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
model.add(Activation('relu'))

Keras will automatically infer the shape of all layers after the first layer. This means you only have to set the input dimensions for the first layer. The first layer from the preceding code snippet, model.add(Flatten(input_shape=(32, 32, 3))), sets the input dimension to (32, 32, 3) and the output dimension to (3072=32 x 32 x 3). The second layer takes in the output of the first layer and sets the output dimensions to (100). This chain of passing the output to the next layer continues until the last layer, which is the output of the model.