LeNet5
The LeNet5 CNN architecture was invented by Yann LeCun in 1998 and was the first CNN. It is a multilayered feed-forward network specifically designed to classify handwritten digits. It was used in LeCun's experiments and consists of seven layers containing trainable weights. The LeNet5 architecture looks like this:
Figure 6: The LeNet5 network
The LeNet5 architecture consists of three convolutional layers and two alternating sequence pooling layers. The last two layers correspond to a traditional fully connected neural network, that is, a fully connected layer followed by an output layer. The main function of the output layer is to calculate the Euclidean distance between the input vector and the parameter vector. The output functions identify the difference between the measurements of the input pattern and our model. The output is kept minimal in order to achieve the best model. Therefore, the fully connected layer is configured so that the difference between the measurements...
