Avatto>>DATA SCIENTIST>>SHORT QUESTIONS>>Deep Learning>>Convolution Neural Networks
The LeNet architecture consists of seven layers as given below:

a)Three convolutional layers
b)Two pooling layers
c)One fully connected layer
d)One output layer
CNN is translation-invariant and this makes CNN more prone to misclassification. Say, for instance, we are performing a face recognition task then CNN checks only the presence of facial features such as eyes, nose, mouth, and ears. It will not check whether those features are present in the correct locations. If the images have all those features then it will be classified as the face irrespective of the location of the features. This is one of the major drawbacks of CNN.
The convolutional neural network uses the special operation called convolution which is capable of extracting important features from the image. Since the convolutional operation extracts good features from the image, the accuracy of CNN is high compared to the other algorithms for the image data.