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<div class="section" id="ctcloss">
<h1>CTCLoss<a class="headerlink" href="#ctcloss" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="torch.nn.CTCLoss">
<em class="property">class </em><code class="sig-prename descclassname">torch.nn.</code><code class="sig-name descname">CTCLoss</code><span class="sig-paren">(</span><em class="sig-param">blank: int = 0</em>, <em class="sig-param">reduction: str = 'mean'</em>, <em class="sig-param">zero_infinity: bool = False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#CTCLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.CTCLoss" title="Permalink to this definition">¶</a></dt>
<dd><p>The Connectionist Temporal Classification loss.</p>
<p>Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the
probability of possible alignments of input to target, producing a loss value which is differentiable
with respect to each input node. The alignment of input to target is assumed to be “many-to-one”, which
limits the length of the target sequence such that it must be <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo>≤</mo></mrow><annotation encoding="application/x-tex">\leq</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7719400000000001em;vertical-align:-0.13597em;"></span><span class="mrel">≤</span></span></span></span>
</span> the input length.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>blank</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>) – blank label. Default <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mn>0</mn></mrow><annotation encoding="application/x-tex">0</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.64444em;vertical-align:0em;"></span><span class="mord">0</span></span></span></span>
</span>.</p></li>
<li><p><strong>reduction</strong> (<em>string</em><em>, </em><em>optional</em>) – Specifies the reduction to apply to the output:
<code class="docutils literal notranslate"><span class="pre">'none'</span></code> | <code class="docutils literal notranslate"><span class="pre">'mean'</span></code> | <code class="docutils literal notranslate"><span class="pre">'sum'</span></code>. <code class="docutils literal notranslate"><span class="pre">'none'</span></code>: no reduction will be applied,
<code class="docutils literal notranslate"><span class="pre">'mean'</span></code>: the output losses will be divided by the target lengths and
then the mean over the batch is taken. Default: <code class="docutils literal notranslate"><span class="pre">'mean'</span></code></p></li>
<li><p><strong>zero_infinity</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>) – Whether to zero infinite losses and the associated gradients.
Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>
Infinite losses mainly occur when the inputs are too short
to be aligned to the targets.</p></li>
</ul>
</dd>
</dl>
<dl class="simple">
<dt>Shape:</dt><dd><ul class="simple">
<li><p>Log_probs: Tensor of size <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><mi>T</mi><mo separator="true">,</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(T, N, C)</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 mathnormal" style="margin-right:0.13889em;">T</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mclose">)</span></span></span></span>
</span>,
where <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>T</mi><mo>=</mo><mtext>input length</mtext></mrow><annotation encoding="application/x-tex">T = \text{input length}</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:0.13889em;">T</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:0.8888799999999999em;vertical-align:-0.19444em;"></span><span class="mord text"><span class="mord">input length</span></span></span></span></span>
</span>,
<span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>N</mi><mo>=</mo><mtext>batch size</mtext></mrow><annotation encoding="application/x-tex">N = \text{batch size}</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:0.10903em;">N</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:0.69444em;vertical-align:0em;"></span><span class="mord text"><span class="mord">batch size</span></span></span></span></span>
</span>, and
<span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>C</mi><mo>=</mo><mtext>number of classes (including blank)</mtext></mrow><annotation encoding="application/x-tex">C = \text{number of classes (including blank)}</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:0.07153em;">C</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:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">number of classes (including blank)</span></span></span></span></span>
</span>.
The logarithmized probabilities of the outputs (e.g. obtained with
<a class="reference internal" href="../nn.functional.html#torch.nn.functional.log_softmax" title="torch.nn.functional.log_softmax"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.log_softmax()</span></code></a>).</p></li>
<li><p>Targets: Tensor of size <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><mi>N</mi><mo separator="true">,</mo><mi>S</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, S)</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 mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal" style="margin-right:0.05764em;">S</span><span class="mclose">)</span></span></span></span>
</span> or
<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><mi mathvariant="normal">sum</mi><mo></mo><mo stretchy="false">(</mo><mtext>target_lengths</mtext><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\operatorname{sum}(\text{target\_lengths}))</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mop"><span class="mord mathrm">s</span><span class="mord mathrm">u</span><span class="mord mathrm">m</span></span><span class="mopen">(</span><span class="mord text"><span class="mord">target_lengths</span></span><span class="mclose">)</span><span class="mclose">)</span></span></span></span>
</span>,
where <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>N</mi><mo>=</mo><mtext>batch size</mtext></mrow><annotation encoding="application/x-tex">N = \text{batch size}</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:0.10903em;">N</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:0.69444em;vertical-align:0em;"></span><span class="mord text"><span class="mord">batch size</span></span></span></span></span>
</span> and
<span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>S</mi><mo>=</mo><mtext>max target length, if shape is </mtext><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>S</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">S = \text{max target length, if shape is } (N, S)</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:0.05764em;">S</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:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">max target length, if shape is </span></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal" style="margin-right:0.05764em;">S</span><span class="mclose">)</span></span></span></span>
</span>.
It represent the target sequences. Each element in the target
sequence is a class index. And the target index cannot be blank (default=0).
In the <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><mi>N</mi><mo separator="true">,</mo><mi>S</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, S)</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 mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal" style="margin-right:0.05764em;">S</span><span class="mclose">)</span></span></span></span>
</span> form, targets are padded to the
length of the longest sequence, and stacked.
In the <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><mi mathvariant="normal">sum</mi><mo></mo><mo stretchy="false">(</mo><mtext>target_lengths</mtext><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\operatorname{sum}(\text{target\_lengths}))</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mop"><span class="mord mathrm">s</span><span class="mord mathrm">u</span><span class="mord mathrm">m</span></span><span class="mopen">(</span><span class="mord text"><span class="mord">target_lengths</span></span><span class="mclose">)</span><span class="mclose">)</span></span></span></span>
</span> form,
the targets are assumed to be un-padded and
concatenated within 1 dimension.</p></li>
<li><p>Input_lengths: Tuple or tensor of size <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><mi>N</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N)</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 mathnormal" style="margin-right:0.10903em;">N</span><span class="mclose">)</span></span></span></span>
</span>,
where <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>N</mi><mo>=</mo><mtext>batch size</mtext></mrow><annotation encoding="application/x-tex">N = \text{batch size}</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:0.10903em;">N</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:0.69444em;vertical-align:0em;"></span><span class="mord text"><span class="mord">batch size</span></span></span></span></span>
</span>. It represent the lengths of the
inputs (must each be <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo>≤</mo><mi>T</mi></mrow><annotation encoding="application/x-tex">\leq T</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7719400000000001em;vertical-align:-0.13597em;"></span><span class="mrel">≤</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:0.68333em;vertical-align:0em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">T</span></span></span></span>
</span>). And the lengths are specified
for each sequence to achieve masking under the assumption that sequences
are padded to equal lengths.</p></li>
<li><p>Target_lengths: Tuple or tensor of size <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><mi>N</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N)</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 mathnormal" style="margin-right:0.10903em;">N</span><span class="mclose">)</span></span></span></span>
</span>,
where <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>N</mi><mo>=</mo><mtext>batch size</mtext></mrow><annotation encoding="application/x-tex">N = \text{batch size}</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:0.10903em;">N</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:0.69444em;vertical-align:0em;"></span><span class="mord text"><span class="mord">batch size</span></span></span></span></span>
</span>. It represent lengths of the targets.
Lengths are specified for each sequence to achieve masking under the
assumption that sequences are padded to equal lengths. If target shape is
<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><mi>N</mi><mo separator="true">,</mo><mi>S</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N,S)</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 mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal" style="margin-right:0.05764em;">S</span><span class="mclose">)</span></span></span></span>
</span>, target_lengths are effectively the stop index
<span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>s</mi><mi>n</mi></msub></mrow><annotation encoding="application/x-tex">s_n</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.58056em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal">s</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.151392em;"><span style="top:-2.5500000000000003em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">n</span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span>
</span> for each target sequence, such that <code class="docutils literal notranslate"><span class="pre">target_n</span> <span class="pre">=</span> <span class="pre">targets[n,0:s_n]</span></code> for
each target in a batch. Lengths must each be <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo>≤</mo><mi>S</mi></mrow><annotation encoding="application/x-tex">\leq S</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7719400000000001em;vertical-align:-0.13597em;"></span><span class="mrel">≤</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:0.68333em;vertical-align:0em;"></span><span class="mord mathnormal" style="margin-right:0.05764em;">S</span></span></span></span>
</span>
If the targets are given as a 1d tensor that is the concatenation of individual
targets, the target_lengths must add up to the total length of the tensor.</p></li>
<li><p>Output: scalar. If <code class="xref py py-attr docutils literal notranslate"><span class="pre">reduction</span></code> is <code class="docutils literal notranslate"><span class="pre">'none'</span></code>, then
<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><mi>N</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N)</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 mathnormal" style="margin-right:0.10903em;">N</span><span class="mclose">)</span></span></span></span>
</span>, where <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>N</mi><mo>=</mo><mtext>batch size</mtext></mrow><annotation encoding="application/x-tex">N = \text{batch size}</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:0.10903em;">N</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:0.69444em;vertical-align:0em;"></span><span class="mord text"><span class="mord">batch size</span></span></span></span></span>
</span>.</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"># Target are to be padded</span>
<span class="gp">>>> </span><span class="n">T</span> <span class="o">=</span> <span class="mi">50</span> <span class="c1"># Input sequence length</span>
<span class="gp">>>> </span><span class="n">C</span> <span class="o">=</span> <span class="mi">20</span> <span class="c1"># Number of classes (including blank)</span>
<span class="gp">>>> </span><span class="n">N</span> <span class="o">=</span> <span class="mi">16</span> <span class="c1"># Batch size</span>
<span class="gp">>>> </span><span class="n">S</span> <span class="o">=</span> <span class="mi">30</span> <span class="c1"># Target sequence length of longest target in batch (padding length)</span>
<span class="gp">>>> </span><span class="n">S_min</span> <span class="o">=</span> <span class="mi">10</span> <span class="c1"># Minimum target length, for demonstration purposes</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="c1"># Initialize random batch of input vectors, for *size = (T,N,C)</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">randn</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span><span class="o">.</span><span class="n">log_softmax</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">()</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="c1"># Initialize random batch of targets (0 = blank, 1:C = classes)</span>
<span class="gp">>>> </span><span class="n">target</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="n">C</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">S</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="n">input_lengths</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">N</span><span class="p">,),</span> <span class="n">fill_value</span><span class="o">=</span><span class="n">T</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">target_lengths</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="n">S_min</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="n">S</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">N</span><span class="p">,),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">ctc_loss</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">CTCLoss</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">loss</span> <span class="o">=</span> <span class="n">ctc_loss</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">input_lengths</span><span class="p">,</span> <span class="n">target_lengths</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="go">>>></span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="c1"># Target are to be un-padded</span>
<span class="gp">>>> </span><span class="n">T</span> <span class="o">=</span> <span class="mi">50</span> <span class="c1"># Input sequence length</span>
<span class="gp">>>> </span><span class="n">C</span> <span class="o">=</span> <span class="mi">20</span> <span class="c1"># Number of classes (including blank)</span>
<span class="gp">>>> </span><span class="n">N</span> <span class="o">=</span> <span class="mi">16</span> <span class="c1"># Batch size</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="c1"># Initialize random batch of input vectors, for *size = (T,N,C)</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">randn</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span><span class="o">.</span><span class="n">log_softmax</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">input_lengths</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">N</span><span class="p">,),</span> <span class="n">fill_value</span><span class="o">=</span><span class="n">T</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="c1"># Initialize random batch of targets (0 = blank, 1:C = classes)</span>
<span class="gp">>>> </span><span class="n">target_lengths</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="n">T</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">N</span><span class="p">,),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">target</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="n">C</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">target_lengths</span><span class="p">),),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">ctc_loss</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">CTCLoss</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">loss</span> <span class="o">=</span> <span class="n">ctc_loss</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">input_lengths</span><span class="p">,</span> <span class="n">target_lengths</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
</pre></div>
</div>
<dl class="simple">
<dt>Reference:</dt><dd><p>A. Graves et al.: Connectionist Temporal Classification:
Labelling Unsegmented Sequence Data with Recurrent Neural Networks:
<a class="reference external" href="https://www.cs.toronto.edu/~graves/icml_2006.pdf">https://www.cs.toronto.edu/~graves/icml_2006.pdf</a></p>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>In order to use CuDNN, the following must be satisfied: <code class="xref py py-attr docutils literal notranslate"><span class="pre">targets</span></code> must be
in concatenated format, all <code class="xref py py-attr docutils literal notranslate"><span class="pre">input_lengths</span></code> must be <cite>T</cite>. <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>b</mi><mi>l</mi><mi>a</mi><mi>n</mi><mi>k</mi><mo>=</mo><mn>0</mn></mrow><annotation encoding="application/x-tex">blank=0</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.69444em;vertical-align:0em;"></span><span class="mord mathnormal">b</span><span class="mord mathnormal" style="margin-right:0.01968em;">l</span><span class="mord mathnormal">a</span><span class="mord mathnormal">n</span><span class="mord mathnormal" style="margin-right:0.03148em;">k</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:0.64444em;vertical-align:0em;"></span><span class="mord">0</span></span></span></span>
</span>,
<code class="xref py py-attr docutils literal notranslate"><span class="pre">target_lengths</span></code> <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo>≤</mo><mn>256</mn></mrow><annotation encoding="application/x-tex">\leq 256</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7719400000000001em;vertical-align:-0.13597em;"></span><span class="mrel">≤</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:0.64444em;vertical-align:0em;"></span><span class="mord">2</span><span class="mord">5</span><span class="mord">6</span></span></span></span>
</span>, the integer arguments must be of
dtype <code class="xref py py-attr docutils literal notranslate"><span class="pre">torch.int32</span></code>.</p>
<p>The regular implementation uses the (more common in PyTorch) <cite>torch.long</cite> dtype.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>In some circumstances when using the CUDA backend with CuDNN, this operator
may select a nondeterministic algorithm to increase performance. If this is
undesirable, you can try to make the operation deterministic (potentially at
a performance cost) by setting <code class="docutils literal notranslate"><span class="pre">torch.backends.cudnn.deterministic</span> <span class="pre">=</span>
<span class="pre">True</span></code>.
Please see the notes on <a class="reference internal" href="../notes/randomness.html"><span class="doc">Reproducibility</span></a> for background.</p>
</div>
</dd></dl>
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