xxxxxxxxxx
# To save the model:
from keras.models import save_model
# you can write whatever you desire instead of 'my_model'
# model = Your trained model
model.save('my_model')
# To load the model:
from keras.models import load_model
reconstructed_model = load_model("my_model")
xxxxxxxxxx
# Save the weights
model.save_weights('./checkpoints/my_checkpoint')
# Create a new model instance
model = create_model()
# Restore the weights
model.load_weights('./checkpoints/my_checkpoint')
xxxxxxxxxx
keras_model_path = "/tmp/keras_save"
model.save(keras_model_path)
restored_keras_model = tf.keras.models.load_model(keras_model_path)