ML - Saving a Deep Learning model in Keras

Training a neural network/deep learning model usually takes a lot of time, particularly if the hardware capacity of the system doesn’t match up to the requirement. Once the training is done, we save the model to a file. To reuse the model at a later point of time to make predictions, we load the saved model.
Through Keras, models can be saved in three formats:

YAML format
JSON format
HDF5 format

YAML and JSON files store only model structure, whereas, HDF5 file stores complete neural network model along with structure and weights. Therefore, if the model structure is saved using YAML or JSON format, weights should be stored in an HDF5 file to store the entire model.
Considering Boston house prices dataset:
Code: Loading the dataset and preprocessing the data

import keras 
from keras.datasets import boston_housing 
  
(train_data, train_targets), (test_data, test_targets)= boston_housing.load_data() 
  
mean = train_data.mean(axis = 0) 
train_data-= mean 
std = train_data.std(axis = 0) 
  
train_data/= std 
test_data-= mean 
test_data/= std 

Code: Training a neural network model on it

from keras import models 
from keras import layers 
  
model = models.Sequential() 
model.add(layers.Dense(64, activation ="relu", input_shape =(train_data.shape[1], ))) 
model.add(layers.Dense(64, activation ="relu")) 
model.add(layers.Dense(1)) 
model.compile(optimizer ="rmsprop", loss ="mse", metrics =["mae"]) 
loss, accuracy = model.evaluate(test_data, test_targets) 

Output:

Code: Saving and reloading model in HDF5 file format

from keras.models import load_model 
model.save("network.h5") 
loaded_model = load_model("network.h5") 
loss, accuracy = loaded_model.evaluate(test_data, test_targets) 

Output:

Code: Saving and reloading model in JSON file format

# Saving model structure to a JSON file 
  
model_json = model.to_json() # with open("network.json", "w") as json_file: 
    json_file.write(model_json) 
  
# Saving weights of the model to a HDF5 file 
model.save_weights("network.h5") 
  
# Loading JSON file  
json_file = open("network.json", 'r') 
loaded_model_json = json_file.read() 
json_file.close() 
loaded_model = model_from_json(loaded_model_json) 
  
# Loading weights 
loaded_model.load_weights("network.h5") 
loss, accuracy = loaded_model.evaluate(test_data, test_targets) 

Code: Saving and reloading model in YAML file format

# Saving model structure to a YAML file 
model_yaml = model.to_yaml()  
with open("network.yaml", "w") as yaml_file: 
    yaml_file.write(model_yaml) 
  
# Saving weights of the model to a HDF5 file 
model.save_weights("network.h5") 
  
# Loading YAML file  
yaml_file = open("network.yaml", 'r') 
loaded_model_yaml = yaml_file.read() 
yaml_file.close() 
loaded_model = model_from_yaml(loaded_model_yaml) 
  
# Loading weights 
loaded_model.load_weights("network.h5") 
loss, accuracy = loaded_model.evaluate(test_data, test_targets) 

My Personal Notes

ML – Saving a Deep Learning model in Keras

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