Chapter9n.py

from __future__ import division, print_function, unicode_literals               # Needed for interations/loops to work properly!!!
import os
import tensorflow as tf
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.preprocessing import StandardScaler
from datetime import datetime


housing = fetch_california_housing()
m, n = housing.data.shape
housing_data_with_bias = np.c_[np.ones((m,1)), housing.data]

scaler = StandardScaler()
scaled_housing_data = scaler.fit_transform(housing.data)
scaled_housing_data_with_bias = np.c_[np.ones((m, 1)), scaled_housing_data]

def fetch_batch(epoch, batch_index, batch_size):
    np.random.seed(epoch * n_batches + batch_index)
    indices = np.random.randint(m, size=batch_size)
    X_batch = scaled_housing_data_with_bias[indices]
    y_batch = housing.target.reshape(-1, 1)[indices]
    return X_batch, y_batch

now = datetime.utcnow().strftime("%Y%m%d%H%M%S")                                # Include a time stamp in the log directory name...
root_logdir = "tf_logs"                                                         # This will stop TensorBoard from merging stats from different runs.
logdir = "{}/run-{}/".format(root_logdir, now)

learning_rate = 0.01

X = tf.placeholder(tf.float32, shape=(None, n + 1), name="X")
y = tf.placeholder(tf.float32, shape=(None, 1), name="y")
theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), 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.GradientDescentOptimizer(learning_rate=learning_rate)
training_op = optimizer.minimize(mse)

init = tf.global_variables_initializer()                                        # Initialize all variables

mse_summary = tf.summary.scalar('MSE', mse)
file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())

n_epochs = 10
batch_size = 100
n_batches = int(np.ceil(m / batch_size))

with tf.Session() as sess:                                                      # Open and run a TensorFlow session
    sess.run(init)

    for epoch in range(n_epochs):
        for batch_index in range(n_batches):
            X_batch, y_batch = fetch_batch(epoch, batch_index, batch_size)
            if batch_index % 10 == 0:
                summary_str = mse_summary.eval(feed_dict={X: X_batch, y: y_batch})
                step = epoch * n_batches + batch_index
                file_writer.add_summary(summary_str, step)
            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})

    best_theta = theta.eval()

file_writer.close()
print(best_theta)