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	<title>custom loss Archives - Petamind</title>
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	<title>custom loss Archives - Petamind</title>
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		<title>Advanced Keras &#8211; Custom loss functions</title>
		<link>https://petaminds.com/advanced-keras-custom-loss-functions/</link>
					<comments>https://petaminds.com/advanced-keras-custom-loss-functions/#comments</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Wed, 23 Mar 2022 00:31:00 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[cost function]]></category>
		<category><![CDATA[custom loss]]></category>
		<category><![CDATA[K]]></category>
		<category><![CDATA[keras]]></category>
		<category><![CDATA[keras backend]]></category>
		<category><![CDATA[loss function]]></category>
		<category><![CDATA[neural network]]></category>
		<category><![CDATA[tensorflow]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=1391</guid>

					<description><![CDATA[<p>When working on machine learning problems, sometimes you want to construct your own custom loss function(s). This article will introduce abstract Keras backend for that purpose. Keras loss functions From Keras loss documentation, there are several built-in loss functions, e.g. mean_absolute_percentage_error, cosine_proximity, kullback_leibler_divergence etc. When compiling a Keras model, we often pass two parameters, i.e. [&#8230;]</p>
<p>The post <a href="https://petaminds.com/advanced-keras-custom-loss-functions/">Advanced Keras &#8211; Custom loss functions</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
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