In a Convolutional Neural Network (CNN), the activation function introduces non-linearity into the model, allowing it to learn from the error and make adjustments, which is essential for learning complex patterns. While ReLU (Rectified Linear Unit) is the most commonly used activation function, there are several other options available, each with its characteristics and use cases. Below are a few alternatives to ReLU:
1. Sigmoid Activation Function
cnn.add(tf.keras.layers.Conv2D(filters=64, kernel_size=3, activation='sigmoid'))
2. Tanh (Hyperbolic Tangent) Activation Function
cnn.add(tf.keras.layers.Conv2D(filters=64, kernel_size=3, activation='tanh'))
3. Leaky ReLU
cnn.add(tf.keras.layers.Conv2D(filters=64, kernel_size=3, activation=tf.keras.layers.LeakyReLU(alpha=0.01)))
4. Parametric ReLU (PReLU)
cnn.add(tf.keras.layers.Conv2D(filters=64, kernel_size=3, activation=tf.keras.layers.PReLU()))
5. Exponential Linear Unit (ELU)
cnn.add(tf.keras.layers.Conv2D(filters=64, kernel_size=3, activation='elu'))
6. Swish
cnn.add(tf.keras.layers.Conv2D(filters=64, kernel_size=3, activation='swish'))
7. Softmax
cnn.add(tf.keras.layers.Conv2D(filters=64, kernel_size=3, activation='softmax'))
Leaky ReLU and PReLU are variants of ReLU designed to allow a small, positive gradient when the unit is not active and can help mitigate the vanishing gradient problem associated with standard ReLU units.
ELU and Swish can be particularly useful when dealing with a vanishing gradient problem, as they assign mean values close to zero which helps in pushing the model towards making decisions.
Softmax is generally used in the output layer for multi-class classification problems but can be used in hidden layers when constructing more complex architectures.
