import tensorflow as tf

y = tf.constant([1, 2, 3, 0, 2])

y = tf.one_hot(y, depth=4)  # max_label=3种

y = tf.cast(y, dtype=tf.float32)

out = tf.random.normal([5, 4])

out

loss1 = tf.reduce_mean(tf.square(y - out))

loss1

loss2 = tf.square(tf.norm(y - out)) / (5 * 4)

loss2

loss3 = tf.reduce_mean(tf.losses.MSE(y, out))

loss3

a = tf.fill([4], 0.25)

a * tf.math.log(a) / tf.math.log(2.)

-tf.reduce_sum(a * tf.math.log(a) / tf.math.log(2.))

a = tf.constant([0.1, 0.1, 0.1, 0.7])

-tf.reduce_sum(a * tf.math.log(a) / tf.math.log(2.))

a = tf.constant([0.01, 0.01, 0.01, 0.97])

-tf.reduce_sum(a * tf.math.log(a) / tf.math.log(2.))

tf.losses.categorical_crossentropy([0, 1, 0, 0], [0.25, 0.25, 0.25, 0.25])

tf.losses.categorical_crossentropy([0, 1, 0, 0], [0.1, 0.1, 0.8, 0.1])

tf.losses.categorical_crossentropy([0, 1, 0, 0], [0.1, 0.7, 0.1, 0.1])

tf.losses.categorical_crossentropy([0, 1, 0, 0], [0.01, 0.97, 0.01, 0.01])

tf.losses.BinaryCrossentropy()([1],[0.1])

tf.losses.binary_crossentropy([1],[0.1])