问题
saver = tf.train.import_meta_graph(filepath)
tf.reset_default_graph()
sess = tf.Session()
saver.restore(sess, tf.train.latest_checkpoint('/home/deep_learning_tests/tensorflow/'))
Ok, the code is simple. And loading tensorflow model with the original path works perfectly.
But the problem is that if I move the tensorflow model (including .index, .meta, checkpoint) to other path it gives error
tensorflow.python.framework.errors_impl.NotFoundError: /home/deep_learning_tests/tensorflow/d:/labtest/tensorflow; No such file or directory
It tries to find the original file path. If the original file path still has the model (meaning model is only copied to the new directory), it works. But if the original file directory is deleted and only new directory remains, it gives the above error.
How can I load the tensorflow model that is moved from the original directory?
回答1:
Just want to add something. I used the
saver.restore(sess, 'path_to_dir') to restore, it didn't work. I don't have .ckpt file in path_to_dir, what I have are 3 files: model_name.index, model_name.meta, model_name.data. So the way I solved my problem is to use
saver.restore(sess, 'path_to_dir/model_name').
回答2:
Ok, I finally managed how to load the moved/copied model.
If you are using
tf.train.latest_checkpoint
then the loading target file must be in the same directory as it was created. Otherwise you must open the file 'checkpoint' and modify the directory path in the file. It works but not recommended.
My recommendation is that DO NOT USE
tf.train.latest_checkpoint
to load the model when the saved model is moved or copied to another directory / system.
Just use this
saver.restore(sess, 'path/to/file')
Then it will load the model.
To be clear, if you are trying to load like below
saver = tf.train.import_meta_graph(filepath)
tf.reset_default_graph()
sess = tf.Session()
saver.restore(sess, tf.train.latest_checkpoint('file/path/to/new/directory'))
then you must modify 'checkpoint' file to the new directory path.
Otherwise, just do
saver = tf.train.import_meta_graph(filepath)
tf.reset_default_graph()
sess = tf.Session()
saver.restore(sess, 'file/path/to/new/directory')
========================================================== I found another problem that people should know. Somehow, if I train in windows (didn't check linux or mac osx), checkpoint file writes its path with absolute path.
So if you are trying to load the model from some other system it will not find the correct directory path to load since it is looking for the absolute directory path written by windows directory system (which starts with c:/ or d:/ etc)
My checkpoint example is as follow.
model_checkpoint_path: "d:/Projects_data/emulator_data/NEW/cnn_21category_char\tf_ckpt\_loss_1.357984_accuracy_0.5358-2700" all_model_checkpoint_paths: "d:/Projects_data/emulator_data/NEW/cnn_21category_char\tf_ckpt\_loss_1.403583_accuracy_0.5247-1500" all_model_checkpoint_paths: "d:/Projects_data/emulator_data/NEW/cnn_21category_char\tf_ckpt\_loss_1.385835_accuracy_0.5302-1800" all_model_checkpoint_paths: "d:/Projects_data/emulator_data/NEW/cnn_21category_char\tf_ckpt\_loss_1.375068_accuracy_0.5334-2100" all_model_checkpoint_paths: "d:/Projects_data/emulator_data/NEW/cnn_21category_char\tf_ckpt\_loss_1.359645_accuracy_0.5363-2400" all_model_checkpoint_paths: "d:/Projects_data/emulator_data/NEW/cnn_21category_char\tf_ckpt\_loss_1.357984_accuracy_0.5358-2700"
If you want it to be read, then you must change it to relative path like below:
model_checkpoint_path: "_loss_1.357984_accuracy_0.5358-2700" all_model_checkpoint_paths: "_loss_1.403583_accuracy_0.5247-1500" all_model_checkpoint_paths: "_loss_1.385835_accuracy_0.5302-1800" all_model_checkpoint_paths: "_loss_1.375068_accuracy_0.5334-2100" all_model_checkpoint_paths: "_loss_1.359645_accuracy_0.5363-2400" all_model_checkpoint_paths: "_loss_1.357984_accuracy_0.5358-2700"
Then it will work. So I would recommend to check the checkpoint file if it is written as the absolute path.
来源：https://stackoverflow.com/questions/50391464/load-tensorflow-model-from-moved-directory