#全0和全1矩阵

v1 = tf.Variable(tf.zeros([3,3,3]), name="v1") 

v2 = tf.Variable(tf.ones([10,5]), name="v2") 
 
#填充单值矩阵 
v3 = tf.Variable(tf.fill([2,3], 9)) 

 
#常量矩阵 
v4_1 = tf.constant([1, 2, 3, 4, 5, 6, 7]) 
v4_2 = tf.constant(-1.0, shape=[2, 3]) 


# 和v4_1形状一样的全1或全0矩阵

v5_1=tf.ones_like(v4_1)

v5_2=tf.zeros_like(v4_1) 


#生成等差数列 
v6_1 = tf.linspace(10.0, 12.0, 30, name="linspace")#float32 or float64 
v7_1 = tf.range(10, 20, 3)#just int32 
 
#生成各种随机数据矩阵 

#平均分布

v8_1 = tf.Variable(tf.random_uniform([2,4], minval=0.0, maxval=2.0, dtype=tf.float32, seed=1234, name="v8_1")) 
#正态分布

v8_2 = tf.Variable(tf.random_normal([2,3], mean=0.0, stddev=1.0, dtype=tf.float32, seed=1234, name="v8_2")) 

#正态分布,但是去掉2sigma外的数字

v8_3 = tf.Variable(tf.truncated_normal([2,3], mean=0.0, stddev=1.0, dtype=tf.float32, seed=1234, name="v8_3")) 

#把这3个行重排列
v8_5 = tf.random_shuffle([[1,2,3],[4,5,6],[6,6,6]], seed=134, name="v8_5") 

np.save("v1.npy",sess.run(v1))#numpy save v1 as file 
test_a = np.load("v1.npy") 
print test_a[1,2]