损失函数及其梯度

Typical Loss

• Mean Squared Error

• Cross Entropy Loss
  • binary
  • multi-class
  • +softmax

MSE

• \(loss = \sum[y-(xw+b)]^2\)

• \(L_{2-norm} = ||y-(xw+b)||_2\)

• \(loss = norm(y-(xw+b))^2\)

Derivative

• \(loss = \sum[y-f_\theta(x)]^2\)

• \(\frac{\nabla\text{loss}}{\nabla{\theta}}=2\sum{[y-f_\theta(x)]}*\frac{\nabla{f_\theta{(x)}}}{\nabla{\theta}}\)

MSE Gradient

import tensorflow as tf

x = tf.random.normal([2, 4])
w = tf.random.normal([4, 3])
b = tf.zeros([3])
y = tf.constant([2, 0])

with tf.GradientTape() as tape:
    tape.watch([w, b])
    prob = tf.nn.softmax(x @ w + b, axis=1)
    loss = tf.reduce_mean(tf.losses.MSE(tf.one_hot(y, depth=3), prob))

grads = tape.gradient(loss, [w, b])
grads[0]

grads[1]

Softmax

• soft version of max

• 大的越来越大，小的越来越小、越密集

Derivative

\[ p_i = \frac{e^{a_i}}{\sum_{k=1}^Ne^{a_k}} \]

• i=j

\[ \frac{\partial{p_i}}{\partial{a_j}}=\frac{\partial{\frac{e^{a_i}}{\sum_{k=1}^Ne^{a_k}}}}{{\partial{a_j}}} = p_i(1-p_j) \]

• \(i\neq{j}\)
  \[ \frac{\partial{p_i}}{\partial{a_j}}=\frac{\partial{\frac{e^{a_i}}{\sum_{k=1}^Ne^{a_k}}}}{{\partial{a_j}}} = -p_j*p_i \]

x = tf.random.normal([2, 4])
w = tf.random.normal([4, 3])
b = tf.zeros([3])
y = tf.constant([2, 0])

with tf.GradientTape() as tape:
    tape.watch([w, b])
    logits =x @ w + b
    loss = tf.reduce_mean(
        tf.losses.categorical_crossentropy(tf.one_hot(y, depth=3),
                                           logits,
                                           from_logits=True))

grads = tape.gradient(loss, [w, b])
grads[0]

grads[1]

转载于:https://www.cnblogs.com/nickchen121/p/10906835.html