Which activation function is used in the final layer of the generator model?tanhleakyrelurelusigmoidsoftmax

To determine which activation function is used in the final layer of the generator model, we need to consider the purpose of the generator in a Generative Adversarial Network (GAN). The generator aims to create outputs (such as images) that mimic the training data.

Breakdown of Options:

1. tanh: Commonly used in the generator's final layer, especially for tasks like image generation, as it outputs values in the range of $[-1, 1]$.
2. Leaky ReLU: Typically used in hidden layers but not ideal for the final output.
3. ReLU: This activation is also not suited for the final layer due to its output range being $[0, \infty)$.
4. Sigmoid: Often used for binary classification, outputting values between $[0, 1]$; can be used depending on the context of the output.
5. Softmax: Primarily used in classification tasks and not in the final layer of a generator.

Common Practices:

The most common activation function for the final layer of the generator in tasks like image generation is tanh, especially if the generated values are meant to be normalized.

Final Answer:

In the final layer of the generator model, the most commonly used activation function is tanh.