神经网络的搭建课分四步完成：准备工作、前向传播、反向传播和循环迭代。
√0.导入模块，生成模拟数据集；
import
常量定义
生成数据集
√1.前向传播：定义输入、参数和输出
x= y_=
w1= w2=
a= y=
√2. 反向传播：定义损失函数、反向传播方法
loss=
train_step=
√3. 生成会话，训练 STEPS 轮
with tf.session() as sess
Init_op=tf. global_variables_initializer()
sess_run(init_op)
STEPS=3000
for i in range(STEPS):
start=
end=
sess.run(train_step, feed_dict:)
python代码：
#coding:utf-8 #0导入模块，生成模拟数据集。 #tensorflow学习笔记(北京大学) tf3_6.py 完全解析神经网络搭建学习 import tensorflow as tf import numpy as np BATCH_SIZE = 8 SEED = 23455 rdm = np.random.RandomState(SEED) X = rdm.rand(32,2) Y_ = [[int(x0 + x1 < 1)] for (x0, x1) in X] #for(x0,x1) in x是从x中取出x0和x1的所有值，[int(x0+x1<1)]意思时x0+x1<1，则Y_=1;否则为0； print("X: ",X) print("Y_: ",Y_) x = tf.placeholder(tf.float32, shape=(None, 2))#用placeholder实现输入定义 y_= tf.placeholder(tf.float32, shape=(None, 1))#用placeholder实现占位 w1= tf.Variable(tf.random_normal([2, 3], stddev=1, seed=1))#正态分布随机数 w2= tf.Variable(tf.random_normal([3, 1], stddev=1, seed=1))#正态分布随机数 a = tf.matmul(x, w1)#点积 y = tf.matmul(a, w2)#点积 #2定义损失函数及反向传播方法。 loss_mse = tf.reduce_mean(tf.square(y-y_)) train_step = tf.train.GradientDescentOptimizer(0.001).minimize(loss_mse) #train_step = tf.train.MomentumOptimizer(0.001,0.9).minimize(loss_mse) #train_step = tf.train.AdamOptimizer(0.001).minimize(loss_mse) #3生成会话，训练STEPS轮 with tf.Session() as sess: init_op = tf.global_variables_initializer()#初始化 sess.run(init_op) # 输出目前（未经训练）的参数取值。 print("w1: ", sess.run(w1)) print("w2: ", sess.run(w2)) print(" ") # 训练模型。 STEPS = 3000 for i in range(STEPS):#3000轮 start = (i*BATCH_SIZE) % 32 #i*8%32 end = start + BATCH_SIZE #i*8%32+8 sess.run(train_step, feed_dict={x: X[start:end], y_: Y_[start:end]}) if i % 500 == 0: total_loss = sess.run(loss_mse, feed_dict={x: X, y_: Y_}) print("After %d training step(s), loss_mse on all data is %g" % (i, total_loss)) # 输出训练后的参数取值。 print(" ") print("w1: ", sess.run(w1)) print("w2: ", sess.run(w2)) #，只搭建承载计算过程的 #计算图，并没有运算，如果我们想得到运算结果就要用到“会话 Session()”了。 #√会话（Session）： 执行计算图中的节点运算 print("w1: ", w1) print("w2: ", w2) """ X: [[ 0.83494319 0.11482951] [ 0.66899751 0.46594987] [ 0.60181666 0.58838408] [ 0.31836656 0.20502072] [ 0.87043944 0.02679395] [ 0.41539811 0.43938369] [ 0.68635684 0.24833404] [ 0.97315228 0.68541849] [ 0.03081617 0.89479913] [ 0.24665715 0.28584862] [ 0.31375667 0.47718349] [ 0.56689254 0.77079148] [ 0.7321604 0.35828963] [ 0.15724842 0.94294584] [ 0.34933722 0.84634483] [ 0.50304053 0.81299619] [ 0.23869886 0.9895604 ] [ 0.4636501 0.32531094] [ 0.36510487 0.97365522] [ 0.73350238 0.83833013] [ 0.61810158 0.12580353] [ 0.59274817 0.18779828] [ 0.87150299 0.34679501] [ 0.25883219 0.50002932] [ 0.75690948 0.83429824] [ 0.29316649 0.05646578] [ 0.10409134 0.88235166] [ 0.06727785 0.57784761] [ 0.38492705 0.48384792] [ 0.69234428 0.19687348] [ 0.42783492 0.73416985] [ 0.09696069 0.04883936]] Y_: [[1], [0], [0], [1], [1], [1], [1], [0], [1], [1], [1], [0], [0], [0], [0], [0], [0], [1], [0], [0], [1], [1], [0], [1], [0], [1], [1], [1], [1], [1], [0], [1]] w1: [[-0.81131822 1.48459876 0.06532937] [-2.4427042 0.0992484 0.59122431]] w2: [[-0.81131822] [ 1.48459876] [ 0.06532937]] After 0 training step(s), loss_mse on all data is 5.13118 After 500 training step(s), loss_mse on all data is 0.429111 After 1000 training step(s), loss_mse on all data is 0.409789 After 1500 training step(s), loss_mse on all data is 0.399923 After 2000 training step(s), loss_mse on all data is 0.394146 After 2500 training step(s), loss_mse on all data is 0.390597 w1: [[-0.70006633 0.9136318 0.08953571] [-2.3402493 -0.14641267 0.58823055]] w2: [[-0.06024267] [ 0.91956186] [-0.0682071 ]] """
学习内容来自：慕课APP中人工智能实践-Tensorflow笔记；北京大学曹健老师的课程
#初始化