TensorFlow demo scope and restricted scope of operators

An example description

variable_scope can also use with tf.variable_scope("name") as xxxscope to define the scope. When using this method, the defined scope variable xxxscope will no longer be restricted by the surrounding scope.

two code

import tensorflow as tf
tf.reset_default_graph()
with tf.variable_scope("scope1") as sp:
     var1 = tf.get_variable("v", [1])
print("sp:",sp.name)                      #sp: scope1
print("var1:",var1.name)                  #var1: scope1/v:0    
with tf.variable_scope("scope2"):
    var2 = tf.get_variable("v", [1])
    
    with tf.variable_scope(sp) as sp1: #Put sp in the scope scope2 and as sp1, so that v3 is not restricted by the outer scope
        var3 = tf.get_variable("v3", [1])
          
        with tf.variable_scope("") : #Let v4 have one more empty layer
            var4 = tf.get_variable("v4", [1])
            
print("sp1:",sp1.name) #sp1: scope1 sp1 is under scope2, but the output is still scope1, no change
print("var2:",var2.name)    #var2: scope2/v:0
print("var3:",var3.name) #var3: scope1/v3:0 indicates that var3 is under scope1, again indicating that sp is not restricted by the outer layer.
print("var4:",var4.name) #var4: scope1//v4:0 v4 has one more empty layer
with tf.variable_scope("scope"):
    with tf.name_scope("bar"):
        v = tf.get_variable("v", [1])
        x = 1.0 + v
        with tf.name_scope(""): #null character returns the scope to the top level
            y = 1.0 + v
print("v:",v.name) #v: ​​scope/v:0 The naming of variables is only limited by variable_scope, not by name_scope
print("x.op:",x.op.name) #x.op: scope/bar/add The operator is not only limited by the name_scope scope, but also by the variable_scope scope.
print("y.op:",y.op.name) #y.op: add is returned to the top level under the empty character scope

Three running results

sp: scope1

var1:scope1/v:0

sp1: scope1

var2:scope2/v:0

var3:scope1/v3:0

var4: scope1 // v4: 0

v: scope/v:0

x.op: scope/bar/add

y.op: add