Issue
I have recently saved some models which I have trained in another machine, and didn't save it like I have seen in another models, with the h5
extension. I don't grasp yet how to load the weights. I can load the model, but without the weights means like nothing. Please help :-)
from keras.models import load_model
from keras.models import model_from_json
model_LSTM_rendimiento = keras.models
model_LSTM_super = keras.models
model_LSTM_primero = keras.models
model_LSTM_rendimiento.load_model('../model_LSTM_rendimiento')
model_LSTM_super.load_model('../model_LSTM_super')
model_LSTM_primero.load_model('../model_LSTM_primero')
model_LSTM_primero.load_weights('../model_LSTM_primero_weights')
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/tmp/ipykernel_186379/3422008780.py in <module>
12 # model_LSTM_super.load_weights('../model_LSTM_super_weights')
13 model_LSTM_primero.load_model('../model_LSTM_primero')
---> 14 model_LSTM_primero.load_weights('../model_LSTM_primero_weights')
AttributeError: module 'keras.models' has no attribute 'load_weights'
Solution
Since you haven't saved the model in the h5 format, I'll assume you used the SavedModel format like this:
model.save('path/to/location')
If this is what you did, then loading the model like this is enough:
model = keras.models.load_model('path/to/location')
You don't have to load the weights separately; from the SavedModel documentation:
SavedModel is the more comprehensive save format that saves the model architecture, weights, and the traced Tensorflow subgraphs of the call functions. This enables Keras to restore both built-in layers as well as custom objects.
Your code:
from tensorflow import keras
model_LSTM_rendimiento = keras.models.load_model('../model_LSTM_rendimiento')
model_LSTM_super = keras.models.load_model('../model_LSTM_super')
model_LSTM_primero = keras.models.load_model('../model_LSTM_primero')
Answered By - ClaudiaR
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