是否有可能恢复一个变量,只有当它存在时?这样做最惯用的方法是什么?
例如,考虑以下最小示例:
import tensorflow as tf
import glob
import sys
import os
with tf.variable_scope('volatile'):
x = tf.get_variable('x', initializer=0)
with tf.variable_scope('persistent'):
y = tf.get_variable('y', initializer=0)
add1 = tf.assign_add(y, 1)
saver = tf.train.Saver(tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, 'persistent'))
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
tf.get_default_graph().finalize()
print('save file', sys.argv[1])
if glob.glob(sys.argv[1] + '*'):
saver.restore(sess, sys.argv[1])
print(sess.run(y))
sess.run(add1)
print(sess.run(y))
saver.save(sess, sys.argv[1])
当使用相同的参数运行两次时,程序首先打印 0\n1
,然后按预期打印 1\n2
。现在假设您更新代码以获得新功能,方法是在 persistent
范围。当存在旧的保存文件时再次运行此命令将中断以下内容:
NotFoundError (see above for traceback): Key persistent/z not found in checkpoint
[[Node: save/RestoreV2_1 = RestoreV2[dtypes=[DT_INT32],
_device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0,
save/RestoreV2_1/tensor_names,
save/RestoreV2_1/shape_and_slices)]]
[[Node: save/Assign_1/_18 = _Recv[client_terminated=false,
recv_device="/job:localhost/replica:0/task:0/device:GPU:0",
send_device="/job:localhost/replica:0/task:0/device:CPU:0",
send_device_incarnation=1,
tensor_name="edge_12_save/Assign_1",
tensor_type=DT_FLOAT,
_device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]
最佳答案
您可以使用以下函数进行恢复(取自 here ):
def optimistic_restore(session, save_file, graph=tf.get_default_graph()):
reader = tf.train.NewCheckpointReader(save_file)
saved_shapes = reader.get_variable_to_shape_map()
var_names = sorted([(var.name, var.name.split(':')[0]) for var in tf.global_variables()
if var.name.split(':')[0] in saved_shapes])
restore_vars = []
for var_name, saved_var_name in var_names:
curr_var = graph.get_tensor_by_name(var_name)
var_shape = curr_var.get_shape().as_list()
if var_shape == saved_shapes[saved_var_name]:
restore_vars.append(curr_var)
opt_saver = tf.train.Saver(restore_vars)
opt_saver.restore(session, save_file)
我通常运行 sess.run(tf.global_variables_initializer())
以确保所有变量都已初始化,然后我运行 optimistic_restore(sess,...)
恢复可以恢复的变量。
关于python - TensorFlow - 如果存在则恢复,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/47997203/