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  <section id="slideflow-modelparams">
<span id="model-params"></span><h1>slideflow.ModelParams<a class="headerlink" href="#slideflow-modelparams" title="Permalink to this heading">¶</a></h1>
<p>The <a class="reference internal" href="#slideflow.ModelParams" title="slideflow.ModelParams"><code class="xref py py-class docutils literal notranslate"><span class="pre">ModelParams</span></code></a> class organizes model and training parameters/hyperparameters and assists with model building.</p>
<p>See <a class="reference internal" href="../training/#training"><span class="std std-ref">Training</span></a> for a detailed look at how to train models.</p>
<section id="modelparams">
<h2>ModelParams<a class="headerlink" href="#modelparams" title="Permalink to this heading">¶</a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="slideflow.ModelParams">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">ModelParams</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'CrossEntropy'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/slideflow/model/torch/#ModelParams"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#slideflow.ModelParams" title="Permalink to this definition">¶</a></dt>
<dd><p>Build a set of hyperparameters.</p>
<p>Configure a set of training parameters via keyword arguments.</p>
<p>Parameters are configured in the context of the current deep learning
backend (Tensorflow or PyTorch), which can be viewed with
<code class="xref py py-func docutils literal notranslate"><span class="pre">slideflow.backend()</span></code>. While most model parameters are
cross-compatible between Tensorflow and PyTorch, some parameters are
unique to a backend, so this object should be configured in the same
backend that the model will be trained in.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tile_px</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Tile width in pixels. Defaults to 299.</p></li>
<li><p><strong>tile_um</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Tile width in microns (int) or
magnification (str, e.g. “20x”). Defaults to 302.</p></li>
<li><p><strong>epochs</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 epochs to train the full model. Defaults to 3.</p></li>
<li><p><strong>toplayer_epochs</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 epochs to only train the fully-connected layers. Defaults to 0.</p></li>
<li><p><strong>model</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Base model architecture name. Defaults to ‘xception’.</p></li>
<li><p><strong>pooling</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Post-convolution pooling. ‘max’, ‘avg’, or ‘none’. Defaults to ‘max’.</p></li>
<li><p><strong>loss</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Loss function. Defaults to ‘sparse_categorical_crossentropy’.</p></li>
<li><p><strong>learning_rate</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Learning rate. Defaults to 0.0001.</p></li>
<li><p><strong>learning_rate_decay</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Learning rate decay rate. Defaults to 0.</p></li>
<li><p><strong>learning_rate_decay_steps</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Learning rate decay steps. Defaults to 100000.</p></li>
<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Batch size. Defaults to 16.</p></li>
<li><p><strong>hidden_layers</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 fully-connected hidden layers after core model. Defaults to 0.</p></li>
<li><p><strong>hidden_layer_width</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Width of fully-connected hidden layers. Defaults to 500.</p></li>
<li><p><strong>optimizer</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 of optimizer. Defaults to ‘Adam’.</p></li>
<li><p><strong>early_stop</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Use early stopping. Defaults to False.</p></li>
<li><p><strong>early_stop_patience</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Patience for early stopping, in epochs. Defaults to 0.</p></li>
<li><p><strong>early_stop_method</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Metric to monitor for early stopping. Defaults to ‘loss’.</p></li>
<li><p><strong>manual_early_stop_epoch</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – Manually override early stopping to occur at this epoch/batch.
Defaults to None.</p></li>
<li><p><strong>manual_early_stop_batch</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – Manually override early stopping to occur at this epoch/batch.
Defaults to None.</p></li>
<li><p><strong>training_balance</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>optional</em>) – Type of batch-level balancing to use during training.
Options include ‘tile’, ‘category’, ‘patient’, ‘slide’, and None. Defaults to ‘category’ if a
classification loss is provided, and ‘patient’ if a regression loss is provided.</p></li>
<li><p><strong>validation_balance</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>optional</em>) – Type of batch-level balancing to use during validation.
Options include ‘tile’, ‘category’, ‘patient’, ‘slide’, and None. Defaults to ‘none’.</p></li>
<li><p><strong>trainable_layers</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 layers which are traininable. If 0, trains all layers.
Defaults to 0.</p></li>
<li><p><strong>l1</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – L1 regularization weight. Defaults to 0.</p></li>
<li><p><strong>l2</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – L2 regularization weight. Defaults to 0.</p></li>
<li><p><strong>l1_dense</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – L1 regularization weight for Dense layers. Defaults to the value of l1.</p></li>
<li><p><strong>l2_dense</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – L2 regularization weight for Dense layers. Defaults to the value of l2.</p></li>
<li><p><strong>dropout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – Post-convolution dropout rate. Defaults to 0.</p></li>
<li><p><strong>uq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – Use uncertainty quantification with dropout. Requires dropout &gt; 0. Defaults to False.</p></li>
<li><p><strong>augment</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>optional</em>) – <p>Image augmentations to perform. Characters in the string designate augmentations.
Combine these characters to define the augmentation pipeline. For example, ‘xyrj’ will perform x-flip,
y-flip, rotation, and JPEG compression. True will use all augmentations. Defaults to ‘xyrj’.</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Character</p></th>
<th class="head"><p>Augmentation</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>x</p></td>
<td><p>Random x-flipping</p></td>
</tr>
<tr class="row-odd"><td><p>y</p></td>
<td><p>Random y-flipping</p></td>
</tr>
<tr class="row-even"><td><p>r</p></td>
<td><p>Random cardinal rotation</p></td>
</tr>
<tr class="row-odd"><td><p>j</p></td>
<td><p>Random JPEG compression (10% chance to JPEG compress with quality between 50-100%)</p></td>
</tr>
<tr class="row-even"><td><p>b</p></td>
<td><p>Random Guassian blur (50% chance to blur with sigma between 0.5 - 2.0)</p></td>
</tr>
<tr class="row-odd"><td><p>n</p></td>
<td><p><a class="reference internal" href="../norm/#stain-augmentation"><span class="std std-ref">Stain Augmentation</span></a> (requires stain normalizer)</p></td>
</tr>
</tbody>
</table>
</p></li>
<li><p><strong>normalizer</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>optional</em>) – Normalization strategy to use on image tiles. Defaults to None.</p></li>
<li><p><strong>normalizer_source</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>optional</em>) – Stain normalization preset or
path to a source image. Valid presets include ‘v1’, ‘v2’, and
‘v3’. If None, will use the default present (‘v3’).
Defaults to None.</p></li>
<li><p><strong>include_top</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Include post-convolution fully-connected layers from the core model. Defaults
to True. include_top=False is not currently compatible with the PyTorch backend.</p></li>
<li><p><strong>drop_images</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Drop images, using only other slide-level features as input.
Defaults to False.</p></li>
</ul>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="slideflow.ModelParams.to_dict">
<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Dict" title="(in Python v3.12)"><span class="pre">Dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#slideflow.ModelParams.to_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>Return a dictionary of configured parameters.</p>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="slideflow.ModelParams.get_normalizer">
<span class="sig-name descname"><span class="pre">get_normalizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../norm/#slideflow.norm.StainNormalizer" title="slideflow.norm.StainNormalizer"><span class="pre">StainNormalizer</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><a class="headerlink" href="#slideflow.ModelParams.get_normalizer" title="Permalink to this definition">¶</a></dt>
<dd><p>Return a configured <code class="xref py py-class docutils literal notranslate"><span class="pre">slideflow.StainNormalizer</span></code>.</p>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="slideflow.ModelParams.validate">
<span class="sig-name descname"><span class="pre">validate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><a class="headerlink" href="#slideflow.ModelParams.validate" title="Permalink to this definition">¶</a></dt>
<dd><p>Check that hyperparameter combinations are valid.</p>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="slideflow.ModelParams.model_type">
<span class="sig-name descname"><span class="pre">model_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></span><a class="headerlink" href="#slideflow.ModelParams.model_type" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns ‘regression’, ‘classification’, or ‘survival’, reflecting the loss.</p>
</dd></dl>

</section>
<section id="mini-batch-balancing">
<h2>Mini-batch balancing<a class="headerlink" href="#mini-batch-balancing" title="Permalink to this heading">¶</a></h2>
<p>During training, mini-batch balancing can be customized to assist with increasing representation of sparse outcomes or small slides. Five mini-batch balancing methods are available when configuring <a class="reference internal" href="#slideflow.ModelParams" title="slideflow.ModelParams"><code class="xref py py-class docutils literal notranslate"><span class="pre">slideflow.ModelParams</span></code></a>, set through the parameters <code class="docutils literal notranslate"><span class="pre">training_balance</span></code> and <code class="docutils literal notranslate"><span class="pre">validation_balance</span></code>. These are <code class="docutils literal notranslate"><span class="pre">'tile'</span></code>, <code class="docutils literal notranslate"><span class="pre">'category'</span></code>, <code class="docutils literal notranslate"><span class="pre">'patient'</span></code>, <code class="docutils literal notranslate"><span class="pre">'slide'</span></code>, and <code class="docutils literal notranslate"><span class="pre">'none'</span></code>.</p>
<p>If <strong>tile-level balancing</strong> (“tile”) is used, tiles will be selected randomly from the population of all extracted tiles.</p>
<p>If <strong>slide-based balancing</strong> (“patient”) is used, batches will contain equal representation of images from each slide.</p>
<p>If <strong>patient-based balancing</strong> (“patient”) is used, batches will balance image tiles across patients. The balancing is similar to slide-based balancing, except across patients (as each patient may have more than one slide).</p>
<p>If <strong>category-based balancing</strong> (“category”) is used, batches will contain equal representation from each outcome category.</p>
<p>If <strong>no balancing</strong> is performed, batches will be assembled by randomly selecting from TFRecords. This is equivalent to slide-based balancing if each slide has its own TFRecord (default behavior).</p>
<p>See <a class="reference internal" href="../dataloaders/#balancing"><span class="std std-ref">Oversampling with balancing</span></a> for more discussion on sampling and mini-batch balancing.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>If you are <a class="reference internal" href="../training/#training-with-trainer"><span class="std std-ref">using a Trainer</span></a> to train your models, you can further customize the mini-batch balancing strategy by using <a class="reference internal" href="../dataset/#slideflow.Dataset.balance" title="slideflow.Dataset.balance"><code class="xref py py-meth docutils literal notranslate"><span class="pre">slideflow.Dataset.balance()</span></code></a> on your training and/or validation datasets.</p>
</div>
</section>
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<li><a class="reference internal" href="#">slideflow.ModelParams</a><ul>
<li><a class="reference internal" href="#modelparams">ModelParams</a><ul>
<li><a class="reference internal" href="#slideflow.ModelParams"><code class="docutils literal notranslate"><span class="pre">ModelParams</span></code></a></li>
<li><a class="reference internal" href="#slideflow.ModelParams.to_dict"><code class="docutils literal notranslate"><span class="pre">to_dict()</span></code></a></li>
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