python-kerasHow do I use the Softmax activation function with Python and Keras?

The Softmax activation function can be used in Python and Keras to classify multiple classes of data. It is a generalization of the logistic sigmoid function that "squashes" a K-dimensional vector of arbitrary real values to a K-dimensional vector of real values in the range (0, 1) that add up to 1.

Example code using the Softmax activation function with Python and Keras is as follows:

import keras
from keras.models import Sequential
from keras.layers import Dense

model = Sequential()
model.add(Dense(units=4, activation='softmax'))

The code above creates a Sequential model with a single Dense layer containing 4 units and a Softmax activation function.

The parts of the code are:

1. import keras - Imports the Keras library.
2. from keras.models import Sequential - Imports the Sequential model from the Keras library.
3. from keras.layers import Dense - Imports the Dense layer from the Keras library.
4. model = Sequential() - Creates a new Sequential model.
5. model.add(Dense(units=4, activation='softmax')) - Adds a Dense layer with 4 units and a Softmax activation function to the model.

Helpful links

• Keras documentation on Softmax activation
• Towards Data Science article on Softmax activation

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