1. 三层简单神经网络



In [1]:

    
import tensorflow as tf

1.1 定义变量



In [2]:

    
w1= tf.Variable(tf.random_normal([2, 3], stddev=1, seed=1))
w2= tf.Variable(tf.random_normal([3, 1], stddev=1, seed=1))
x = tf.constant([[0.7, 0.9]])

1.2 定义前向传播的神经网络



In [3]:

    
a = tf.matmul(x, w1)
y = tf.matmul(a, w2)

1.3 调用会话输出结果



In [4]:

    
sess = tf.Session()
sess.run(w1.initializer)  
sess.run(w2.initializer)  
print(sess.run(y))  
sess.close()









    



[[ 3.95757794]]

2. 使用placeholder



In [5]:

    
x = tf.placeholder(tf.float32, shape=(1, 2), name="input")
a = tf.matmul(x, w1)
y = tf.matmul(a, w2)

sess = tf.Session()

init_op = tf.global_variables_initializer()  
sess.run(init_op)

print(sess.run(y, feed_dict={x: [[0.7,0.9]]}))









    



[[ 3.95757794]]

3. 增加多个输入



In [6]:

    
x = tf.placeholder(tf.float32, shape=(3, 2), name="input")
a = tf.matmul(x, w1)
y = tf.matmul(a, w2)

sess = tf.Session()
#使用tf.global_variables_initializer()来初始化所有的变量
init_op = tf.global_variables_initializer()  
sess.run(init_op)

print(sess.run(y, feed_dict={x: [[0.7,0.9],[0.1,0.4],[0.5,0.8]]}))









    



[[ 3.95757794]
 [ 1.15376544]
 [ 3.16749191]]