Hands-On Machine Learning on Google Cloud Platform

Sequential model

The Sequential model is a linear stack of layers. We can create a Sequential model by passing a list of layer instances to the constructor as follows:

from keras.models import Sequentialfrom keras.layers import Dense, Activationmodel = Sequential([    Dense(32, input_shape=(784,)),    Activation('relu'),    Dense(10),    Activation('softmax'),])

We can also simply add layers via the .add() method:

model = Sequential()model.add(Dense(32, input_dim=784))model.add(Activation('relu'))

This type of model needs to know what input shape it should expect. For this reason, the first layer in a Sequential model needs to receive information about its input shape. There are several possible ways to do this:

• Pass an input_shape argument to the first ...