Deep Learning with TensorFlow

Building deep learning models

The core data structure of Keras is a model, which is a way to organize layers. There are two types of model:

• Sequential: The main type of model. It is simply a linear stack of layers.
• Keras functional API: These are used for more complex architectures.

You define a sequential model as follows:

from keras.models import Sequentialmodel = Sequential()

Once a model is defined, you can add one or more layers. The stacking operation is provided by the add() statement:

from keras.layers import Dense, Activation

For example, add a first fully connected NN layer and the Activation function:

model.add(Dense(output_dim=64, input_dim=100))model.add(Activation("relu"))

Then add a second softmax layer:

 model.add(Dense(output_dim=10)) ...