ML - Saving a Deep Learning model in Keras
Last Updated :
17 May, 2020
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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:
Python3 1==
Code: Training a neural network model on it
Python3 1==
Output:
Code: Saving and reloading model in HDF5 file format
Python3 1==
Output:
Python3 1==
Code: Saving and reloading model in YAML file format
Python3 1==
- YAML format
- JSON format
- HDF5 format
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
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)
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)
# 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)
# 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)
