What is Activations module in Keras, How to use it?

In Keras, activations are functions that are used to introduce non-linearity into the output of a neural network layer. The "activations" module in Keras provides several options for activation functions, such as sigmoid, tanh, ReLU, and softmax.

Here's an example of how to use the activations module in Keras:

python
from keras.models import Sequential
from keras.layers import Dense, Activation

model = Sequential()
model.add(Dense(64, input_dim=100))
model.add(Activation('relu'))
model.add(Dense(32))
model.add(Activation('sigmoid'))
model.add(Dense(10))
model.add(Activation('softmax'))

In the example above, we are creating a sequential model with three dense layers. The first layer has 64 units and takes an input of 100 dimensions. The activation function used in this layer is the ReLU activation function, which introduces non-linearity into the output of this layer. The second layer has 32 units and is activated using the sigmoid function. The third and final layer has 10 units and is activated using the softmax function, which is commonly used in multi-class classification problems.

By default, Keras uses the linear activation function, which is equivalent to not using any activation function at all. However, you can choose to use any of the activation functions available in the "activations" module to introduce non-linearity into the output of your neural network layers. Activation functions help improve the performance of the model by allowing the neural network to learn non-linear relationships between the input and output variables.

Keywords: Keras activations, activation functions, ReLU, sigmoid, tanh, softmax, non-linearity, neural network layers, how to use Keras activations.