Just use tensorflow Saver class to save the graph in checkpoints.
saver = tf.train.Saver() # ... with tf.Session() as sess: # your code here ... save_path = saver.save(sess, "/path/model.ckpt") print("Model saved in path: %s" % save_path)
and load with this way
# 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, "/path/model.ckpt") print("Model restored.")
Here is the doc of tensorflow https://www.tensorflow.org/guide/saved_model
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