tensorflow2 dropout

Dropout is a regularization technique used in deep neural networks to prevent overfitting. In TensorFlow 2, dropout can be implemented using the `tf.keras.layers.Dropout` layer.

The `tf.keras.layers.Dropout` layer randomly sets a fraction of the input units to 0 at each update during training time, which helps to prevent the neural network from relying too heavily on any one feature. The dropout rate is a hyperparameter that specifies the fraction of the input units to drop.

Here's an example of how to use the `tf.keras.layers.Dropout` layer in a neural network:

import tensorflow as tf

model = tf.keras.Sequential([
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10)
])

In this example, a dropout layer with a rate of 0.2 is added after the first dense layer. During training, 20% of the input units to the dropout layer will be randomly set to 0 at each update.