October 2018
Intermediate to advanced
368 pages
9h 20m
English
Content preview from Advanced Deep Learning with Keras
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Densely connected convolutional networks (DenseNet)
Figure 2.4.1: A 4-layer Dense block in DenseNet. The input to each layer is made of all the previous feature maps.
DenseNet attacks the problem of vanishing gradient using a different approach. Instead of using shortcut connections, all the previous feature maps will become the input of the next layer. The preceding figure, shows an example of a dense interconnection in one Dense block.
For simplicity, in this figure, we'll only show four layers. Notice that the input to layer l is the concatenation of all previous feature maps. If we designate the BN-ReLU-Conv2D as the operation H(x), then the output ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.