<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"
	xmlns:content="http://purl.org/rss/1.0/modules/content/"
	xmlns:wfw="http://wellformedweb.org/CommentAPI/"
	xmlns:dc="http://purl.org/dc/elements/1.1/"
	xmlns:atom="http://www.w3.org/2005/Atom"
	xmlns:sy="http://purl.org/rss/1.0/modules/syndication/"
	xmlns:slash="http://purl.org/rss/1.0/modules/slash/"
	>

<channel>
	<title>model Archives - Petamind</title>
	<atom:link href="https://petaminds.com/tag/model/feed/" rel="self" type="application/rss+xml" />
	<link>https://petaminds.com/tag/model/</link>
	<description>A.I, Data and Software Engineering</description>
	<lastBuildDate>Tue, 05 Oct 2021 06:08:01 +0000</lastBuildDate>
	<language>en-US</language>
	<sy:updatePeriod>
	hourly	</sy:updatePeriod>
	<sy:updateFrequency>
	1	</sy:updateFrequency>
	<generator>https://wordpress.org/?v=6.9.4</generator>

<image>
	<url>https://petaminds.com/wp-content/uploads/2019/09/ic_launcher.png</url>
	<title>model Archives - Petamind</title>
	<link>https://petaminds.com/tag/model/</link>
	<width>32</width>
	<height>32</height>
</image> 
	<item>
		<title>Continue training big models on less powerful devices</title>
		<link>https://petaminds.com/continue-training-big-models-on-less-powerful-devices/</link>
					<comments>https://petaminds.com/continue-training-big-models-on-less-powerful-devices/#respond</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Sun, 22 Mar 2020 00:51:57 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[check-point]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[keras]]></category>
		<category><![CDATA[model]]></category>
		<category><![CDATA[out of memory]]></category>
		<category><![CDATA[save]]></category>
		<category><![CDATA[tensorflow]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=2338</guid>

					<description><![CDATA[<p>It would not be a surprise that you may not have a powerful expensive machine to train a complicate model. You may experience the problem of not enough memory during training in some epoch. This article demonstrates a simple workaround for this. The problem Training deep learning models requires a lot of computing power. For [&#8230;]</p>
<p>The post <a href="https://petaminds.com/continue-training-big-models-on-less-powerful-devices/">Continue training big models on less powerful devices</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
					<wfw:commentRss>https://petaminds.com/continue-training-big-models-on-less-powerful-devices/feed/</wfw:commentRss>
			<slash:comments>0</slash:comments>
		
		
			</item>
		<item>
		<title>Save, restore, visualise Graph with TensorFlow v2.0 &#038; KERAS</title>
		<link>https://petaminds.com/save-restore-visualise-graph-with-tensorflow-v2-0-keras/</link>
					<comments>https://petaminds.com/save-restore-visualise-graph-with-tensorflow-v2-0-keras/#comments</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Tue, 08 Oct 2019 12:36:30 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[front-end]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[keras]]></category>
		<category><![CDATA[MNIST]]></category>
		<category><![CDATA[model]]></category>
		<category><![CDATA[restore]]></category>
		<category><![CDATA[save]]></category>
		<category><![CDATA[tensor]]></category>
		<category><![CDATA[tensorboard]]></category>
		<category><![CDATA[tensorflow]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=1031</guid>

					<description><![CDATA[<p>TensorFlow 2.0 is coming really soon. Therefore, we quickly show some useful features, i.e., save and load a pre-trained model, with v.2 syntax. To make it more intuitive, we will also visualise the graph of the neural network model. Benefits of saving a model Quick answer: to save time, easy-share, and fast deploy. A SavedModel [&#8230;]</p>
<p>The post <a href="https://petaminds.com/save-restore-visualise-graph-with-tensorflow-v2-0-keras/">Save, restore, visualise Graph with TensorFlow v2.0 &#038; KERAS</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
					<wfw:commentRss>https://petaminds.com/save-restore-visualise-graph-with-tensorflow-v2-0-keras/feed/</wfw:commentRss>
			<slash:comments>2</slash:comments>
		
		
			</item>
	</channel>
</rss>
