python - Save and load weights in keras

Question

Im trying to save and load weights from the model i have trained.

the code im using to save the model is.

TensorBoard(log_dir='/output')
model.fit_generator(image_a_b_gen(batch_size), steps_per_epoch=1, epochs=1)
model.save_weights('model.hdf5')
model.save_weights('myModel.h5')

Let me know if this an incorrect way to do it,or if there is a better way to do it.

but when i try to load them,using this,

from keras.models import load_model
model = load_model('myModel.h5')

but i get this error:

---

ValueError                                Traceback (most recent call 
last)
<ipython-input-7-27d58dc8bb48> in <module>()
      1 from keras.models import load_model
----> 2 model = load_model('myModel.h5')

/home/decentmakeover2/anaconda3/lib/python3.5/site-
packages/keras/models.py in load_model(filepath, custom_objects, compile)
    235         model_config = f.attrs.get('model_config')
    236         if model_config is None:
--> 237             raise ValueError('No model found in config file.')
    238         model_config = json.loads(model_config.decode('utf-8'))
    239         model = model_from_config(model_config, 
custom_objects=custom_objects)

ValueError: No model found in config file.

Any suggestions on what i may be doing wrong? Thank you in advance.

Answer 1

Here is a YouTube video that explains exactly what you're wanting to do: Save and load a Keras model

There are three different saving methods that Keras makes available. These are described in the video link above (with examples), as well as below.

First, the reason you're receiving the error is because you're calling load_model incorrectly.

To save and load the weights of the model, you would first use

model.save_weights('my_model_weights.h5')

to save the weights, as you've displayed. To load the weights, you would first need to build your model, and then call load_weights on the model, as in

model.load_weights('my_model_weights.h5')

Another saving technique is model.save(filepath). This save function saves:

• The architecture of the model, allowing to re-create the model.
• The weights of the model.
• The training configuration (loss, optimizer).
• The state of the optimizer, allowing to resume training exactly where you left off.

To load this saved model, you would use the following:

from keras.models import load_model
new_model = load_model(filepath)'

Lastly, model.to_json(), saves only the architecture of the model. To load the architecture, you would use

from keras.models import model_from_json
model = model_from_json(json_string)