Tensorflow实现损失函数

最近在学习tensorflow，今天特地总结下损失函数，多总结总是件好事，也方便大家查找。资料来源于《tensorflow机器学习实战指南》。

import tensorflow as tf import matplotlib.pyplot as plt

回归算法损失函数

x_vals = tf.linspace(-1.,1.,500) target = tf.constant(0.)

l2正则损失函数（即欧拉损失函数）

sess = tf.InteractiveSession() l2_y_vals = tf.square(target-x_vals) l2_y_out = sess.run(l2_y_vals)

l1正则损失函数（即绝对值损失函数）

l1_y_vals = tf.abs(target-x_vals) l1_y_out = sess.run(l1_y_vals)

Pseudo-Huber损失函数

delta1 = tf.constant(0.25) phuber1_y_vals = tf.multiply(tf.square(delta1),tf.sqrt(1.+tf.square((target-x_vals)/delta1))-1.) phuber1_y_out = sess.run(phuber1_y_vals) delta2 = tf.constant(5.) phuber2_y_vals = tf.multiply(tf.square(delta2),tf.sqrt(1.+tf.square((target-x_vals)/delta2))-1.) phuber2_y_out = sess.run(phuber2_y_vals)

回归算法的损失函数

x_array = sess.run(x_vals) plt.plot(x_array,l2_y_out,'b-',label='L2 Loss') plt.plot(x_array,l1_y_out,'r--',label='L1 Loss') plt.plot(x_array,phuber1_y_out,'k-.',label = 'P-Huber Loss(0.25)') plt.plot(x_array,phuber2_y_out,'g:',label= 'P-Huber Loss(5.)') plt.ylim(-0.2,0.4) plt.legend(loc='lower right',prop={'size':11}) plt.show()

分类算法损失函数

x_vals = tf.linspace(-3.,5.,500) target = tf.constant(1.) targets = tf.fill([500,],1.)

Hinge损失函数

hinge_y_vals = tf.maximum(0.,1.-tf.multiply(target,x_vals)) hinge_y_out = sess.run(hinge_y_vals)

两类交叉熵损失函数（cross_entropy loss）

xentropy_y_vals = -tf.multiply(target,tf.log(x_vals))-tf.multiply((1.-target),tf.log(1.-x_vals)) xentropy_y_out = sess.run(xentropy_y_vals)

sigmoid交叉熵损失函数(sigmoid cross entropy loss)

xentropy_sigmoid_y_vals = tf.nn.sigmoid_cross_entropy_with_logits(logits=x_vals,labels=targets) xentropy_sigmoid_y_out = sess.run(xentropy_sigmoid_y_vals)

加权交叉熵损失函数(weighted cross entropy loss)

weight = tf.constant(0.5) xentropy_weighted_y_vals = tf.nn.weighted_cross_entropy_with_logits(x_vals,targets,weight) xentropy_weighted_y_out = sess.run(xentropy_weighted_y_vals)

softmax交叉熵损失函数(softmax cross entropy loss)

unscaled_logits = tf.constant([[1.,-3.,10.]]) target_dist = tf.constant([[0.1,0.02,0.88]]) softmax_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits=unscaled_logits,labels = target_dist) sess.run(softmax_entropy)

稀疏softmax交叉熵损失函数(sparse softmax cross entropy loss)

unscaled_logits = tf.constant([[1.,-3.,10.]]) sparse_target_dist = tf.constant([2]) sparse_xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=unscaled_logits,labels=sparse_target_dist) sess.run(sparse_xentropy)

分类算法损失函数

x_array = sess.run(x_vals) plt.plot(x_array,hinge_y_out,'b-',label='Hinge Loss') plt.plot(x_array,xentropy_y_out,'r--',label='Cross Entropy Loss') plt.plot(x_array,xentropy_sigmoid_y_out,'k-.',label='Cross Entropy Sigmoid Loss') plt.plot(x_array,xentropy_weighted_y_out,'g:',label='Weighted Cross Entropy Loss(x0.5)') plt.ylim(-1.5,3) plt.legend(loc='lower right',prop={'size':11}) plt.show()