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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