tensorflow.keras.sequential.add

The `add()` method in `tensorflow.keras.Sequential` class is used to add layers to the neural network model sequentially. Each layer is added one after the other in the order they are specified.

For example, to create a simple neural network with two hidden layers, we can use the `add()` method to add each layer to the model:

import tensorflow as tf

model = tf.keras.Sequential()

# Adding the first hidden layer with 64 units and ReLU activation function
model.add(tf.keras.layers.Dense(64, activation='relu'))

# Adding the second hidden layer with 32 units and ReLU activation function
model.add(tf.keras.layers.Dense(32, activation='relu'))

# Adding the output layer with 10 units and softmax activation function
model.add(tf.keras.layers.Dense(10, activation='softmax'))

In this example, we first create an empty `Sequential` model and then use the `add()` method to add three layers to it. The first two layers are hidden layers with ReLU activation functions, and the last layer is the output layer with softmax activation function.