Advanced Deep Learning with Keras

Deep Neural Networks
…
if version == 2:
model = resnet_v2(input_shape=input_shape, depth=depth)
else:
model = resnet_v1(input_shape=input_shape, depth=depth)
The accuracy of ResNet v2 is shown in following table:
# Layers n % Accuracy on CIFAR10
(Original paper)
ResNet56 9 NA 93.01
ResNet110 18 93.63 93.15
% Accuracy on CIFAR10
(This book)
Table 2.3.1: The ResNet v2 architectures validated on the CIFAR10 dataset
In the Keras applications package, ResNet50 has been implemented as well with
the corresponding checkpoint for reuse. This is an alternative implementation but
tied to the 50-layer ResNet v1.
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.
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