Basic layers of CNN
A CNN is composed of a sequence of layers, where every layer of the network goes through a differentiable function to transform itself from one volume of activation to another. Four main types of layers are used to build a CNN: Convolutional layer, Rectified Linear Units layer, Pooling layer, and Fully-connected layer. All these layers are stacked together to form a full CNN.
A regular CNN could have the following architecture:
[INPUT - CONV - RELU - POOL - FC]
However, in a deep CNN, there are generally more layers interspersed between these five basic layers.
A classic deep neural network will have the following structure:
Input -> Conv->ReLU->Conv->ReLu->Pooling->ReLU->Conv->ReLu->Pooling->Fully Connected
AlexNet, as mentioned in the earlier section, can be taken as a perfect example for this kind of structure. The architecture of AlexNet is shown in Figure 3.4. After every layer, an implicit ReLU non-linearity has been added. We will explain...
