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Keras Documentation
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<li class="toctree-l1"><a class="reference internal" href="../why-use-keras/">Why use Keras</a>
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<p class="caption"><span class="caption-text">Getting started</span></p>
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<h1 id="wrappers-for-the-scikit-learn-api">Wrappers for the Scikit-Learn API</h1>
<p>You can use <code>Sequential</code> Keras models (single-input only) as part of your Scikit-Learn workflow via the wrappers found at <code>keras.wrappers.scikit_learn.py</code>.</p>
<p>There are two wrappers available:</p>
<p><code>keras.wrappers.scikit_learn.KerasClassifier(build_fn=None, **sk_params)</code>, which implements the Scikit-Learn classifier interface,</p>
<p><code>keras.wrappers.scikit_learn.KerasRegressor(build_fn=None, **sk_params)</code>, which implements the Scikit-Learn regressor interface.</p>
<h3 id="arguments">Arguments</h3>
<ul>
<li><strong>build_fn</strong>: callable function or class instance</li>
<li><strong>sk_params</strong>: model parameters & fitting parameters</li>
</ul>
<p><code>build_fn</code> should construct, compile and return a Keras model, which
will then be used to fit/predict. One of the following
three values could be passed to <code>build_fn</code>:</p>
<ol>
<li>A function</li>
<li>An instance of a class that implements the <code>__call__</code> method</li>
<li>None. This means you implement a class that inherits from either
<code>KerasClassifier</code> or <code>KerasRegressor</code>. The <code>__call__</code> method of the
present class will then be treated as the default <code>build_fn</code>.</li>
</ol>
<p><code>sk_params</code> takes both model parameters and fitting parameters. Legal model
parameters are the arguments of <code>build_fn</code>. Note that like all other
estimators in scikit-learn, <code>build_fn</code> should provide default values for
its arguments, so that you could create the estimator without passing any
values to <code>sk_params</code>.</p>
<p><code>sk_params</code> could also accept parameters for calling <code>fit</code>, <code>predict</code>,
<code>predict_proba</code>, and <code>score</code> methods (e.g., <code>epochs</code>, <code>batch_size</code>).
fitting (predicting) parameters are selected in the following order:</p>
<ol>
<li>Values passed to the dictionary arguments of
<code>fit</code>, <code>predict</code>, <code>predict_proba</code>, and <code>score</code> methods</li>
<li>Values passed to <code>sk_params</code></li>
<li>The default values of the <code>keras.models.Sequential</code>
<code>fit</code>, <code>predict</code>, <code>predict_proba</code> and <code>score</code> methods</li>
</ol>
<p>When using scikit-learn's <code>grid_search</code> API, legal tunable parameters are
those you could pass to <code>sk_params</code>, including fitting parameters.
In other words, you could use <code>grid_search</code> to search for the best
<code>batch_size</code> or <code>epochs</code> as well as the model parameters.</p>
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