Difference between BinaryCrossentropy and CategoricalCrossentropy

TensorFlow Crossentropy

This post explains difference between BinaryCrossentropy and CategoricalCrossentropy

BinaryCrossentropy

BinaryCrossentropy is used for computing loss between true labels and predicted labels

Use BinaryCrossentropy only when there are only two label classes. For each example, there should be a single floating-point value per prediction

How to calculate `BinaryCrossentropy` in `TensorFlow`


import tensorflow as tf

y_true = [[0., 1.], [0., 0.]]
y_pred = [[0.6, 0.4], [0.4, 0.6]]

bce_loss = tf.keras.losses.BinaryCrossentropy()

bce_loss_value = bce_loss(y_true, y_pred).numpy()
print(bce_loss_value)

CategoricalCrossentropy

CategoricalCrossentropy is used for computing loss between true labels and predicted labels

Use CategoricalCrossentropy only when there are two or more label classes.

While using CategoricalCrossentropy labels should be provided in a one_hot representation

How to calculate `CategoricalCrossentropy` in `TensorFlow`


import tensorflow as tf

y_true = [[0, 1, 0], [0, 0, 1]]
y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]

cce_loss = tf.keras.losses.CategoricalCrossentropy()

cce_loss_value = cce_loss(y_true, y_pred).numpy()
print(cce_loss_value)