tensorflow 变量
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tf.get_variable(): 不受 name_scope 的影响,在未指定共享变量时,重名报错
tf.Variable() : 会自动检测有无重名,重名自行处理
with tf.name_scope(‘name_scope_1‘):# name_scope 的作用封装一堆操作,使数据流图有层次感 var1 = tf.get_variable(name=‘var1‘, shape=[1], dtype=tf.float32) var2 = tf.get_variable(name=‘var1‘, shape=[1], dtype=tf.float32) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) print(var1.name, sess.run(var1)) print(var2.name, sess.run(var2)) # ValueError: Variable var1 already exists, disallowed. Did you mean # to set reuse=True in VarScope? Originally defined at: # var1 = tf.get_variable(name=‘var1‘, shape=[1], dtype=tf.float32)
当需要共享变量时,使用tf.variable_scope()
with tf.variable_scope(‘variable_scope_y‘) as scope: var1 = tf.get_variable(name=‘var1‘, shape=[1], dtype=tf.float32) scope.reuse_variables() # 设置共享变量 var1_reuse = tf.get_variable(name=‘var1‘) var2 = tf.Variable(initial_value=[2.], name=‘var2‘, dtype=tf.float32) var2_reuse = tf.Variable(initial_value=[2.], name=‘var2‘, dtype=tf.float32) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) print(var1.name, sess.run(var1)) print(var1_reuse.name, sess.run(var1_reuse)) print(var2.name, sess.run(var2)) print(var2_reuse.name, sess.run(var2_reuse)) # 输出结果: # variable_scope_y/var1:0 [-1.59682846] # variable_scope_y/var1:0 [-1.59682846] 可以看到变量var1_reuse重复使用了var1 # variable_scope_y/var2:0 [ 2.] # variable_scope_y/var2_1:0 [ 2.] 也可以这样with tf.variable_scope(‘foo‘) as foo_scope: v = tf.get_variable(‘v‘, [1]) with tf.variable_scope(‘foo‘, reuse=True): v1 = tf.get_variable(‘v‘) assert v1 == v 或者这样:with tf.variable_scope(‘foo‘) as foo_scope: v = tf.get_variable(‘v‘, [1]) with tf.variable_scope(foo_scope, reuse=True): v1 = tf.get_variable(‘v‘) assert v1 == v
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