Computational Analysis and Deep Learning for Medical Care. Группа авторов

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СКАЧАТЬ tanh 32 2,000 6 Dense 120 1 × 1 5 × 5 1 tanh 48,120 48,120 7 Dense - 84 - - tanh 10,164 10,164 8 Dense - 10 - - softmax - - 60,000 (Total)

      (1.6)

      The number of feature map is 14×14 and the number of learning parameters is (coefficient + bias) × no. filters = (1+1) × 6 = 12 parameters and the number of connections = 30×14×14 = 5,880.

Layer 3: In this layer, only 10 out of 16 feature maps are connected to six feature maps of the previous layer as shown in Table 1.2. Each unit in C3 is connected to several 5 × 5 receptive fields at identical locations in S2. Total number of trainable parameters = (3×5×5+1)×6+(4×5×5+1)×9+(6×5×5+1) = 1516. Total number of connections = (3×5×5+1)×6×10×10+(4×5×5+1) ×9×10×10 +(6×5×5+1)×10×10 = 151,600. Total number of parameters is 60K.

      1.2.2 AlexNet

Alex Krizhevsky et al. [2] presented a new architecture “AlexNet” to train the ImageNet dataset, which consists of 1.2 million high-resolution images, into 1,000 different classes. In the original implementation, layers are divided into two and to train them on separate GPUs (GTX 580 3GB GPUs) takes around 5–6 days. The network contains five convolutional layers, maximum pooling layers and it is followed by three fully connected layers, and finally a 1,000-way softmax classifier. The network uses ReLU activation function, data augmentation, dropout and smart optimizer layers, local response normalization, and overlapping pooling. The AlexNet has 60M parameters. Figure 1.2 shows the architecture of AlexNet and Table 1.3 shows the various parameters of AlexNet.

Table 1.2 Every column indicates which feature map in S2 are combined by the units in a particular feature map of C3 [1].

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