layers.Dense

The layers.Dense class in TensorFlow is used to create a fully connected neural network layer. It is a type of dense layer that takes the output of the previous layer and applies a set of weights to it, followed by an activation function. It is also known as a fully connected layer or a dense layer because every neuron in the previous layer is connected to every neuron in the current layer.

The Dense layer in TensorFlow has several parameters that can be set, including the number of neurons in the layer, the activation function to be used, the kernel initializer, and the bias initializer. These parameters can be adjusted to create a neural network that is tailored to a specific problem or dataset.

Here is an example of creating a Dense layer in TensorFlow:

import tensorflow as tf

# define the input shape
input_shape = (None, 784)

# create a Dense layer with 128 neurons and ReLU activation function
dense_layer = tf.keras.layers.Dense(128, activation='relu', input_shape=input_shape)

# print the summary of the layer
print(dense_layer.summary())

In this example, we create a Dense layer with 128 neurons and ReLU activation function. The input shape is set to (None, 784), which means that the layer can accept inputs of any batch size and 784 input features. The summary of the layer is printed, which shows the number of parameters in the layer and the output shape.