Advanced Deep Learning with Keras

Deep Neural Networks
Transition Layer
(2)
Residual Block
(3)
16 × 16 { 1× 1 Conv2 D, strides = 2}
8 × 8
8 × 8 64 ⎧3× 3 Conv2 Dstrides , = 2if 1st Conv2D⎫ ⎨
⎬ × n
⎩3×
3 Conv2D
⎭
Average Pooling 1 × 1 8×
8 AveragePooling2D
Table 2.2.1: ResNet network architecture configuration
Figure 2.2.4: The model architecture of ResNet for the CIFAR10 dataset classification
# Layers n % Accuracy on CIFAR10
(Original paper)
ResNet20 3 91.25 92.16
ResNet32 5 92.49 92.46
ResNet44 7 92.83 92.50
ResNet56 9 93.03 92.71
ResNet110 18 93.57 92.65
Table 2.2.2: ResNet architectures validated with CIFAR10
[ 56 ]
% Accuracy on CIFAR10
(This book)
The following listing shows the partial code of resnet-cifar10-2.2.1.py,
which is the Keras model implementation of ResNet v1:
def resnet_v1(input_shape, depth, num_classes=10):