TensorFlow Activation Functions: Complete Guide

TensorFlow offers a variety of activation functions, including but not limited to:

1. rectified linear unit
2. The sigmoid function in TensorFlow.
3. hyperbolic tangent function in TensorFlow
4. a function in TensorFlow called softplus
5. tf.nn.softsign can be rewritten as TensorFlow’s soft sign function.
6. An exponential linear unit function
7. tf.nn.leaky_relu is a function in TensorFlow that computes the Leaky ReLU activation function.
8. tf.nn.log_softmax calculates the logarithm of the softmax function.

These activation functions can be used in the hidden and output layers of neural networks to help the network learn nonlinear relationships and solve problems like vanishing gradients. TensorFlow also provides high-level interfaces such as tf.keras.layers for easily incorporating many common activation functions when building neural network models.