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 kerasfrom 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-= meanstd = train_data.std(axis = 0) train_data/= stdtest_data-= meantest_data/= std |
Code: Training a neural network model on it
from keras import modelsfrom 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_modelmodel.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 filemodel.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 weightsloaded_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 filemodel_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 filemodel.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 weightsloaded_model.load_weights("network.h5")loss, accuracy = loaded_model.evaluate(test_data, test_targets) |
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