Save and load weights in keras

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)

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For loading weights, you need to have a model first. It must be:

existingModel.save_weights('weightsfile.h5')
existingModel.load_weights('weightsfile.h5')     

If you want to save and load the entire model (this includes the model's configuration, it's weights and the optimizer states for further training):

model.save_model('filename')
model = load_model('filename')

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Since this question is quite old, but still comes up in google searches, I thought it would be good to point out the newer (and recommended) way to save Keras models. Instead of saving them using the older h5 format like has been shown before, it is now advised to use the SavedModel format, which is actually a dictionary that contains both the model configuration and the weights.

More information can be found here: https://www.tensorflow.org/guide/keras/save_and_serialize

The snippets to save & load can be found below:

model.fit(test_input, test_target)
# Calling save('my_model') creates a SavedModel folder 'my_model'.
model.save('my_model')

# It can be used to reconstruct the model identically.
reconstructed_model = keras.models.load_model('my_model')

A sample output of this :

https://i.stack.imgur.com/tllIL.png

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Loading model from scratch requires you to build model from scratch, so you can try saving your model architecture first using model.to_json()

model_architecture = model.to_json()

Save model weighs using

model.save_weights('model_weights.h5')
       

For loading the weights you need to reconstruct your model using the saved json file first.

from tensorflow.keras.models import model_from_json
model = model_from_json(model_architecture) 

Then load the weights using

model.load_weights('model_weights.h5') 

You can now Compile and test the model , No need to retrain eg

model.compile(loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
          optimizer=keras.optimizers.Adam(lr=0.001), metrics=["accuracy"])

model.evaluate(x_test, y_test, batch_size=32, verbose=2)

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