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<div class="section" id="embedding">
<h1>Embedding<a class="headerlink" href="#embedding" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="torch.nn.Embedding">
<em class="property">class </em><code class="sig-prename descclassname">torch.nn.</code><code class="sig-name descname">Embedding</code><span class="sig-paren">(</span><em class="sig-param">num_embeddings: int</em>, <em class="sig-param">embedding_dim: int</em>, <em class="sig-param">padding_idx: Optional[int] = None</em>, <em class="sig-param">max_norm: Optional[float] = None</em>, <em class="sig-param">norm_type: float = 2.0</em>, <em class="sig-param">scale_grad_by_freq: bool = False</em>, <em class="sig-param">sparse: bool = False</em>, <em class="sig-param">_weight: Optional[torch.Tensor] = None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/sparse.html#Embedding"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Embedding" title="Permalink to this definition">¶</a></dt>
<dd><p>A simple lookup table that stores embeddings of a fixed dictionary and size.</p>
<p>This module is often used to store word embeddings and retrieve them using indices.
The input to the module is a list of indices, and the output is the corresponding
word embeddings.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>num_embeddings</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a>) – size of the dictionary of embeddings</p></li>
<li><p><strong>embedding_dim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a>) – the size of each embedding vector</p></li>
<li><p><strong>padding_idx</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><em>optional</em>) – If given, pads the output with the embedding vector at <code class="xref py py-attr docutils literal notranslate"><span class="pre">padding_idx</span></code>
(initialized to zeros) whenever it encounters the index.</p></li>
<li><p><strong>max_norm</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a><em>, </em><em>optional</em>) – If given, each embedding vector with norm larger than <code class="xref py py-attr docutils literal notranslate"><span class="pre">max_norm</span></code>
is renormalized to have norm <code class="xref py py-attr docutils literal notranslate"><span class="pre">max_norm</span></code>.</p></li>
<li><p><strong>norm_type</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a><em>, </em><em>optional</em>) – The p of the p-norm to compute for the <code class="xref py py-attr docutils literal notranslate"><span class="pre">max_norm</span></code> option. Default <code class="docutils literal notranslate"><span class="pre">2</span></code>.</p></li>
<li><p><strong>scale_grad_by_freq</strong> (<em>boolean</em><em>, </em><em>optional</em>) – If given, this will scale gradients by the inverse of frequency of
the words in the mini-batch. Default <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
<li><p><strong>sparse</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>optional</em>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code>, gradient w.r.t. <code class="xref py py-attr docutils literal notranslate"><span class="pre">weight</span></code> matrix will be a sparse tensor.
See Notes for more details regarding sparse gradients.</p></li>
</ul>
</dd>
<dt class="field-even">Variables</dt>
<dd class="field-even"><p><strong>~Embedding.weight</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the learnable weights of the module of shape (num_embeddings, embedding_dim)
initialized from <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="script">N</mi><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mn>1</mn><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\mathcal{N}(0, 1)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord"><span class="mord mathcal" style="margin-right:0.14736em;">N</span></span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord">1</span><span class="mclose">)</span></span></span></span>
</span></p>
</dd>
</dl>
<dl class="simple">
<dt>Shape:</dt><dd><ul class="simple">
<li><p>Input: <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mo>∗</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(*)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord">∗</span><span class="mclose">)</span></span></span></span>
</span>, LongTensor of arbitrary shape containing the indices to extract</p></li>
<li><p>Output: <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mo>∗</mo><mo separator="true">,</mo><mi>H</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(*, H)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord">∗</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mclose">)</span></span></span></span>
</span>, where <cite>*</cite> is the input shape and <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>H</mi><mo>=</mo><mtext>embedding_dim</mtext></mrow><annotation encoding="application/x-tex">H=\text{embedding\_dim}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.68333em;vertical-align:0em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:1.00444em;vertical-align:-0.31em;"></span><span class="mord text"><span class="mord">embedding_dim</span></span></span></span></span>
</span></p></li>
</ul>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Keep in mind that only a limited number of optimizers support
sparse gradients: currently it’s <code class="xref py py-class docutils literal notranslate"><span class="pre">optim.SGD</span></code> (<cite>CUDA</cite> and <cite>CPU</cite>),
<code class="xref py py-class docutils literal notranslate"><span class="pre">optim.SparseAdam</span></code> (<cite>CUDA</cite> and <cite>CPU</cite>) and <code class="xref py py-class docutils literal notranslate"><span class="pre">optim.Adagrad</span></code> (<cite>CPU</cite>)</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>With <code class="xref py py-attr docutils literal notranslate"><span class="pre">padding_idx</span></code> set, the embedding vector at
<code class="xref py py-attr docutils literal notranslate"><span class="pre">padding_idx</span></code> is initialized to all zeros. However, note that this
vector can be modified afterwards, e.g., using a customized
initialization method, and thus changing the vector used to pad the
output. The gradient for this vector from <a class="reference internal" href="#torch.nn.Embedding" title="torch.nn.Embedding"><code class="xref py py-class docutils literal notranslate"><span class="pre">Embedding</span></code></a>
is always zero.</p>
</div>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># an Embedding module containing 10 tensors of size 3</span>
<span class="gp">>>> </span><span class="n">embedding</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Embedding</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="c1"># a batch of 2 samples of 4 indices each</span>
<span class="gp">>>> </span><span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">],[</span><span class="mi">4</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">9</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">embedding</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="go">tensor([[[-0.0251, -1.6902, 0.7172],</span>
<span class="go"> [-0.6431, 0.0748, 0.6969],</span>
<span class="go"> [ 1.4970, 1.3448, -0.9685],</span>
<span class="go"> [-0.3677, -2.7265, -0.1685]],</span>
<span class="go"> [[ 1.4970, 1.3448, -0.9685],</span>
<span class="go"> [ 0.4362, -0.4004, 0.9400],</span>
<span class="go"> [-0.6431, 0.0748, 0.6969],</span>
<span class="go"> [ 0.9124, -2.3616, 1.1151]]])</span>
<span class="gp">>>> </span><span class="c1"># example with padding_idx</span>
<span class="gp">>>> </span><span class="n">embedding</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Embedding</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">padding_idx</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">5</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">embedding</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="go">tensor([[[ 0.0000, 0.0000, 0.0000],</span>
<span class="go"> [ 0.1535, -2.0309, 0.9315],</span>
<span class="go"> [ 0.0000, 0.0000, 0.0000],</span>
<span class="go"> [-0.1655, 0.9897, 0.0635]]])</span>
</pre></div>
</div>
<dl class="method">
<dt id="torch.nn.Embedding.from_pretrained">
<em class="property">classmethod </em><code class="sig-name descname">from_pretrained</code><span class="sig-paren">(</span><em class="sig-param">embeddings</em>, <em class="sig-param">freeze=True</em>, <em class="sig-param">padding_idx=None</em>, <em class="sig-param">max_norm=None</em>, <em class="sig-param">norm_type=2.0</em>, <em class="sig-param">scale_grad_by_freq=False</em>, <em class="sig-param">sparse=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/sparse.html#Embedding.from_pretrained"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.Embedding.from_pretrained" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates Embedding instance from given 2-dimensional FloatTensor.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>embeddings</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – FloatTensor containing weights for the Embedding.
First dimension is being passed to Embedding as <code class="docutils literal notranslate"><span class="pre">num_embeddings</span></code>, second as <code class="docutils literal notranslate"><span class="pre">embedding_dim</span></code>.</p></li>
<li><p><strong>freeze</strong> (<em>boolean</em><em>, </em><em>optional</em>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code>, the tensor does not get updated in the learning process.
Equivalent to <code class="docutils literal notranslate"><span class="pre">embedding.weight.requires_grad</span> <span class="pre">=</span> <span class="pre">False</span></code>. Default: <code class="docutils literal notranslate"><span class="pre">True</span></code></p></li>
<li><p><strong>padding_idx</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><em>optional</em>) – See module initialization documentation.</p></li>
<li><p><strong>max_norm</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a><em>, </em><em>optional</em>) – See module initialization documentation.</p></li>
<li><p><strong>norm_type</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a><em>, </em><em>optional</em>) – See module initialization documentation. Default <code class="docutils literal notranslate"><span class="pre">2</span></code>.</p></li>
<li><p><strong>scale_grad_by_freq</strong> (<em>boolean</em><em>, </em><em>optional</em>) – See module initialization documentation. Default <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
<li><p><strong>sparse</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>optional</em>) – See module initialization documentation.</p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># FloatTensor containing pretrained weights</span>
<span class="gp">>>> </span><span class="n">weight</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mf">2.3</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mf">5.1</span><span class="p">,</span> <span class="mf">6.3</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">embedding</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Embedding</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">weight</span><span class="p">)</span>
<span class="gp">>>> </span><span class="c1"># Get embeddings for index 1</span>
<span class="gp">>>> </span><span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">([</span><span class="mi">1</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">embedding</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="go">tensor([[ 4.0000, 5.1000, 6.3000]])</span>
</pre></div>
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