问题
I just finished training my model only to find out that I exported a model for serving that had problems with the signatures. How do I update them?
(One common problem is setting the wrong shape for CloudML Engine).
回答1:
Don't worry -- you don't need to retrain your model. That said, there is a little work to be done. You're going to create a new (corrected) serving graph, load the checkpoints into that graph, and then export this graph.
For example, suppose you add a placeholder, but didn't set the shape, even though you meant to (e.g., to run on CloudML). In that case, your graph may have looked like:
x = tf.placeholder(tf.float32)
y = foo(x)
...
To correct this:
# Create the *correct* graph
with tf.Graph().as_default() as new_graph:
x = tf.placeholder(tf.float32, shape=[None])
y = foo(x)
saver = tf.train.Saver()
# (Re-)define the inputs and the outputs.
inputs = {"x": tf.saved_model.utils.build_tensor_info(x)}
outputs = {"y": tf.saved_model.utils.build_tensor_info(y)}
signature = tf.saved_model.signature_def_utils.build_signature_def(
inputs=inputs,
outputs=outputs,
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME
)
with tf.Session(graph=new_graph) as session:
# Restore the variables
vars_path = os.path.join(old_export_dir, 'variables', 'variables')
saver.restore(session, vars_path)
# Save out the corrected model
b = builder.SavedModelBuilder(new_export_dir)
b.add_meta_graph_and_variables(session, ['serving_default'], signature)
b.save()
来源:https://stackoverflow.com/questions/42801551/how-do-i-change-the-signatures-of-my-savedmodel-without-retraining-the-model