A Keras layer is just like a neural network layer. There are fully connected layers, max pool layers, and activation layers. A layer can be added to the model using the model's add() function. For example, a simple model can be represented by the following:
from keras.models import Sequential
from keras.layers.core import Dense, Activation, Flatten
#Creating the Sequential model
model = Sequential()
#Layer 1 - Adding a flatten layer
model.add(Flatten(input_shape=(32, 32, 3)))
#Layer 2 - Adding a fully connected layer
model.add(Dense(100))
#Layer 3 - Adding a ReLU activation layer
model.add(Activation('relu'))
#Layer 4- Adding a fully connected layer
model.add(Dense(60))
#Layer 5 - Adding an ReLU activation layer ...
