Hello. Trying to learn tensorflow so apologies if I’m missing something obvious. I can’t seem to make predictions on a loaded model.  My original model seems to work just find and I can get a prediction without issues. However, saving and loading that same model doesn’t seem to do what I would think.
Predicting with the original model gives me appropriate values while the loaded model gives me an error:

model.save(“Model”)
s = testFrame.head(1).drop([‘classification’,‘cloudProb’], axis=1).loc[0].to_dict()
loadedModel = tf.saved_model.load(‘Model’)
model(s)
loadedModel(s)

The above code works when passing ‘s’ into model but fails for the loadedModel.

ValueError: Could not find matching function to call loaded from the SavedModel. Got:
Positional arguments (2 total):
* {‘band2’: 346.0, ‘band3’: 625.0, ‘band4’: 443.0, ‘band8’: 2688.0}
* False
Keyword arguments: {}
Expected these arguments to match one of the following 4 option(s):
Option 1:
Positional arguments (2 total):
* {‘band4’: TensorSpec(shape=(None, 1), dtype=tf.float32, name=‘band4’), ‘band8’: TensorSpec(shape=(None, 1), dtype=tf.float32, name=‘band8’), ‘band3’: TensorSpec(shape=(None, 1), dtype=tf.float32, name=‘band3’), ‘band2’: TensorSpec(shape=(None, 1), dtype=tf.float32, name=‘band2’)}
* False
Keyword arguments: {}

… it goes on to give me all the options. What am I missing here? Thanks for the help.