ch9_saving_models.py

import tensorflow as tf
import numpy as np
from ch9_gradient_descent import load_data
from ch9_mini_batch_gradient_descent import fetch_batch


def main():

    print("Saving Models Example")
    X_train, y_train = load_data()
    m,n = X_train.data.shape

    batch_size = 5000
    n_batches = int(np.ceil(m / batch_size))
    n_epochs = 1000
    learning_rate = 0.01

    X = tf.placeholder(dtype=tf.float32, shape=(None, n), name="X")
    y = tf.placeholder(dtype=tf.float32, shape=(None, 1), name="y")

    theta = tf.Variable(tf.random_uniform([n, 1], -1.0, 1.0), name="theta")
    y_pred = tf.matmul(X, theta, name="predictions")

    error = y_pred - y
    mse = tf.reduce_mean(tf.square(error), name="mse")

    optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=0.9)

    training_op = optimizer.minimize(mse)
    init = tf.global_variables_initializer()

    #Create the Saver
    saver = tf.train.Saver()

    with tf.Session() as sess:
        sess.run(init)
        for epoch in range(n_epochs):
            for batch_index in range(n_batches):
                X_batch, y_batch = fetch_batch(X_train, y_train, batch_index, batch_size)
                sess.run(training_op, feed_dict={X: X_batch, y: y_batch})

            if epoch % 100 == 0:
                print("Epoch ", epoch, " MSE = ", mse.eval(feed_dict={X: X_batch, y: y_batch}))
                # Call the save function to save the model
                # save at intermediate points
                save_path = saver.save(sess,"tmp/my_model.ckpt")
        best_theta = theta.eval()
        print(best_theta)
        # save the final model
        save_path = saver.save(sess,"tmp/my_final_model.ckpt")

if __name__ == "__main__":
    main()