参考曹健老师的tensorFlow公开课
第三节的代码
主要是介绍了构建神经网络的流程
代码简单,但是流程值得记住
特别是关键的函数需要记忆
#coding:utf-8 #1、导入模块、生成数据集 import tensorflow as tf import numpy as np BATCH_SIZE = 8 seed = 23455 #基于see产生随机数 rng = np.random.RandomState(seed) #随机数返回32行,2列的矩阵 表示32组,体积和重量 作为输入数据集 X = rng.rand(32,2) #根据X人工生成Y,作为训练数据标签 Y = [[int(X0 + X1 <1)] for(X0,X1) in X] #????这种写法 print("X:\n",X) print("Y:\n",Y) #2 定义什么网络的前向传播过程:输入、参数、输出、网络 x = tf.placeholder(tf.float32,shape=(None,2)) y_ = tf.placeholder(tf.float32,shape=(None,1)) w1 = tf.Variable(tf.random_normal([2,3],stddev=1,seed=1)) w2 = tf.Variable(tf.random_normal([3,1],stddev=1,seed=1)) a = tf.matmul(x,w1) y = tf.matmul(a,w2) #3定义反向传播 loss = tf.reduce_mean(tf.square(y-y_)) #写loss的时候犯了一个错误,就是 y-y_ 写成了 Y-y_ Y是训练数据的存储,不是计算图中的变量 # y是计算图中的输出,y_是计算图的输入 train_step = tf.train.GradientDescentOptimizer(0.001).minimize(loss) #or # train_step = tf.train.MomentumOptimizer(0.001,0.9).minimize(loss) # train_step = tf.train.AdamOptimizer(0.001).minimize(loss) #4生成会话,训练steps轮 with tf.Session() as sess: init_op = tf.global_variables_initializer() sess.run(init_op) #输出目前未经计算的参数的数值 print("w1:\n",sess.run(w1)) print("w1:\n", sess.run(w2)) print("\n") #训练模型 STEPS = 3000 for i in range(STEPS): start = (i*BATCH_SIZE)%32 end = start +BATCH_SIZE sess.run(train_step,feed_dict={x:X[start:end],y_:Y[start:end]}) if i%500 == 0: total_loss = sess.run(loss,feed_dict={x:X,y_:Y}) print("After %d traning steps,loss on all data is %g"%(i,total_loss)) print("\n") print("w1:\n",sess.run(w1)) print("w2:\n",sess.run(w2))