Common Matrix Generation for Tensorflow

# All-0 and all-1 matrices

v1 = (([3,3,3]), name="v1") 

v2 = (([10,5]), name="v2") 
 
#Populate single-valued matrices
v3 = (([2,3], 9)) 

 
# Constant Matrix
v4_1 = ([1, 2, 3, 4, 5, 6, 7]) 
v4_2 = (-1.0, shape=[2, 3]) 


# All-1 or all-0 matrices with the same shape as v4_1

v5_1=tf.ones_like(v4_1)

v5_2=tf.zeros_like(v4_1) 


# Generate an isometric series
v6_1 = (10.0, 12.0, 30, name="linspace")#float32 or float64 
v7_1 = (10, 20, 3)#just int32 
 
# Generate various randomized data matrices

# Mean distribution

v8_1 = (tf.random_uniform([2,4], minval=0.0, maxval=2.0, dtype=tf.float32, seed=1234, name="v8_1")) 
#Normal distribution

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

#Normal distribution, but with the numbers outside of 2 sigma removed #

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

# Rearrange the 3 rows
v8_5 = tf.random_shuffle([[1,2,3],[4,5,6],[6,6,6]], seed=134, name="v8_5") 

("",(v1))#numpy save v1 as file 
test_a = ("") 
print test_a[1,2]