Introduction

Keras is a high-level neural networks API, written in Python, which is capable of running on top of TensorFlow, CNTK, or Theano. Among the core components of Keras, layers are fundamental building blocks that specify how data flows through the network. Two important types of layers in Keras are the `Dense` layer and the `Activation` layer. Understanding the differences between these two types enables developers to design efficient neural network architectures.

Dense Layer in Keras

The `Dense` layer, also known as a fully connected layer, is a core layer in Keras used for building a neural network. It is often utilized in feedforward neural networks.

Technical Explanation

• Definition: The `Dense` layer is a standard layer where each neuron receives input from all neurons of the previous layer. Formally, the outputs are calculated as:
  $$y = f(wx + b)$$
  Where $x$ is the input vector, $w$ is the weight matrix, $b$ is the bias vector, and $f$ is the activation function.
• Parameters:
  • `units`: Positive integer, denoting the dimensionality of the output space.
  • `activation`: Activation function to use. If you don't specify anything, no activation is applied.
  • `kernel_initializer`: Method to initialize the kernel weights matrix.
  • `bias_initializer`: Method to initialize the bias vector.

Example

• Definition: The `Activation` layer applies an activation function element-wise to a tensor.
• Activation Functions: Common activation functions include:
  • `relu`: Rectifier Linear Unit
  • `sigmoid`: Logistic Sigmoid
  • `tanh`: Hyperbolic Tangent
• Parameters:
  • `activation`: String name of the activation function to use.
• Use `Dense` layer when:
  • You require an entire transformation, i.e., from input to a new space through weighted connections.
  • You prefer simplicity by including activation functions directly inside the layer.
• Use `Activation` layer when:
  • You prefer clarity and separation of linear transformations and non-linear activations.
  • You plan to experiment with different activation functions without altering the setup of the linear transformations (weights and biases).