<p dir="auto"><a class="user-mention notranslate" data-hovercard-type="user" data-hovercard-url="/users/fchollet/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" href="https://github.com/fchollet">@fchollet</a> and other kerasors,<br>
We all know that if we are dealing with large scale of data such as ImageNet, we could write a customized generator which produces batch data (often as numpy.array) from disk. Then we could train our model with <code class="notranslate">model.fit_generator()</code>. But, if we want to use <code class="notranslate">ImageDataGenerator</code> to do the online data augmentation at the same time, what is the simplest way to implement? Note that I would like to use its <code class="notranslate">flow()</code> method instead of <code class="notranslate">flow_from_directory()</code> method.</p>
<p dir="auto">Hello everyone, I have a tricky programming question. I want to implement an exponentially moving average of centroids on hidden layers inputs activations of an AutoEncoder to maximize ICC over several class in keras<br>
(adding a loss penalizing this term).<br>
It would require to know the final output of the classification for calculating the centroids. I could do it by adding the output of this layer as an extra output of neural network. But it would duplicate data. And it won t be nice to work with in the case if I haven t to apply this on any hidden layers.<br>
Do you have any ideas how to do so ? Thank you</p>