來源：中國大學MOOC

損失函數有三種：均方誤差、自定義、交叉熵

均方誤差：

#coding:utf-8
#預測多或預測少的影響一樣
#0導入模塊，生成數據集
import tensorflow as tf
import numpy as np
BATCH_SIZE = 8
SEED = 23455

rdm = np.random.RandomState(SEED)
X = rdm.rand(32,2)
Y_= [[x1+x2+(rdm.rand()/10.0-0.05)] for (x1,x2) in X]

#1定義神經網絡的輸入、參數和輸出，定義前向傳播過程
x = tf.placeholder(tf.float32, shape=(None, 2))
y_
 
= tf.placeholder(tf.float32, shape=(None, 1))
w1 = tf.Variable(tf.random_normal([2,1], stddev=1, seed=1))
y = tf.matmul(x,w1)

#2定義損失函數及反向傳播方法
#定義損失函數為MSE，反向傳播方法為梯度下降
loss_mse = tf.reduce_mean(tf.square(y-y_))
train_step = tf.train.GradientDescentOptimizer(0.001).minimize(loss_mse)

#3生成會話，訓練STEPS輪
with tf.Session() as sess:
    init_op 
 
= tf.global_variables_initializer()
    sess.run(init_op)
    STEPS = 20000
    for i in range(STEPS):
        start = (i*BATCH_SIZE) % 32
        end = (i*BATCH_SIZE) % 32 + BATCH_SIZE
        sess.run(train_step, feed_dict={x:X[start:end], y_:Y_[start:end]})
        if i % 500 == 0:
            print "After %d training steps, w1 is:
 
" % (i)
            print sess.run(w1)
    print "Final w1 is: \n",sess.run(w1)

損失函數