In TensorFlow’s Keras, there are two main ways to define a neural network architecture: the Sequential API and the Functional API.
The Sequential API is a simpler, more straightforward way to define a linear stack of layers for a neural network. It allows for the creation of a sequential model by simply adding layers one by one. For example:
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation
model = Sequential([
Dense(32, input_shape=(784,)),
Activation('relu'),
Dense(10),
Activation('softmax')
])The Functional API, on the other hand, allows for a more complex, non-linear architecture of a neural network, where the layers can be connected in any way. It is a way to define a directed acyclic graph of layers that can have multiple inputs and outputs. For example:
from tensorflow.keras.layers import Input, Dense, Activation
from tensorflow.keras.models import Model
inputs = Input(shape=(784,))
x = Dense(32)(inputs)
x = Activation('relu')(x)
predictions = Dense(10, activation='softmax')(x)
model = Model(inputs=inputs, outputs=predictions)The Functional API allows for greater flexibility and control over the architecture of a neural network. It also enables the creation of more complex models that may have multiple inputs or outputs, shared layers, or loops. The Functional API can be used for a wider range of problems, including image segmentation, object detection, and natural language processing.
In summary, the Sequential API is a simpler, more straightforward way to define a linear stack of layers for a neural network, while the Functional API allows for greater flexibility and control over the architecture of a neural network, including the ability to define directed acyclic graphs of layers. The choice between the two APIs depends on the complexity of the problem being solved and the desired architecture of the neural network.