Sequential Model
Learn how a neural network is built in Keras.
We'll cover the following
Chapter Goals:
- Initialize an MLP model in Keras
A. Building the MLP
In Keras, every neural network model is an instance of the Sequential object. This acts as the container of the neural network, allowing us to build the model by stacking multiple layers inside the Sequential object.
The most commonly used Keras neural network layer is the Dense layer. This represents a fully-connected layer in the neural network, and it is the most important building block of an MLP model.
When building a model, we start off by initializing a Sequential object. We can initialize an empty Sequential object and add layers onto the model using the add function, or we can directly initialize the Sequential object with a list of layers.
model = Sequential()layer1 = Dense(5, input_dim=4)model.add(layer1)layer2 = Dense(3, activation='relu')model.add(layer2)
layer1 = Dense(5, input_dim=4)layer2 = Dense(3, activation='relu')model = Sequential([layer1, layer2])
The Dense object takes in a single required argument, which is the number of neurons in the fully-connected layer. The activation keyword argument specifies the activation function for the layer (the default is no activation). In the code snippets above, we used no activation for layer1 and ReLU activation for layer2.
The first layer of the Sequential model represents the input layer. Therefore, in the first layer we need to specify the feature dimension of the input data for the model, which we do with the input_dim keyword argument.
In the code snippets above, we set the input feature dimension to 4, meaning that the input data has shape (batch_size, 4) (where batch_size is the data's batch size, decided at runtime).
Time to code!
The coding exercise for this chapter involves setting up a Keras Sequential model with a single Dense layer. We start off with an empty initialized Sequential object.
Set model equal to Sequential initialized with no arguments.
# CODE HERE
We'll build a three layer MLP model. The first layer will consist of 5 neurons and use ReLU activation. It will also act as the input layer for the model.
To create the input layer, we'll initialize a Dense object with the requisite number of neurons and activation. We'll also set the input_dim keyword argument to 2, which represents the feature dimension of the input data for the model.
Set layer1 equal to a Dense with 5 as the required argument, ‘relu’ for the activation keyword argument, and 2 for the input_dim keyword argument.
Then call model.add on layer1.
# CODE HERE
Get hands-on with 1300+ tech skills courses.