https://colab.research.google.com/github/CleanPegasus/Traffic-Sign-Classifier/blob/master/Traffic_Sign_Classifier.ipynb#scrollTo=fMJ_3RR5HEN9

what does each part mean and do within the code above. This is a CNN classifier.

def neural model): model sequential ( ) model .add (Conv2D(60, (5, 5), input-shape- model .add (Conv2D(60, (5, 5), input-shape model.add (MaxPooling2D(pool-size = (2,2))) (32, (32, 32, 32, 1), 1), activation activation 'relu')) 'relu') model.add (Conv2D (30, (3, 3), activation 'relu')) model.add (Conv2D (30, (3, 3), activation'relu')) model.add(MaxPoolina2D(pool-size = (2, 2))) #model . add ( Dropout ( 0 . 5 ) ) model.add (Flatten()) model .add(Dense (500, activation = 'relu.)) model.add (Dropout (0.5)) model.add (Dense (num classes, activation - softmax")) model .compile (Adam (Ir 0.001), loss = 'categorical-crossentropy. return model metrics-: [' accuracy'])