A Sequential model in Keras is a linear stack of neural network layers, in which the output of one layer is passed as input to the next layer. The Sequential model is the simplest and most common way of building a deep learning model in Keras.
To initialize a Sequential model in Keras, you can use the ‘Sequential()‘ function, which creates an empty Sequential model. Here is an example:
from tensorflow.keras.models import Sequential
model = Sequential()Once you have created the Sequential model, you can add layers to it using the ‘add()‘ method. For example, to add a Dense layer with 64 units and a ReLU activation function, you can do:
from tensorflow.keras.layers import Dense
model.add(Dense(64, activation='relu'))You can add as many layers as you want to the Sequential model, and customize the number of units, activation functions, regularization techniques, and other hyperparameters to improve the model’s performance on your specific task.
The purpose of initializing a Sequential model is to define the architecture and the parameters of the neural network, and to prepare it for training and prediction. By adding layers to the Sequential model, you are defining the sequence in which the data will be transformed by the network, and allowing it to learn the features and patterns in the data that are relevant to the task at hand. Once you have built the Sequential model, you can compile it with an optimizer, a loss function, and evaluation metrics, and then train it on the training data by calling the ‘fit()‘ method. Finally, you can use the trained model to make predictions on new data by calling the ‘predict()‘ method.