">gelu_fast_approx()</span></code></a> for the
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<section id="mlx-nn-groupnorm">
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<h1>mlx.nn.GroupNorm<a class="headerlink" href="#mlx-nn-groupnorm" title="Permalink to this heading">#</a></h1>
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<dl class="py class">
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||||
<dt class="sig sig-object py" id="mlx.nn.GroupNorm">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">mlx.nn.</span></span><span class="sig-name descname"><span class="pre">GroupNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_groups</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/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">dims</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/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</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/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</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/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pytorch_compatible</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/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#mlx.nn.GroupNorm" title="Permalink to this definition">#</a></dt>
|
||||
<dd><p>Applies Group Normalization [1] to the inputs.</p>
|
||||
<p>Computes the same normalization as layer norm, namely</p>
|
||||
<div class="math notranslate nohighlight">
|
||||
\[y = \frac{x - E[x]}{\sqrt{Var[x]} + \epsilon} \gamma + \beta,\]</div>
|
||||
<p>where <span class="math notranslate nohighlight">\(\gamma\)</span> and <span class="math notranslate nohighlight">\(\beta\)</span> are learned per feature dimension
|
||||
parameters initialized at 1 and 0 respectively. However, the mean and
|
||||
variance are computed over the spatial dimensions and each group of
|
||||
features. In particular, the input is split into num_groups across the
|
||||
feature dimension.</p>
|
||||
<p>The feature dimension is assumed to be the last dimension and the dimensions
|
||||
that precede it (except the first) are considered the spatial dimensions.</p>
|
||||
<p>[1]: <a class="reference external" href="https://arxiv.org/abs/1803.08494">https://arxiv.org/abs/1803.08494</a></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>num_groups</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of groups to separate the features into</p></li>
|
||||
<li><p><strong>dims</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – The feature dimensions of the input to normalize over</p></li>
|
||||
<li><p><strong>eps</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – A small additive constant for numerical stability</p></li>
|
||||
<li><p><strong>affine</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True learn an affine transform to apply after the
|
||||
normalization.</p></li>
|
||||
<li><p><strong>pytorch_compatible</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True perform the group normalization in
|
||||
the same order/grouping as PyTorch.</p></li>
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</dd>
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<h1>mlx.nn.LayerNorm<a class="headerlink" href="#mlx-nn-layernorm" title="Permalink to this heading">#</a></h1>
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<dl class="py class">
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<dt class="sig sig-object py" id="mlx.nn.LayerNorm">
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<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">mlx.nn.</span></span><span class="sig-name descname"><span class="pre">LayerNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dims</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/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</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/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</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/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#mlx.nn.LayerNorm" title="Permalink to this definition">#</a></dt>
|
||||
<dd><p>Applies layer normalization [1] on the inputs.</p>
|
||||
<p>Computes</p>
|
||||
<div class="math notranslate nohighlight">
|
||||
\[y = \frac{x - E[x]}{\sqrt{Var[x]} + \epsilon} \gamma + \beta,\]</div>
|
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<p>where <span class="math notranslate nohighlight">\(\gamma\)</span> and <span class="math notranslate nohighlight">\(\beta\)</span> are learned per feature dimension
|
||||
parameters initialized at 1 and 0 respectively.</p>
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<p>[1]: <a class="reference external" href="https://arxiv.org/abs/1607.06450">https://arxiv.org/abs/1607.06450</a></p>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<dd class="field-odd"><ul class="simple">
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<li><p><strong>dims</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – The feature dimension of the input to normalize over</p></li>
|
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<li><p><strong>eps</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – A small additive constant for numerical stability</p></li>
|
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<li><p><strong>affine</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True learn an affine transform to apply after the
|
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<h1>mlx.nn.Linear<a class="headerlink" href="#mlx-nn-linear" title="Permalink to this heading">#</a></h1>
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<dt class="sig sig-object py" id="mlx.nn.Linear">
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<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">mlx.nn.</span></span><span class="sig-name descname"><span class="pre">Linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_dims</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/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_dims</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/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</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/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#mlx.nn.Linear" title="Permalink to this definition">#</a></dt>
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|
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|
||||
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|
||||
\[y = W^\top x + b\]</div>
|
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<p>where <span class="math notranslate nohighlight">\(W\)</span> has shape <code class="docutils literal notranslate"><span class="pre">[output_dims,</span> <span class="pre">input_dims]</span></code>.</p>
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<li><p><strong>input_dims</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – The dimensionality of the input features</p></li>
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<section id="mlx-nn-mish">
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<h1>mlx.nn.Mish<a class="headerlink" href="#mlx-nn-mish" title="Permalink to this heading">#</a></h1>
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<dl class="py class">
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<dt class="sig sig-object py" id="mlx.nn.Mish">
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<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">mlx.nn.</span></span><span class="sig-name descname"><span class="pre">Mish</span></span><a class="headerlink" href="#mlx.nn.Mish" title="Permalink to this definition">#</a></dt>
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<dd><p>Applies the Mish function, element-wise.
|
||||
Mish: A Self Regularized Non-Monotonic Neural Activation Function.</p>
|
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<p>Reference: <a class="reference external" href="https://arxiv.org/abs/1908.08681">https://arxiv.org/abs/1908.08681</a></p>
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<div class="math notranslate nohighlight">
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\[\text{Mish}(x) = x * \text{Tanh}(\text{Softplus}(x))\]</div>
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<h1>mlx.nn.RMSNorm<a class="headerlink" href="#mlx-nn-rmsnorm" title="Permalink to this heading">#</a></h1>
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<dl class="py class">
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<dt class="sig sig-object py" id="mlx.nn.RMSNorm">
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<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">mlx.nn.</span></span><span class="sig-name descname"><span class="pre">RMSNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dims</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/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</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/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-05</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#mlx.nn.RMSNorm" title="Permalink to this definition">#</a></dt>
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<dd><p>Applies Root Mean Square normalization [1] to the inputs.</p>
|
||||
<p>Computes</p>
|
||||
<div class="math notranslate nohighlight">
|
||||
\[y = \frac{x}{\sqrt{E[x^2] + \epsilon}} \gamma\]</div>
|
||||
<p>where <span class="math notranslate nohighlight">\(\gamma\)</span> is a learned per feature dimension parameter initialized at
|
||||
1.</p>
|
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<p>[1]: <a class="reference external" href="https://arxiv.org/abs/1910.07467">https://arxiv.org/abs/1910.07467</a></p>
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<li><p><strong>dims</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – The feature dimension of the input to normalize over</p></li>
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<li><p><strong>eps</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – A small additive constant for numerical stability</p></li>
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<h1>mlx.nn.ReLU<a class="headerlink" href="#mlx-nn-relu" title="Permalink to this heading">#</a></h1>
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<dl class="py class">
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<dt class="sig sig-object py" id="mlx.nn.ReLU">
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<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">mlx.nn.</span></span><span class="sig-name descname"><span class="pre">ReLU</span></span><a class="headerlink" href="#mlx.nn.ReLU" title="Permalink to this definition">#</a></dt>
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<h1>mlx.nn.RoPE<a class="headerlink" href="#mlx-nn-rope" title="Permalink to this heading">#</a></h1>
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<dl class="py class">
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<dt class="sig sig-object py" id="mlx.nn.RoPE">
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<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">mlx.nn.</span></span><span class="sig-name descname"><span class="pre">RoPE</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dims</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/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">traditional</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/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base</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/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">10000</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#mlx.nn.RoPE" title="Permalink to this definition">#</a></dt>
|
||||
<dd><p>Implements the rotary positional encoding [1].</p>
|
||||
<p>The traditional implementation rotates consecutive pairs of elements in the
|
||||
feature dimension while the default implementation rotates pairs with
|
||||
stride half the feature dimensions for efficiency.</p>
|
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<p>[1]: <a class="reference external" href="https://arxiv.org/abs/2104.09864">https://arxiv.org/abs/2104.09864</a></p>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
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<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>dims</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – The feature dimensions to be rotated. If the input feature
|
||||
is larger than dims then the rest is left unchanged.</p></li>
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<li><p><strong>traditional</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – If set to True choose the traditional
|
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implementation which is slightly less efficient. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
|
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<li><p><strong>base</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – The base used to compute angular frequency for
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<dt class="sig sig-object py" id="mlx.nn.SELU">
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<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">mlx.nn.</span></span><span class="sig-name descname"><span class="pre">SELU</span></span><a class="headerlink" href="#mlx.nn.SELU" title="Permalink to this definition">#</a></dt>
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<dd><p>Applies the Scaled Exponential Linear Unit.</p>
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\[\begin{split}\text{selu}(x) = \begin{cases}
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\lambda \alpha (\exp(x) - 1) & \text{if } x \leq 0
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\end{cases}\end{split}\]</div>
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<section id="mlx-nn-sequential">
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<h1>mlx.nn.Sequential<a class="headerlink" href="#mlx-nn-sequential" title="Permalink to this heading">#</a></h1>
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<dl class="py class">
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<dt class="sig sig-object py" id="mlx.nn.Sequential">
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<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">mlx.nn.</span></span><span class="sig-name descname"><span class="pre">Sequential</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">modules</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#mlx.nn.Sequential" title="Permalink to this definition">#</a></dt>
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<dd><p>A layer that calls the passed callables in order.</p>
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<h1>mlx.nn.SiLU<a class="headerlink" href="#mlx-nn-silu" title="Permalink to this heading">#</a></h1>
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<dt class="sig sig-object py" id="mlx.nn.SiLU">
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<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">mlx.nn.</span></span><span class="sig-name descname"><span class="pre">SiLU</span></span><a class="headerlink" href="#mlx.nn.SiLU" title="Permalink to this definition">#</a></dt>
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<h1>mlx.nn.Step</h1>
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<h1>mlx.nn.Step<a class="headerlink" href="#mlx-nn-step" title="Permalink to this heading">#</a></h1>
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<dl class="py class">
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<dt class="sig sig-object py" id="mlx.nn.Step">
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<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">mlx.nn.</span></span><span class="sig-name descname"><span class="pre">Step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">threshold</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/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#mlx.nn.Step" title="Permalink to this definition">#</a></dt>
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<dd><p>Applies the Step Activation Function.</p>
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<p>This function implements a binary step activation, where the output is set
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to 1 if the input is greater than a specified threshold, and 0 otherwise.</p>
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<div class="math notranslate nohighlight">
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\[\begin{split}\text{step}(x) = \begin{cases}
|
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0 & \text{if } x < \text{threshold} \\
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1 & \text{if } x \geq \text{threshold}
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\end{cases}\end{split}\]</div>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<dd class="field-odd"><p><strong>threshold</strong> – The value to threshold at.</p>
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