tf.trainable_variables方法

import tensorflow as tf

v1 = tf.get_variable('v1', shape=[1])
v2 = tf.get_variable('v2', shape=[1], trainable=False)

with tf.variable_scope('scope1'):
    s1 = tf.get_variable('s1', shape=[1], initializer=tf.random_normal_initializer())
g1=tf.Graph()
g2=tf.Graph()

with g1.as_default():
    g1v1 = tf.get_variable('g1v1', shape=[1])
    g1v2 = tf.get_variable('g1v2', shape=[1], trainable=False)
    g1vs = tf.trainable_variables()
    # [<tf.Variable 'g1v1:0' shape=(1,) dtype=float32_ref>]
    print(g1vs)

with g2.as_default():
    g2v1 = tf.get_variable('g2v1', shape=[1])
    g2v2 = tf.get_variable('g2v2', shape=[1], trainable=False)
    g2vs = tf.trainable_variables()
    # [<tf.Variable 'g2v1:0' shape=(1,) dtype=float32_ref>]
    print(g2vs)

with tf.Session() as sess:
    vs = tf.trainable_variables()
    # [<tf.Variable 'v1:0' shape=(1,) dtype=float32_ref>, <tf.Variable 'scope1/s1:0' shape=(1,) dtype=float32_ref>]
    print(vs)

tf.trainable_variables 返回所有 当前计算图中 在获取变量时未标记 trainable=False 的变量集合

从1.4版本开始可以支持传入scope，来获取指定scope中的变量集合