Use .ckpt and .meta -files to make predictions with Inception in TensorFlow
<p>I used the Inception-library <em>(Tensorflow/models/Inception-tutorial on github)</em>, 
feeding selfmade TFRecord-files to imagenet_train.py, and ended up with a 7Gb directory containing .ckpt and .meta files.</p>

<p>How do i use them to make actual predictions?(also evaluating, testing, <strong>testing with actual .jpg files</strong>, continue the training)</p>

<p>What would be the next steps?Are there dummy-friendly examples(tutorials)?</p>

<p><em>[I know thereÂ´s a thread with a similar name, but it contains barely a question nor answers.]</em></p>

<p><a href="https://www.tensorflow.org/versions/r0.10/how_tos/variables/index.html#restoring-variables" rel="nofollow">https://www.tensorflow.org/versions/r0.10/how_tos/variables/index.html#restoring-variables</a> provides pretty much everything you should need.</p>

<p>The code there is:</p>

<pre><code># Add ops to save and restore all the variables.
saver = tf.train.Saver()

# Later, launch the model, use the saver to restore variables from disk, and
# do some work with the model.
with tf.Session() as sess:
  # Restore variables from disk.
  saver.restore(sess, "/tmp/model.ckpt")
  print("Model restored.")
  # Do some work with the model
  ...
</code></pre>

<p>So at the <code>...</code> part, you could load your .jpegs and feed them into the network with placeholders and feeddicts.  </p>

python machine-learning tensorflow