tf.Variable

import tensorflow as tf
tf.reset_default_graph()
v1 = tf.Variable(tf.constant(1.0, shape=[1]), name="v1")
v2 = tf.Variable(tf.constant(2.0), name="v2")
v3 = tf.Variable(2.0, name="v3")
result1 = v1 + v2
result2 = v1 + v3
print(v1)  # <tf.Variable 'v1:0' shape=(1,) dtype=float32_ref>
print(v2) # <tf.Variable 'v2:0' shape=() dtype=float32_ref>
print(v3) # <tf.Variable 'v3:0' shape=() dtype=float32_ref>
print(result1) # Tensor("add:0", shape=(1,), dtype=float32)
print(result2) # Tensor("add_1:0", shape=(1,), dtype=float32)

with tf.Session() as sess:
     tf.global_variables_initializer().run()# sess.run(tf.global_variables_initializer())
     print(sess.run(v1)) # [ 1.]
     print(sess.run(v2)) # 2.0
     print(sess.run(v3)) # 2.0
     print(sess.run(result1)) # [ 3.]
     print(sess.run(result2)) # [ 3.]

 

 

import tensorflow as tf
tf.reset_default_graph()

get_variable_a = tf.get_variable("a", (2, 5))
variable_b = tf.Variable(initial_value=2.2,name="b") # initial_value 必须指定 ， <tf.Variable 'b:0' shape=() dtype=int32_ref>
variable_d = tf.Variable(initial_value=tf.constant(4.4, shape=[3, 4]),name="d") # <tf.Variable 'd:0' shape=(3, 4) dtype=float32_ref>

 

with tf.Session() as sess:
      tf.global_variables_initializer().run()

     sess_run_a = sess.run(get_variable_a)
    print(type(sess_run_a))
    print(sess_run_a)



   sess_run_b = sess.run(variable_b)
   print(type(sess_run_b))
   print(sess_run_b)



   sess_run_d = sess.run(variable_d)
   print(type(sess_run_d))
   print(sess_run_d)

 

 

<class 'numpy.ndarray'>
[[-0.8444121   0.45873487  0.43813705 -0.20740622  0.8408555 ]
 [ 0.5865301   0.8926667  -0.38779628  0.23797786 -0.4147941 ]]
<class 'numpy.float32'>
2.2
<class 'numpy.ndarray'>
[[4.4 4.4 4.4 4.4]
 [4.4 4.4 4.4 4.4]
 [4.4 4.4 4.4 4.4]]