简单神经网络TensorFlow实现 简单神经网络TensorFlow实现

  1 #学习TensorFlow笔记
  2 import tensorflow as tf
  3  
  4 #定义变量
  5 #Variable 定义张量及shape
  6 w1= tf.Variable(tf.random_normal([2, 3], stddev=1, seed=1))
  7 w2= tf.Variable(tf.random_normal([3, 1], stddev=1, seed=1))
  8 with tf.Session() as sess:
  9     print(sess.run(w1.initializer))
 10     print(sess.run(w2.initializer))
 11 #None
 12 #None
 13  
 14  
 15  
 16 #打印张量，查看数据shape等信息
 17 print(w1)
 18 print(w2)
 19 #<tf.Variable \'Variable:0\' shape=(2, 3) dtype=float32_ref>
 20 #<tf.Variable \'Variable_1:0\' shape=(3, 1) dtype=float32_ref>
 21  
 22 #tf.constan是一个计算，结果为一个张量，保存在变量x中
 23 x = tf.constant([[0.7, 0.9]])
 24 print(x)
 25 #Tensor("Const:0", shape=(1, 2), dtype=float32)
 26 with tf.Session() as sess:
 27     print(sess.run(x))
 28 #[[ 0.69999999  0.89999998]]
 29  
 30  
 31 #定义前向传播的神经网络
 32 #matmul做矩阵乘法
 33 a = tf.matmul(x, w1)   # x shape=(1, 2)   w1 shape=(2, 3)
 34  
 35 print(a)
 36 #Tensor("MatMul:0", shape=(1, 3), dtype=float32)
 37  
 38 y = tf.matmul(a, w2)  #a shape=(1, 3)   w2 shape=(3, 1)
 39 print(y)
 40 #Tensor("MatMul_1:0", shape=(1, 1), dtype=float32)
 41  
 42  
 43 #调用会话输出结果
 44 with tf.Session() as sess:
 45     sess.run(w1.initializer)
 46     sess.run(w2.initializer)
 47     print(sess.run(a))
 48     #[[-2.76635647  1.12854266  0.57783246]]
 49     print(sess.run(y))
 50     #[[ 3.95757794]]
 51  
 52 #placeholder
 53 x=tf.placeholder(tf.float32,shape=(1,2),name="input")
 54 a=tf.matmul(x,w1)
 55 y=tf.matmul(a,w2)
 56 sess=tf.Session()
 57 init_op=tf.global_variables_initializer()
 58 sess.run(init_op)
 59  
 60 print(sess.run(y,feed_dict={x:[[0.8,0.9]]}))
 61 #[[ 4.2442317]]
 62 x = tf.placeholder(tf.float32, shape=(3, 2), name="input")
 63 a = tf.matmul(x, w1)
 64 y = tf.matmul(a, w2)
 65  
 66 sess = tf.Session()
 67 #使用tf.global_variables_initializer()来初始化所有的变量
 68 init_op = tf.global_variables_initializer()
 69 sess.run(init_op)
 70  
 71 print(sess.run(y, feed_dict={x: [[0.7,0.9],[0.1,0.4],[0.5,0.8]]}))
 72  
 73 \'\'\'
 74 [[ 3.95757794]
 75  [ 1.15376544]
 76  [ 3.16749239]]
 77 \'\'\'
 78 
#整体神经网络的实现

 81 import tensorflow as tf
 82 from   numpy.random import RandomState
 83 #定义神经网络的参数，输入和输出节点
 84 batch_size=8
 85 #均值为0 方差为1 随机分布满足正态分布 shape为2*3
 86 w1=tf.Variable(tf.random_normal([2,3],stddev=1,seed=1))
 87 w2=tf.Variable(tf.random_normal([3, 1], stddev=1, seed=1))
 88 #shape 根据数据自动计算 batchsize个
 89 x=tf.placeholder(tf.float32,shape=(None,2))
 90 y_=tf.placeholder(tf.float32,shape=(None,1))
 91  
 92 #定义前向传播过程，损失函数及反向传播算法
 93  
 94 a=tf.matmul(x,w1)
 95 y=tf.matmul(a,w2)
 96 #损失函数 使用交叉熵
 97 #优化方法使用AdamOptimizer
 98 cross_entropy=-tf.reduce_mean(y_*tf.log(tf.clip_by_value(y,1e-10,1.0)))
 99 train_step=tf.train.AdamOptimizer(learning_rate=0.001).minimize(cross_entropy)
100  
101 rdm=RandomState(1)
102 #随机生成128个数据 shape 128*2
103 X=rdm.rand(128,2)
104  
105 #Y的值是模拟的 ，实际假设x2+x1如果大于1则标签Y为1 否则标签Y为0
106 Y=[[int(x1+x2<1)] for (x1,x2) in X]
107  
108 #创建一个会话 ，运算计算图
109 #全局初始化变量
110 STEPS = 5000
111 with tf.Session() as sess:
112     init_op=tf.global_variables_initializer()
113     sess.run(init_op)
114     # 输出目前（未经训练）的参数取值。
115     print("w1:", sess.run(w1))
116     print("w2:", sess.run(w2))
117     print("\n")
118     for i in range(STEPS):
119         start = (i * batch_size) % 128
120         end = (i * batch_size) % 128 + batch_size
121         sess.run(train_step, feed_dict={x: X[start:end], y_: Y[start:end]})
122         if i % 1000 == 0:
123             total_cross_entropy = sess.run(cross_entropy, feed_dict={x: X, y_: Y})
124             print("After %d training step(s), cross entropy on all data is %g" % (i, total_cross_entropy))
125             # 输出训练后的参数取值。
126     print("\n")
127     print("w1:", sess.run(w1))
128     print("w2:", sess.run(w2))
129  
130 \'\'\'
131  
132 w1: [[-0.81131822  1.48459876  0.06532937]
133  [-2.4427042   0.0992484   0.59122431]]
134 w2: [[-0.81131822]
135  [ 1.48459876]
136  [ 0.06532937]]
137  
138  
139 After 0 training step(s), cross entropy on all data is 0.0674925
140 After 1000 training step(s), cross entropy on all data is 0.0163385
141 After 2000 training step(s), cross entropy on all data is 0.00907547
142 After 3000 training step(s), cross entropy on all data is 0.00714436
143 After 4000 training step(s), cross entropy on all data is 0.00578471
144  
145  
146 w1: [[-1.96182752  2.58235407  1.68203771]
147  [-3.46817183  1.06982315  2.11788988]]
148 w2: [[-1.82471502]
149  [ 2.68546653]
150  [ 1.41819501]]
151  
152 Process finished with exit code 0
153 \'\'\'

学习TensorFlow笔记

　　整体神经网络的实现