TensorFlow裡建立變數的兩種方式有 tf.get_variable() 和 tf.Variable()
阿新 • • 發佈:2019-02-03
import tensorflow as tf with tf.variable_scope('variable_scope_y') as scope: var1 = tf.get_variable(name='var1', shape=[1], dtype=tf.float32) scope.reuse_variables() # 設定共享變數 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)) # 輸出結果: # variable_scope_y/var1:0 [-1.59682846] # variable_scope_y/var1:0 [-1.59682846] 可以看到變數var1_reuse重複使用了var1 # variable_scope_y/var2:0 [ 2.] # variable_scope_y/var2_1:0 [ 2.]