Tensorflow - saving and restoring from different folders

Question

I created and saved simple nn in tensorflow:

import tensorflow as tf
import numpy as np

x = tf.placeholder(tf.float32, [1, 1],name='input_placeholder')
y = tf.placeholder(tf.float32, [1, 1],name='input_placeholder')
W = tf.get_variable('W', [1, 1])
layer = tf.matmul(x, W, name='layer')
loss = tf.subtract(y,layer)
train_step = tf.train.AdagradOptimizer(0.1).minimize(loss, name='train_step')
all_saver = tf.train.Saver() 

sess = tf.Session()
sess.run(tf.global_variables_initializer())

x_test = np.zeros((1, 1))
y_test = np.zeros((1, 1))
some_output = sess.run([train_step],feed_dict = {x:x_test,y:y_test})

save_path = r'C:\Temp\tf_exp\save_folder\test'
all_saver.save(sess,save_path)

Then I took all files in C:\Temp\tf_exp\save_folder\ and moved them (exactly moved not copied) to C:\Temp\tf_exp\restore_folder. The files that I moved are:

checkpoint
test.data-00000-of-00001
test.index
test.meta

Then I tried to restore nn from new location:

meta_path = r'C:\Temp\tf_exp\restore_folder\test.meta'
checkpoint_path = r'C:\Temp\tf_exp\restore_folder\\'
print(checkpoint_path)
new_all_saver = tf.train.import_meta_graph(meta_path)
sess=tf.Session()
new_all_saver.restore(sess, tf.train.latest_checkpoint(checkpoint_path))
graph = tf.get_default_graph()
layer= graph.get_tensor_by_name('layer:0')
x=graph.get_tensor_by_name('input_placeholder:0')

Here is error that restore code generated:

C:\Temp\tf_exp\restore_folder\\
ERROR:tensorflow:Couldn't match files for checkpoint C:\Temp\tf_exp\save_folder\test
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-2-9af4e683fc4b> in <module>()
      5 new_all_saver = tf.train.import_meta_graph(meta_path)
      6 sess=tf.Session()
----> 7 new_all_saver.restore(sess, tf.train.latest_checkpoint(checkpoint_path))
      8 graph = tf.get_default_graph()
      9 layer= graph.get_tensor_by_name('layer:0')

~\AppData\Local\conda\conda\envs\tensorflow\lib\site-packages\tensorflow\python\training\saver.py in restore(self, sess, save_path)
   1555       return
   1556     if save_path is None:
-> 1557       raise ValueError("Can't load save_path when it is None.")
   1558     logging.info("Restoring parameters from %s", save_path)
   1559     sess.run(self.saver_def.restore_op_name,

ValueError: Can't load save_path when it is None.

How can I avoid it? What is the proper way of moving files around?

Update:

As I am searching for the answer, it looks like using relative path is the way to go. But I am not sure how to use relative path. Should I change Python's current working directory to where I save model data?

Answer 1

Just add save_relative_paths=True when creating tf.train.Saver():

# original code: all_saver = tf.train.Saver()
all_saver = tf.train.Saver(save_relative_paths=True)

Please refer to official doc for more details.

Answer 2

You could try to restore by:

with tf.Session() as sess:
    saver = tf.train.import_meta_graph(/path/to/test.meta)
    saver.restore(sess, "path/to/checkpoints/test")

In this case, because you put the name of the checkpoint is "test" so you got 3 files:

test.data-00000-of-00001
test.index
test.meta

Therefore, when you restore, you need to put the path to checkpoint folder + "/test". The system will automatically load the corresponding data and index files.

Answer 3

You could try to open the checkpoint file on notepad and edit it:

model_checkpoint_path: "Name-of-saver"
all_model_checkpoint_paths: "Name-of-saver"