layers.Dense

The `layers.Dense` class in Keras is used to create a fully connected neural network layer. It is a basic building block that connects all neurons in the previous layer to all neurons in the current layer.

The `layers.Dense` class has several parameters, including the number of neurons in the layer, the activation function to use, and whether to use bias or not. The number of neurons in the layer is specified using the `units` parameter, and the activation function is specified using the `activation` parameter.

Here is an example of how to create a `layers.Dense` object with 10 neurons and a ReLU activation function:

from tensorflow.keras import layers

dense_layer = layers.Dense(units=10, activation='relu')

Once created, this object can be added to a neural network model using the `add` method:

from tensorflow.keras import models

model = models.Sequential()
model.add(dense_layer)

This will add the `dense_layer` object to the `model`. When the model is trained, the weights of the `dense_layer` will be updated based on the input and output of the layer, allowing the neural network to learn and improve its performance.