tf.keras.losses.Hinge in TensorFlow

Hello programmers, in this tutorial, we will learn how to use tf.keras.losses.Hinge in TensorFlow.

All the codes are done in a collab notebook

What is Hinge loss?

  • It is a loss function used for training classifiers.
  • It is mainly used for those places where the marginal gap between two classes is large, like the Support Vector Machine(SVM).
  • Its expected values are in the range of -1 to 1. If we have values in binary formate(0,1), they will be converted into -1 to 1.

let’s see this with the help of coding

  • 1st we have a true label of zeros and ones
  • Then we will create random predicted labels with random values.
  • Then we will create a hinge loss function using Keras API and at the end, will pass our true values and predicted values in that function.
#input Labels
y_true = [[0., 1.], 
          [1., 0.]]
#Predicted Lables
y_pred = [[-0.6, 0.2], 
          [0.9, 0.]]

h_loss = tf.keras.losses.Hinge()
h_loss(y_true, y_pred).numpy()

Here we see we got the loss “0.57500005” using the Hinge loss function

Hopefully, you have learned how to use tf.keras.losses.Hinge in TensorFlow

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