Practical Convolutional Neural Networks

Creating the CNN model

We are going to construct a complex network. However, it has a straightforward architecture. At the beginning, we use Xavier as the network initializer. Once we initialize the network bias using the Xavier initializer. The input layer is followed by a convolutional layer (convolutional layer 1), which is again followed by a BN layer (that is, BN layer 1). Then there is a pooling layer with strides of two and a kernel size of two. Then another BN layer follows the second convolutional layer. Next, there is the second pooling layer with strides of two and kernel size of two. Well, then the max polling layer is followed by a flattening layer that flattens the input from (None, height, width, channels) to (None, height ...