Machine Learning - Save and load model

Here are trained how to save and load model AI, machine learning and depth into learn two differ slightly, because the depth of learning you want to save the entire network structure, slightly different

1. machine learning models use a way to preserve the python comes with pickle

import pickle

f = open('saved_model/rfc.pickle','wb')
pickle.dump (rfc, f) # 1 is a trained model parameters
f.close()
#load model
f = open('saved_model/rfc.pickle','rb')
rfc1 = pickle.load(f)
f.close()

 

2. Second way to save machine learning models using sklearn modules joblib

from sklearn.externals import joblib

joblib.dump(rfc, 'saved_model/rfc.pkl')
#load model
rfc2 = joblib.load('saved_model/rfc.pkl')

 

3.tensorflow way to preserve the depth learning model

Save =========== model

save_file = './model.ckpt'

saver = tf.train.Saver()

saver.save(sess, save_file)


Model loads ===========

saver = tf.train.Saver()

with tf.Session() as sess:
    # Load the weights and bias
    # Load weights and bias term
    saver.restore(sess, save_file)