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118: gelu | TensorFlow | Tutorial
In this tutorial, we will learn about the gelu activation function in TensorFlow. The gelu activation function, short for Gaussian Error Linear Unit, is a popular activation function used in deep learning models.
What is gelu?
The gelu activation function is defined as:
gelu(x) = 0.5 * x * (1 + tanh(sqrt(2/pi) * (x + 0.044715 * x^3)))
Implementing gelu in TensorFlow
To implement the gelu activation function in TensorFlow, you can use the following code snippet:
import tensorflow as tf
def gelu(x):
return 0.5 * x * (1 + tf.tanh(tf.sqrt(2 / tf.constant(np.pi)) * (x + 0.044715 * tf.pow(x, 3))))
Usage in a neural network
You can use the gelu activation function in your neural network models by passing it as the activation function in the respective layers. For example:
model = tf.keras.Sequential([
tf.keras.layers.Dense(64, activation='gelu'),
tf.keras.layers.Dense(10, activation='softmax')
])
Conclusion
The gelu activation function is a powerful tool in deep learning models and can help improve the performance of your neural networks. By implementing gelu in TensorFlow, you can take advantage of this activation function in your own projects.
