Generally, a deep learning model takes a large amount of time to train, so
Lets first create a model in Keras. This is a simple autoencoder model. If you need to know more about autoencoders please refer this blog.
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 |
from keras.models import Model from keras.layers import Dense, Input import matplotlib.pyplot as plt import numpy as np # creating an autoencoder model input_shape = Input(shape = (784,)) dense1 = Dense(512, activation = 'relu')(input_shape) dense2 = Dense(256, activation = 'relu')(dense1) encoded = Dense(32, activation = 'relu')(dense2) dense3 = Dense(256, activation = 'relu')(encoded) dense4 = Dense(512, activation = 'relu')(dense3) decoded = Dense(784, activation = 'relu')(dense4) autoencoder = Model(input_shape, decoded) m = 256 n_epoch = 25 autoencoder.compile(optimizer='adam', loss='mse') autoencoder.fit(output_X_train,output_X_train, epochs=n_epoch, batch_size=m, shuffle=True) |
Above we have created a Keras model named as “autoencoder“. Now lets see how to save this model.
Saving and loading only architecture of a model
In keras, you can save and load architecture of a model in two formats: JSON or YAML Models generated in these two format are human readable and can be edited if needed.
|
1 2 3 4 5 6 7 8 9 10 11 |
# saving and loading model architecture in json format # saving in json format json_model = autoencoder.to_json() json_file = open('autoencoder_json.json', 'w') json_file.write(json_model) # loading model architecture from json file from keras.models import model_from_json json_file = open('autoencoder_json.json', 'r') json_model = model_from_json(json_file.read()) |
|
1 2 3 4 5 6 7 8 9 10 11 |
# saving and loading model architecture in yaml format # saving in yaml format yaml_model = autoencoder.to_yaml() yaml_file = open('autoencoder_yaml.yaml', 'w') yaml_file.write(yaml_model) # loading model architecture from yaml file from keras.models import model_from_yaml yaml_file = open('autoencoder_yaml.yaml', 'r') yaml_model = model_from_yaml(yaml_file.read()) |
Saving and Loading Weights of a Keras Model
With model architecture you will also need model weights to predict output from trained model.
|
1 2 3 4 5 |
# saving model weights autoencoder.save_weights('autoencoder_weights.h5') # loading weights of a keras model json_model.load_weights('autoencoder_weights.h5') |
Saving and Loading Both Architecture and Weights in one File
|
1 2 3 4 5 6 |
# saving whole model autoencoder.save('autoencoder_model.h5') # loading whole model from keras.models import load_model model1 = load_model('autoencoder_model.h5') |
This will save following four parameters in “autoencoder_model.h5” file:
- Model Architecture
- Model Weights
- Loss and Optimizer
- State of the optimizer allowing to resume training where you left.
Hope you enjoy reading.
If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.