The figure shows the architecture of the famous LeNet-5 convolutional network. Calculate the number of trainable parameters and the number of connections (synaptic weights plus biases, i.e. in augmented space). Cx: Convolutional layer x, Sx: Subsampling layer x, Fx: Fully connected layer x C3t maps 16910x10 NPUT 3232 C1:feature maps 602828 S4.L maps 16@55 S2 1 014x14 20 10 84 Ful Gaussian connections Convolutions Subsamp Full connection The figure shows the architecture of the famous LeNet-5 convolutional network. Calculate the number of trainable parameters and the number of connections (synaptic weights plus biases, i.e. in augmented space). Cx: Convolutional layer x, Sx: Subsampling layer x, Fx: Fully connected layer x C3t maps 16910x10 NPUT 3232 C1:feature maps 602828 S4.L maps 16@55 S2 1 014x14 20 10 84 Ful Gaussian connections Convolutions Subsamp Full connection