tf.constant_initializer

import tensorflow as tf
def make_variables(k, initializer):
  return (tf.Variable(initializer(shape=[k], dtype=tf.float32)),
          tf.Variable(initializer(shape=[k, k], dtype=tf.float32)))
v1, v2 = make_variables(3, tf.constant_initializer(2.))

v1
Out[55]: <tf.Variable 'Variable:0' shape=(3,) dtype=float32, numpy=array([2., 2., 2.], dtype=float32)>

Out[56]: 
<tf.Variable 'Variable:0' shape=(3, 3) dtype=float32, numpy=
array([[2., 2., 2.],
       [2., 2., 2.],
       [2., 2., 2.]], dtype=float32)>

make_variables(4, tf.random_uniform_initializer(minval=-1., maxval=1.))

(<tf.Variable 'Variable:0' shape=(4,) dtype=float32, numpy=array([-0.5988991 ,  0.7792051 ,  0.5964377 , -0.86411357], dtype=float32)>,
 <tf.Variable 'Variable:0' shape=(4, 4) dtype=float32, numpy=
 array([[ 0.12324381, -0.5521028 , -0.37995243, -0.530148  ],
        [-0.97156   , -0.3974259 , -0.95826817,  0.9413047 ],
        [-0.36188293,  0.87448907, -0.8246341 , -0.05807495],
        [-0.64107823,  0.38210344,  0.48264408,  0.15233326]],
       dtype=float32)>)

value = [0, 1, 2, 3, 4, 5, 6, 7]
init = tf.constant_initializer(value)
# Fitting shape
tf.Variable(init(shape=[2, 4], dtype=tf.float32))

<tf.Variable 'Variable:0' shape=(2, 4) dtype=float32, numpy=
array([[0., 1., 2., 3.],
       [4., 5., 6., 7.]], dtype=float32)>