Make a deep copy of a keras model in python
These days it's trivial:
model2 = tf.keras.models.clone_model(model1)
This will give you a new model, new layers, and new weights. If for some reason that doesn't work (I haven't tested it) this older solution will:
model1 = Model(...)
model1.compile(...)
model1.save(savepath) # saves compiled state
model2 = keras.models.load_model(savepath)
The issue is that model_copy is probably not compiled after cloning. There are in fact a few issues:
Apparently cloning doesn't copy over the loss function, optimizer info, etc.
Before compiling you need to also build the model.
Moreover, cloning doesn't copy weight over
So you need a couple extra lines after cloning. For example, for 10 input variables:
model_copy= keras.models.clone_model(model1)
model_copy.build((None, 10)) # replace 10 with number of variables in input layer
model_copy.compile(optimizer='rmsprop', loss='categorical_crossentropy')
model_copy.set_weights(model.get_weights())
Easier Method 1: Loading weights from file
If I understand your question correctly, there is an easier way to do this. You don't need to clone the model, just need to save the old_weights and set the weights at beginning of the loop. You can simply load weights from file as you are doing.
for _ in range(10):
model1= create_Model()
model1.compile(optimizer='rmsprop', loss='categorical_crossentropy')
model1.load_weights('my_weights')
for j in range(0, image_size):
model1.fit(sample[j], sample_lbl[j])
prediction= model1.predict(sample[j])
Easier Method 2: Loading weights from previous get_weights()
Or if you prefer not to load from file:
model1= create_Model()
model1.compile(optimizer='rmsprop', loss='categorical_crossentropy')
model1.load_weights('my_weights')
old_weights = model1.get_weights()
for _ in range(10):
model1.set_weights(old_weights)
for j in range(0, image_size):
model1.fit(sample[j], sample_lbl[j])
prediction= model1.predict(sample[j])