tensorflow - cannot restore model - "Couldn't match files for checkpoint"

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Here is my model saved to disk:

import tensorflow as tf
import numpy as np


BATCH_SIZE = 3
VECTOR_SIZE = 1
LEARNING_RATE = 0.1

x = tf.placeholder(tf.float32, [BATCH_SIZE, VECTOR_SIZE],
                   name='input_placeholder')
y = tf.placeholder(tf.float32, [BATCH_SIZE, VECTOR_SIZE],
                   name='labels_placeholder')

W = tf.get_variable('W', [VECTOR_SIZE, BATCH_SIZE])
b = tf.get_variable('b', [VECTOR_SIZE], initializer=tf.constant_initializer(0.0))

y_hat = tf.matmul(W, x) + b
predict = tf.add(tf.matmul(W, x), b, name='predict')
total_loss = tf.reduce_mean(y-y_hat)
train_step = tf.train.AdagradOptimizer(LEARNING_RATE).minimize(total_loss)
X = np.ones([BATCH_SIZE, VECTOR_SIZE])
Y = np.ones([BATCH_SIZE, VECTOR_SIZE])
all_saver = tf.train.Saver() 

sess= tf.Session()
sess.run(tf.global_variables_initializer())
sess.run([train_step], feed_dict = {x: X, y:Y})
save_path = r'C:\tmp\tmp\\'
all_saver.save(sess,save_path)

While trying to restore

checkpoint_path = r'C:\tmp\tmp\\'
tf.train.latest_checkpoint(checkpoint_path)

I am getting the following error message:

ERROR:tensorflow:Couldn't match files for checkpoint C:\tmp\tmp\\

In C:\tmp\tmp\ I have the following files:

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

Any thoughts?

Are the files just named line that? starting with dot?

If that is the case you should consider to save them differently because this could be the problem.

Try with:

NUMBER_OF_CKPT = 60 saver.save(sess,save_path,global_step=NUMBER_OF_CKPT)

What is usually done is to save also the global_step as the number of the ckpt.

Hope to have solved it!

Error Restoring Model in Tensorflow After Changing the Optimizer , To begin with, I build a model in tensorflow, and then I saved the graph, with variables into a checkpoint through: saver = tf.train.Saver()� System information OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux on Google Cloud instance TensorFlow installed from (source or binary): binary (pip) TensorFlow version (use command

From saver.save tensorflow api:

save_path: String. Path to the checkpoint filename. If the saver is sharded, this is the prefix of the sharded checkpoint filename.

In save_path you didn't specify checkpoint filename.

For future use, try setting: checkpoint_path = r'C:\tmp\tmp\my-model'.

If you want to load your previously saved model, do the following:

  1. prepend the string my-model for these files:
.data-00000-of-00001
.index
.meta
  1. modify checkpoint file such that it will point to your checkpoint:
model_checkpoint_path: "C:\tmp\tmp\my-model"
all_model_checkpoint_paths: "C:\tmp\tmp\my-model"

Loading the checkpoint should be now possible.

tf.train.saver.restore failed error � Issue #383 � tensorflow/tensorflow , Cause training a model is time consuming, So Save a Checkpoint on training, but error occurred when to restore. The saver.restore says as� WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore().expect_partial(), to silence these warnings, or use assert_consumed() to

FWIW I saw this error while training a custom estimator on AI Platform (Cloud ML Engine). The issue for me was caused by the region of the GCS bucket where I was saving the checkpoints/model metadata.

When the region of this bucket was set to us (multiple regions in United States) I saw this error during evaluation. Setting the region of the GCS bucket to the same region where the AI Platform job was running (us-central1 (Iowa) in my case) resolved the issue.

Problem in restore a previously saved model � Issue #2999 , I used tensorflow 0.9. I want save my model to be reused with that, I simply add tf. train.save() to save and restore my training variables. This is� Update: This popular article shows how to save and restore models in Tensorflow 1.x. If you want to learn the same with Tensorflow2.x, please go to this article that explains how to save and restore Tensorflow 2.x models.

TensorFlow: Save and Restore Models, This is the case for any deep learning platform, as for TensorFlow. In this post we look at saving and restoring a TensorFlow model, which we describe some of� This is because model is saved in the dir directory which is retrieved in the file restore_model_tensorflow.py. Running python file to save the model. python/python3 save_model_tensorflow.py Running python file to restore model. python/python3 restore_model_tensorflow.py Some External Links to be used to remove the errors while creating the code.

Training checkpoints, keras guide on saving and restoring. tf.keras.Model.save_weights saves a TensorFlow checkpoint. net.save_weights(� WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore().expect_partial(), to silence these warnings, or use assert_consumed() to

Saving a fully-functional model is very useful—you can load them in TensorFlow.js (Saved Model, HDF5) and then train and run them in web browsers, or convert them to run on mobile devices using TensorFlow Lite (Saved Model, HDF5) *Custom objects (e.g. subclassed models or layers) require special attention when saving and loading.

Comments
  • Your solution probably works. What I did, I simply wrote r'C:\tmp\tmp\prefix_name'. prefix_name is treated as prefix name and not as part of path in tensorflow.