import tensorflow as tf

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]])  

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

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

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]]}))

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()來初始化所有的變量
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]]})) 

吳裕雄 python 神經網絡——TensorFlow 三層簡單神經網絡的前向傳播算法