Advanced Deep Learning with Keras

Chapter 2
The following listing shows the partial ResNet implementation within Keras. The
code has been contributed to the Keras GitHub repository. From Table 2.2.2 we can
also see that by modifying the value of n, we're able to increase the depth of the
networks. For example, for n = 18, we already have ResNet110, a deep network
with 110 layers. To build ResNet20, we use n = 3:
n = 3
# model version
# orig paper: version = 1 (ResNet v1),
# Improved ResNet: version = 2 (ResNet v2)
version = 1
# computed depth from supplied model parameter n
if version == 1:
depth = n * 6 + 2
elif version == 2:
depth = n * 9 + 2
…
if version == 2:
model = resnet_v2(input_shape=input_shape, depth=depth)
else:
model = resnet_v1(input_shape=input_shape, depth=depth)
The resnet_v1() method is a model builder for ResNet. It uses a utility function,
resnet_layer() to help build the stack of Conv2D-BN-ReLU.
It's referred to as version 1, as we will see in the next section, an improved ResNet
was proposed, and that has been called ResNet version 2, or v2. Over ResNet,
ResNet v2 has an improved residual block design resulting in better performance.
Layers
Output
Size
Filter
Size
Operations
Convolution 32 × 32 16 3×
3 Conv2D
Residual Block 32 × 32 ⎧3×
3 Conv2D⎫ ⎨
⎬ × n
(1)
⎩3×
3 Conv2D⎭
Transition Layer 32 × 32 { 1× 1 Conv2 D, strides = 2}
(1)
16 × 16
Residual Block 16 × 16 32 ⎧3× 3 Conv2 Dstrides , = 2if 1st Conv2D⎫ ⎨
⎬ × n
(2)
⎩3×
3 Conv2D
⎭
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