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<div class="section" id="module-torch.utils.tensorboard">
<span id="torch-utils-tensorboard"></span><h1>torch.utils.tensorboard<a class="headerlink" href="#module-torch.utils.tensorboard" title="Permalink to this heading">¶</a></h1>
<p>Before going further, more details on TensorBoard can be found at
<a class="reference external" href="https://www.tensorflow.org/tensorboard/">https://www.tensorflow.org/tensorboard/</a></p>
<p>Once you’ve installed TensorBoard, these utilities let you log PyTorch models
and metrics into a directory for visualization within the TensorBoard UI.
Scalars, images, histograms, graphs, and embedding visualizations are all
supported for PyTorch models and tensors as well as Caffe2 nets and blobs.</p>
<p>The SummaryWriter class is your main entry to log data for consumption
and visualization by TensorBoard. For example:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torchvision</span>
<span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="kn">import</span> <span class="n">SummaryWriter</span>
<span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">transforms</span>
<span class="c1"># Writer will output to ./runs/ directory by default</span>
<span class="n">writer</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">()</span>
<span class="n">transform</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span><span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">((</span><span class="mf">0.5</span><span class="p">,),</span> <span class="p">(</span><span class="mf">0.5</span><span class="p">,))])</span>
<span class="n">trainset</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">MNIST</span><span class="p">(</span><span class="s1">'mnist_train'</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">)</span>
<span class="n">trainloader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">trainset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">resnet50</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
<span class="c1"># Have ResNet model take in grayscale rather than RGB</span>
<span class="n">model</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">images</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">trainloader</span><span class="p">))</span>
<span class="n">grid</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">make_grid</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_image</span><span class="p">(</span><span class="s1">'images'</span><span class="p">,</span> <span class="n">grid</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_graph</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">images</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
</pre></div>
</div>
<p>This can then be visualized with TensorBoard, which should be installable
and runnable with:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">tensorboard</span>
<span class="n">tensorboard</span> <span class="o">--</span><span class="n">logdir</span><span class="o">=</span><span class="n">runs</span>
</pre></div>
</div>
<p>Lots of information can be logged for one experiment. To avoid cluttering
the UI and have better result clustering, we can group plots by naming them
hierarchically. For example, “Loss/train” and “Loss/test” will be grouped
together, while “Accuracy/train” and “Accuracy/test” will be grouped separately
in the TensorBoard interface.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="kn">import</span> <span class="n">SummaryWriter</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">writer</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">()</span>
<span class="k">for</span> <span class="n">n_iter</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">):</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_scalar</span><span class="p">(</span><span class="s1">'Loss/train'</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">(),</span> <span class="n">n_iter</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_scalar</span><span class="p">(</span><span class="s1">'Loss/test'</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">(),</span> <span class="n">n_iter</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_scalar</span><span class="p">(</span><span class="s1">'Accuracy/train'</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">(),</span> <span class="n">n_iter</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_scalar</span><span class="p">(</span><span class="s1">'Accuracy/test'</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">(),</span> <span class="n">n_iter</span><span class="p">)</span>
</pre></div>
</div>
<p>Expected result:</p>
<a class="reference internal image-reference" href="_images/hier_tags.png"><img alt="_images/hier_tags.png" src="_images/hier_tags.png" style="width: 545.25px; height: 525.75px;" /></a>
<div class="line-block">
<div class="line"><br /></div>
<div class="line"><br /></div>
</div>
<dl class="py class">
<dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.tensorboard.writer.</span></span><span class="sig-name descname"><span class="pre">SummaryWriter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comment</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">purge_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_queue</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flush_secs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">120</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filename_suffix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter" title="Permalink to this definition">¶</a></dt>
<dd><p>Writes entries directly to event files in the log_dir to be consumed by TensorBoard.</p>
<p>The <cite>SummaryWriter</cite> class provides a high-level API to create an event file
in a given directory and add summaries and events to it. The class updates the
file contents asynchronously. This allows a training program to call methods
to add data to the file directly from the training loop, without slowing down
training.</p>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.__init__">
<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comment</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">purge_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_queue</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flush_secs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">120</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filename_suffix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Create a <cite>SummaryWriter</cite> that will write out events and summaries to the event file.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>log_dir</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Save directory location. Default is
runs/<strong>CURRENT_DATETIME_HOSTNAME</strong>, which changes after each run.
Use hierarchical folder structure to compare
between runs easily. e.g. pass in ‘runs/exp1’, ‘runs/exp2’, etc.
for each new experiment to compare across them.</p></li>
<li><p><strong>comment</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Comment log_dir suffix appended to the default
<code class="docutils literal notranslate"><span class="pre">log_dir</span></code>. If <code class="docutils literal notranslate"><span class="pre">log_dir</span></code> is assigned, this argument has no effect.</p></li>
<li><p><strong>purge_step</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – When logging crashes at step <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><mi>X</mi></mrow><annotation encoding="application/x-tex">T+X</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7667em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">T</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal" style="margin-right:0.07847em;">X</span></span></span></span></span> and restarts at step <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>T</mi></mrow><annotation encoding="application/x-tex">T</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">T</span></span></span></span></span>,
any events whose global_step larger or equal to <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>T</mi></mrow><annotation encoding="application/x-tex">T</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">T</span></span></span></span></span> will be
purged and hidden from TensorBoard.
Note that crashed and resumed experiments should have the same <code class="docutils literal notranslate"><span class="pre">log_dir</span></code>.</p></li>
<li><p><strong>max_queue</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Size of the queue for pending events and
summaries before one of the ‘add’ calls forces a flush to disk.
Default is ten items.</p></li>
<li><p><strong>flush_secs</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 often, in seconds, to flush the
pending events and summaries to disk. Default is every two minutes.</p></li>
<li><p><strong>filename_suffix</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Suffix added to all event filenames in
the log_dir directory. More details on filename construction in
tensorboard.summary.writer.event_file_writer.EventFileWriter.</p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="kn">import</span> <span class="n">SummaryWriter</span>
<span class="c1"># create a summary writer with automatically generated folder name.</span>
<span class="n">writer</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">()</span>
<span class="c1"># folder location: runs/May04_22-14-54_s-MacBook-Pro.local/</span>
<span class="c1"># create a summary writer using the specified folder name.</span>
<span class="n">writer</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">(</span><span class="s2">"my_experiment"</span><span class="p">)</span>
<span class="c1"># folder location: my_experiment</span>
<span class="c1"># create a summary writer with comment appended.</span>
<span class="n">writer</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">(</span><span class="n">comment</span><span class="o">=</span><span class="s2">"LR_0.1_BATCH_16"</span><span class="p">)</span>
<span class="c1"># folder location: runs/May04_22-14-54_s-MacBook-Pro.localLR_0.1_BATCH_16/</span>
</pre></div>
</div>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_scalar">
<span class="sig-name descname"><span class="pre">add_scalar</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scalar_value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">new_style</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">double_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_scalar"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_scalar" title="Permalink to this definition">¶</a></dt>
<dd><p>Add scalar data to summary.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tag</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Data identifier</p></li>
<li><p><strong>scalar_value</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> or </em><em>string/blobname</em>) – Value to save</p></li>
<li><p><strong>global_step</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Global step value to record</p></li>
<li><p><strong>walltime</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Optional override default walltime (time.time())
with seconds after epoch of event</p></li>
<li><p><strong>new_style</strong> (<em>boolean</em>) – Whether to use new style (tensor field) or old
style (simple_value field). New style could lead to faster data loading.</p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="kn">import</span> <span class="n">SummaryWriter</span>
<span class="n">writer</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">()</span>
<span class="n">x</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">x</span><span class="p">:</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_scalar</span><span class="p">(</span><span class="s1">'y=2x'</span><span class="p">,</span> <span class="n">i</span> <span class="o">*</span> <span class="mi">2</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
</pre></div>
</div>
<p>Expected result:</p>
<a class="reference internal image-reference" href="_images/add_scalar.png"><img alt="_images/add_scalar.png" src="_images/add_scalar.png" style="width: 312.0px; height: 238.0px;" /></a>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_scalars">
<span class="sig-name descname"><span class="pre">add_scalars</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">main_tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag_scalar_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</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="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_scalars"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_scalars" title="Permalink to this definition">¶</a></dt>
<dd><p>Add many scalar data to summary.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>main_tag</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The parent name for the tags</p></li>
<li><p><strong>tag_scalar_dict</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Key-value pair storing the tag and corresponding values</p></li>
<li><p><strong>global_step</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Global step value to record</p></li>
<li><p><strong>walltime</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Optional override default walltime (time.time())
seconds after epoch of event</p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="kn">import</span> <span class="n">SummaryWriter</span>
<span class="n">writer</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">()</span>
<span class="n">r</span> <span class="o">=</span> <span class="mi">5</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">):</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_scalars</span><span class="p">(</span><span class="s1">'run_14h'</span><span class="p">,</span> <span class="p">{</span><span class="s1">'xsinx'</span><span class="p">:</span><span class="n">i</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">i</span><span class="o">/</span><span class="n">r</span><span class="p">),</span>
<span class="s1">'xcosx'</span><span class="p">:</span><span class="n">i</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">i</span><span class="o">/</span><span class="n">r</span><span class="p">),</span>
<span class="s1">'tanx'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">tan</span><span class="p">(</span><span class="n">i</span><span class="o">/</span><span class="n">r</span><span class="p">)},</span> <span class="n">i</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="c1"># This call adds three values to the same scalar plot with the tag</span>
<span class="c1"># 'run_14h' in TensorBoard's scalar section.</span>
</pre></div>
</div>
<p>Expected result:</p>
<a class="reference internal image-reference" href="_images/add_scalars.png"><img alt="_images/add_scalars.png" src="_images/add_scalars.png" style="width: 348.0px; height: 264.0px;" /></a>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_histogram">
<span class="sig-name descname"><span class="pre">add_histogram</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'tensorflow'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_bins</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="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_histogram"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_histogram" title="Permalink to this definition">¶</a></dt>
<dd><p>Add histogram to summary.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tag</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Data identifier</p></li>
<li><p><strong>values</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a><em>, </em><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><em>numpy.ndarray</em></a><em>, or </em><em>string/blobname</em>) – Values to build histogram</p></li>
<li><p><strong>global_step</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Global step value to record</p></li>
<li><p><strong>bins</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – One of {‘tensorflow’,’auto’, ‘fd’, …}. This determines how the bins are made. You can find
other options in: <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html">https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html</a></p></li>
<li><p><strong>walltime</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Optional override default walltime (time.time())
seconds after epoch of event</p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="kn">import</span> <span class="n">SummaryWriter</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">writer</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">(</span><span class="mi">1000</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_histogram</span><span class="p">(</span><span class="s1">'distribution centers'</span><span class="p">,</span> <span class="n">x</span> <span class="o">+</span> <span class="n">i</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
</pre></div>
</div>
<p>Expected result:</p>
<a class="reference internal image-reference" href="_images/add_histogram.png"><img alt="_images/add_histogram.png" src="_images/add_histogram.png" style="width: 275.0px; height: 217.0px;" /></a>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_image">
<span class="sig-name descname"><span class="pre">add_image</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">img_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataformats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'CHW'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_image"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_image" title="Permalink to this definition">¶</a></dt>
<dd><p>Add image data to summary.</p>
<p>Note that this requires the <code class="docutils literal notranslate"><span class="pre">pillow</span></code> package.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tag</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Data identifier</p></li>
<li><p><strong>img_tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a><em>, </em><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><em>numpy.ndarray</em></a><em>, or </em><em>string/blobname</em>) – Image data</p></li>
<li><p><strong>global_step</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Global step value to record</p></li>
<li><p><strong>walltime</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Optional override default walltime (time.time())
seconds after epoch of event</p></li>
<li><p><strong>dataformats</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Image data format specification of the form
CHW, HWC, HW, WH, etc.</p></li>
</ul>
</dd>
</dl>
<dl class="simple">
<dt>Shape:</dt><dd><p>img_tensor: Default 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><mn>3</mn><mo separator="true">,</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(3, H, W)</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">3</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span></span>. You can use <code class="docutils literal notranslate"><span class="pre">torchvision.utils.make_grid()</span></code> to
convert a batch of tensor into 3xHxW format or call <code class="docutils literal notranslate"><span class="pre">add_images</span></code> and let us do the job.
Tensor with <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><mn>1</mn><mo separator="true">,</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(1, H, W)</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">1</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mclose">)</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><mo stretchy="false">(</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(H, W)</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:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mclose">)</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><mo stretchy="false">(</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo separator="true">,</mo><mn>3</mn><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(H, W, 3)</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:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">3</span><span class="mclose">)</span></span></span></span></span> is also suitable as long as
corresponding <code class="docutils literal notranslate"><span class="pre">dataformats</span></code> argument is passed, e.g. <code class="docutils literal notranslate"><span class="pre">CHW</span></code>, <code class="docutils literal notranslate"><span class="pre">HWC</span></code>, <code class="docutils literal notranslate"><span class="pre">HW</span></code>.</p>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="kn">import</span> <span class="n">SummaryWriter</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span>
<span class="n">img</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span> <span class="o">/</span> <span class="mi">10000</span>
<span class="n">img</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span> <span class="o">/</span> <span class="mi">10000</span>
<span class="n">img_HWC</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">img_HWC</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span> <span class="o">/</span> <span class="mi">10000</span>
<span class="n">img_HWC</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span> <span class="o">/</span> <span class="mi">10000</span>
<span class="n">writer</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">()</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_image</span><span class="p">(</span><span class="s1">'my_image'</span><span class="p">,</span> <span class="n">img</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="c1"># If you have non-default dimension setting, set the dataformats argument.</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_image</span><span class="p">(</span><span class="s1">'my_image_HWC'</span><span class="p">,</span> <span class="n">img_HWC</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">dataformats</span><span class="o">=</span><span class="s1">'HWC'</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
</pre></div>
</div>
<p>Expected result:</p>
<a class="reference internal image-reference" href="_images/add_image.png"><img alt="_images/add_image.png" src="_images/add_image.png" style="width: 365.0px; height: 411.0px;" /></a>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_images">
<span class="sig-name descname"><span class="pre">add_images</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">img_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataformats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'NCHW'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_images"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_images" title="Permalink to this definition">¶</a></dt>
<dd><p>Add batched image data to summary.</p>
<p>Note that this requires the <code class="docutils literal notranslate"><span class="pre">pillow</span></code> package.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tag</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Data identifier</p></li>
<li><p><strong>img_tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a><em>, </em><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><em>numpy.ndarray</em></a><em>, or </em><em>string/blobname</em>) – Image data</p></li>
<li><p><strong>global_step</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Global step value to record</p></li>
<li><p><strong>walltime</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Optional override default walltime (time.time())
seconds after epoch of event</p></li>
<li><p><strong>dataformats</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Image data format specification of the form
NCHW, NHWC, CHW, HWC, HW, WH, etc.</p></li>
</ul>
</dd>
</dl>
<dl class="simple">
<dt>Shape:</dt><dd><p>img_tensor: Default 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><mn>3</mn><mo separator="true">,</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, 3, H, W)</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:0.1667em;"></span><span class="mord">3</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span></span>. If <code class="docutils literal notranslate"><span class="pre">dataformats</span></code> is specified, other shape will be
accepted. e.g. NCHW or NHWC.</p>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="kn">import</span> <span class="n">SummaryWriter</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">img_batch</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">16</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">16</span><span class="p">):</span>
<span class="n">img_batch</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span> <span class="o">/</span> <span class="mi">10000</span> <span class="o">/</span> <span class="mi">16</span> <span class="o">*</span> <span class="n">i</span>
<span class="n">img_batch</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span> <span class="o">/</span> <span class="mi">10000</span><span class="p">)</span> <span class="o">/</span> <span class="mi">16</span> <span class="o">*</span> <span class="n">i</span>
<span class="n">writer</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">()</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_images</span><span class="p">(</span><span class="s1">'my_image_batch'</span><span class="p">,</span> <span class="n">img_batch</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
</pre></div>
</div>
<p>Expected result:</p>
<a class="reference internal image-reference" href="_images/add_images.png"><img alt="_images/add_images.png" src="_images/add_images.png" style="width: 488.4px; height: 147.6px;" /></a>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_figure">
<span class="sig-name descname"><span class="pre">add_figure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figure</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">close</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</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="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_figure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_figure" title="Permalink to this definition">¶</a></dt>
<dd><p>Render matplotlib figure into an image and add it to summary.</p>
<p>Note that this requires the <code class="docutils literal notranslate"><span class="pre">matplotlib</span></code> package.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tag</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Data identifier</p></li>
<li><p><strong>figure</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.12)"><em>Union</em></a><em>[</em><em>Figure</em><em>, </em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><em>List</em></a><em>[</em><em>Figure</em><em>]</em><em>]</em>) – Figure or a list of figures</p></li>
<li><p><strong>global_step</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.12)"><em>Optional</em></a><em>[</em><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>) – Global step value to record</p></li>
<li><p><strong>close</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Flag to automatically close the figure</p></li>
<li><p><strong>walltime</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.12)"><em>Optional</em></a><em>[</em><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>) – Optional override default walltime (time.time())
seconds after epoch of event</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_video">
<span class="sig-name descname"><span class="pre">add_video</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vid_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</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="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_video"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_video" title="Permalink to this definition">¶</a></dt>
<dd><p>Add video data to summary.</p>
<p>Note that this requires the <code class="docutils literal notranslate"><span class="pre">moviepy</span></code> package.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tag</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Data identifier</p></li>
<li><p><strong>vid_tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a>) – Video data</p></li>
<li><p><strong>global_step</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Global step value to record</p></li>
<li><p><strong>fps</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> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Frames per second</p></li>
<li><p><strong>walltime</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Optional override default walltime (time.time())
seconds after epoch of event</p></li>
</ul>
</dd>
</dl>
<dl class="simple">
<dt>Shape:</dt><dd><p>vid_tensor: <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>T</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, T, C, H, W)</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:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">T</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span></span>. The values should lie in [0, 255] for type <cite>uint8</cite> or [0, 1] for type <cite>float</cite>.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_audio">
<span class="sig-name descname"><span class="pre">add_audio</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">snd_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">44100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</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="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_audio"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_audio" title="Permalink to this definition">¶</a></dt>
<dd><p>Add audio data to summary.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tag</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Data identifier</p></li>
<li><p><strong>snd_tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a>) – Sound data</p></li>
<li><p><strong>global_step</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Global step value to record</p></li>
<li><p><strong>sample_rate</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – sample rate in Hz</p></li>
<li><p><strong>walltime</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Optional override default walltime (time.time())
seconds after epoch of event</p></li>
</ul>
</dd>
</dl>
<dl class="simple">
<dt>Shape:</dt><dd><p>snd_tensor: <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><mn>1</mn><mo separator="true">,</mo><mi>L</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(1, L)</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">1</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">L</span><span class="mclose">)</span></span></span></span></span>. The values should lie between [-1, 1].</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_text">
<span class="sig-name descname"><span class="pre">add_text</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">text_string</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</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="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_text"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_text" title="Permalink to this definition">¶</a></dt>
<dd><p>Add text data to summary.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tag</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Data identifier</p></li>
<li><p><strong>text_string</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – String to save</p></li>
<li><p><strong>global_step</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Global step value to record</p></li>
<li><p><strong>walltime</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Optional override default walltime (time.time())
seconds after epoch of event</p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">writer</span><span class="o">.</span><span class="n">add_text</span><span class="p">(</span><span class="s1">'lstm'</span><span class="p">,</span> <span class="s1">'This is an lstm'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_text</span><span class="p">(</span><span class="s1">'rnn'</span><span class="p">,</span> <span class="s1">'This is an rnn'</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_graph">
<span class="sig-name descname"><span class="pre">add_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_to_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_strict_trace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_graph" title="Permalink to this definition">¶</a></dt>
<dd><p>Add graph data to summary.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model</strong> (<a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><em>torch.nn.Module</em></a>) – Model to draw.</p></li>
<li><p><strong>input_to_model</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a>) – A variable or a tuple of
variables to be fed.</p></li>
<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to print graph structure in console.</p></li>
<li><p><strong>use_strict_trace</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to pass keyword argument <cite>strict</cite> to
<cite>torch.jit.trace</cite>. Pass False when you want the tracer to
record your mutable container types (list, dict)</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_embedding">
<span class="sig-name descname"><span class="pre">add_embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_img</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata_header</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="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_embedding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_embedding" title="Permalink to this definition">¶</a></dt>
<dd><p>Add embedding projector data to summary.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>mat</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a><em> or </em><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><em>numpy.ndarray</em></a>) – A matrix which each row is the feature vector of the data point</p></li>
<li><p><strong>metadata</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – A list of labels, each element will be convert to string</p></li>
<li><p><strong>label_img</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a>) – Images correspond to each data point</p></li>
<li><p><strong>global_step</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Global step value to record</p></li>
<li><p><strong>tag</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Name for the embedding</p></li>
</ul>
</dd>
</dl>
<dl>
<dt>Shape:</dt><dd><p>mat: <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>D</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, D)</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:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="mclose">)</span></span></span></span></span>, where N is number of data and D is feature dimension</p>
<p>label_img: <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>C</mi><mo separator="true">,</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, H, W)</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:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span></span></p>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">keyword</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="n">meta</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">meta</span><span class="p">)</span><span class="o"><</span><span class="mi">100</span><span class="p">:</span>
<span class="n">meta</span> <span class="o">=</span> <span class="n">meta</span><span class="o">+</span><span class="n">keyword</span><span class="o">.</span><span class="n">kwlist</span> <span class="c1"># get some strings</span>
<span class="n">meta</span> <span class="o">=</span> <span class="n">meta</span><span class="p">[:</span><span class="mi">100</span><span class="p">]</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">meta</span><span class="p">):</span>
<span class="n">meta</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
<span class="n">label_img</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">32</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">):</span>
<span class="n">label_img</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">*=</span><span class="n">i</span><span class="o">/</span><span class="mf">100.0</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_embedding</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">metadata</span><span class="o">=</span><span class="n">meta</span><span class="p">,</span> <span class="n">label_img</span><span class="o">=</span><span class="n">label_img</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_embedding</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">label_img</span><span class="o">=</span><span class="n">label_img</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_embedding</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">metadata</span><span class="o">=</span><span class="n">meta</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_pr_curve">
<span class="sig-name descname"><span class="pre">add_pr_curve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predictions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_thresholds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">127</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</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="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_pr_curve"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_pr_curve" title="Permalink to this definition">¶</a></dt>
<dd><p>Add precision recall curve.</p>
<p>Plotting a precision-recall curve lets you understand your model’s
performance under different threshold settings. With this function,
you provide the ground truth labeling (T/F) and prediction confidence
(usually the output of your model) for each target. The TensorBoard UI
will let you choose the threshold interactively.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tag</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Data identifier</p></li>
<li><p><strong>labels</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a><em>, </em><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><em>numpy.ndarray</em></a><em>, or </em><em>string/blobname</em>) – Ground truth data. Binary label for each element.</p></li>
<li><p><strong>predictions</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a><em>, </em><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><em>numpy.ndarray</em></a><em>, or </em><em>string/blobname</em>) – The probability that an element be classified as true.
Value should be in [0, 1]</p></li>
<li><p><strong>global_step</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Global step value to record</p></li>
<li><p><strong>num_thresholds</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 thresholds used to draw the curve.</p></li>
<li><p><strong>walltime</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Optional override default walltime (time.time())
seconds after epoch of event</p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="kn">import</span> <span class="n">SummaryWriter</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span> <span class="c1"># binary label</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
<span class="n">writer</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">()</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_pr_curve</span><span class="p">(</span><span class="s1">'pr_curve'</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
</pre></div>
</div>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_custom_scalars">
<span class="sig-name descname"><span class="pre">add_custom_scalars</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">layout</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_custom_scalars"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_custom_scalars" title="Permalink to this definition">¶</a></dt>
<dd><p>Create special chart by collecting charts tags in ‘scalars’.</p>
<p>NOTE: This function can only be called once for each SummaryWriter() object.</p>
<p>Because it only provides metadata to tensorboard, the function can be called before or after the training loop.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>layout</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – {categoryName: <em>charts</em>}, where <em>charts</em> is also a dictionary
{chartName: <em>ListOfProperties</em>}. The first element in <em>ListOfProperties</em> is the chart’s type
(one of <strong>Multiline</strong> or <strong>Margin</strong>) and the second element should be a list containing the tags
you have used in add_scalar function, which will be collected into the new chart.</p>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">layout</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'Taiwan'</span><span class="p">:{</span><span class="s1">'twse'</span><span class="p">:[</span><span class="s1">'Multiline'</span><span class="p">,[</span><span class="s1">'twse/0050'</span><span class="p">,</span> <span class="s1">'twse/2330'</span><span class="p">]]},</span>
<span class="s1">'USA'</span><span class="p">:{</span> <span class="s1">'dow'</span><span class="p">:[</span><span class="s1">'Margin'</span><span class="p">,</span> <span class="p">[</span><span class="s1">'dow/aaa'</span><span class="p">,</span> <span class="s1">'dow/bbb'</span><span class="p">,</span> <span class="s1">'dow/ccc'</span><span class="p">]],</span>
<span class="s1">'nasdaq'</span><span class="p">:[</span><span class="s1">'Margin'</span><span class="p">,</span> <span class="p">[</span><span class="s1">'nasdaq/aaa'</span><span class="p">,</span> <span class="s1">'nasdaq/bbb'</span><span class="p">,</span> <span class="s1">'nasdaq/ccc'</span><span class="p">]]}}</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_custom_scalars</span><span class="p">(</span><span class="n">layout</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_mesh">
<span class="sig-name descname"><span class="pre">add_mesh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vertices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">faces</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">config_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</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="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_mesh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_mesh" title="Permalink to this definition">¶</a></dt>
<dd><p>Add meshes or 3D point clouds to TensorBoard.</p>
<p>The visualization is based on Three.js,
so it allows users to interact with the rendered object. Besides the basic definitions
such as vertices, faces, users can further provide camera parameter, lighting condition, etc.
Please check <a class="reference external" href="https://threejs.org/docs/index.html#manual/en/introduction/Creating-a-scene">https://threejs.org/docs/index.html#manual/en/introduction/Creating-a-scene</a> for
advanced usage.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tag</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Data identifier</p></li>
<li><p><strong>vertices</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a>) – List of the 3D coordinates of vertices.</p></li>
<li><p><strong>colors</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a>) – Colors for each vertex</p></li>
<li><p><strong>faces</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a>) – Indices of vertices within each triangle. (Optional)</p></li>
<li><p><strong>config_dict</strong> – Dictionary with ThreeJS classes names and configuration.</p></li>
<li><p><strong>global_step</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Global step value to record</p></li>
<li><p><strong>walltime</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Optional override default walltime (time.time())
seconds after epoch of event</p></li>
</ul>
</dd>
</dl>
<dl>
<dt>Shape:</dt><dd><p>vertices: <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>B</mi><mo separator="true">,</mo><mi>N</mi><mo separator="true">,</mo><mn>3</mn><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(B, N, 3)</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:0.05017em;">B</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">3</span><span class="mclose">)</span></span></span></span></span>. (batch, number_of_vertices, channels)</p>
<p>colors: <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>B</mi><mo separator="true">,</mo><mi>N</mi><mo separator="true">,</mo><mn>3</mn><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(B, N, 3)</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:0.05017em;">B</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">3</span><span class="mclose">)</span></span></span></span></span>. The values should lie in [0, 255] for type <cite>uint8</cite> or [0, 1] for type <cite>float</cite>.</p>
<p>faces: <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>B</mi><mo separator="true">,</mo><mi>N</mi><mo separator="true">,</mo><mn>3</mn><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(B, N, 3)</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:0.05017em;">B</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">3</span><span class="mclose">)</span></span></span></span></span>. The values should lie in [0, number_of_vertices] for type <cite>uint8</cite>.</p>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="kn">import</span> <span class="n">SummaryWriter</span>
<span class="n">vertices_tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">([</span>