Tensorflow

Low api

def crossentropy(y, y_hat):
    y = tf.clip_by_value(y, 1e-15, 0.99)
    y_hat = tf.clip_by_value(y_hat, 1e-15, 0.99)
    cee = tf.math.reduce_sum(-(y * tf.math.log(y_hat)), axis=1)
    mean_cee = tf.reduce_mean(cee)
    return mean_loss

High api

def cee(y, y_hat):
    return tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=y_hat))

Keras

cce = tf.keras.losses.CategoricalCrossentropy()
cce(y_true, y_pred).numpy()