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Chapter 5grace_hopper = np.array(grace_hopper)/255.0result = classifier.predict(grace_hopper[np.newaxis, ...])predicted_class = np.argmax(result[0], axis=-1)print (predicted_class)Pretty simple indeed. Just remember to use hub.KerasLayer() for wrapping anyHub layer. In this section, we have discussed how to use TensorFlow Hub. Next,we will focus on other CNN architectures.Other CNN architecturesIn this section we will discuss many other different CNN architectures includingAlexNet, residual networks, HighwayNets, DenseNets, and Xception.AlexNetOne of the first convolutional networks was AlexNet [4], which consisted of onlyeight layers; the first five were convolutional ones with max-pooling layers, and thelast three were fully connected. AlexNet [4] is an article cited more than 35,000 times,which started the deep learning revolution (for computer vision). Then, networksstarted to become deeper and deeper. Recently, a new idea has been proposed.Residual networksResidual networks (ResNets) are based on the interesting idea of allowing earlierlayers to be fed directly into deeper layers. These are the so-called skip connections(or fast-forward connections). The key idea is to minimize the risk of vanishing orexploding gradients for deep networks (see Chapter 9, Autoencoders). The buildingblock of a ResNet is called "residual block" or "identity block," which includes bothforward and fast-forward connections.In this example (Figure 20) the output of an earlier layer is added to the output ofa later layer before being sent into a ReLU activation function:Figure 20: An example of Image Segmentation[ 159 ]

Advanced Convolutional Neural NetworksHighwayNets and DenseNetsAn additional weight matrix may be used to learn the skip weights and these modelsare frequently denoted as HighwayNets. Instead, models with several parallelskips are known as DenseNets [5]. It has been noted that the human brain mighthave similar patterns to residual networks since the cortical layer VI neurons getinput from layer I, skipping intermediary layers. In addition, residual networkscan be faster to train since there are fewer layers to propagate through duringeach iteration (deeper layers get input sooner due to the skip connection). Thefollowing is an example of DenseNets (Figure 21, as shown in http://arxiv.org/abs/1608.06993):Figure 21: An example of DenseNetsXceptionXception networks use two basic blocks: a depthwise convolution and a pointwiseconvolution. A depthwise convolution is the channel-wise n × n spatial convolution.Suppose an image has three channels, then we have three convolutions of n × n.A pointwise convolution is a 1×1 convolution. In Xception – an "extreme" versionof an Inception module – we first use a 1×1 convolution to map cross-channelcorrelations, and then separately map the spatial correlations of every outputchannel as shown in Figure 22 (from https://arxiv.org/pdf/1610.02357.pdf):[ 160 ]

Advanced Convolutional Neural Networks

HighwayNets and DenseNets

An additional weight matrix may be used to learn the skip weights and these models

are frequently denoted as HighwayNets. Instead, models with several parallel

skips are known as DenseNets [5]. It has been noted that the human brain might

have similar patterns to residual networks since the cortical layer VI neurons get

input from layer I, skipping intermediary layers. In addition, residual networks

can be faster to train since there are fewer layers to propagate through during

each iteration (deeper layers get input sooner due to the skip connection). The

following is an example of DenseNets (Figure 21, as shown in http://arxiv.org/

abs/1608.06993):

Figure 21: An example of DenseNets

Xception

Xception networks use two basic blocks: a depthwise convolution and a pointwise

convolution. A depthwise convolution is the channel-wise n × n spatial convolution.

Suppose an image has three channels, then we have three convolutions of n × n.

A pointwise convolution is a 1×1 convolution. In Xception – an "extreme" version

of an Inception module – we first use a 1×1 convolution to map cross-channel

correlations, and then separately map the spatial correlations of every output

channel as shown in Figure 22 (from https://arxiv.org/pdf/1610.02357.pdf):

[ 160 ]

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