03_linreg_starter.py

""" Starter code for simple linear regression example using placeholders
Created by Chip Huyen ([email protected])
CS20: "TensorFlow for Deep Learning Research"
cs20.stanford.edu
Lecture 03
"""
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import time

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf

import utils

DATA_FILE = 'data/birth_life_2010.txt'

# Step 1: read in data from the .txt file
data, n_samples = utils.read_birth_life_data(DATA_FILE)

# Step 2: create placeholders for X (birth rate) and Y (life expectancy)
# Remember both X and Y are scalars with type float
X, Y = None, None
#############################
########## TO DO ############
#############################

# Step 3: create weight and bias, initialized to 0.0
# Make sure to use tf.get_variable
w, b = None, None
#############################
########## TO DO ############
#############################

# Step 4: build model to predict Y
# e.g. how would you derive at Y_predicted given X, w, and b
Y_predicted = None
#############################
########## TO DO ############
#############################

# Step 5: use the square error as the loss function
loss = None
#############################
########## TO DO ############
#############################

# Step 6: using gradient descent with learning rate of 0.001 to minimize loss
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001).minimize(loss)

start = time.time()

# Create a filewriter to write the model's graph to TensorBoard
#############################
########## TO DO ############
#############################

with tf.Session() as sess:
    # Step 7: initialize the necessary variables, in this case, w and b
    #############################
    ########## TO DO ############
    #############################

    # Step 8: train the model for 100 epochs
    for i in range(100):
        total_loss = 0
        for x, y in data:
            # Execute train_op and get the value of loss.
            # Don't forget to feed in data for placeholders
            _, loss = ########## TO DO ############
            total_loss += loss

        print('Epoch {0}: {1}'.format(i, total_loss/n_samples))

    # close the writer when you're done using it
    #############################
    ########## TO DO ############
    #############################
    writer.close()
    
    # Step 9: output the values of w and b
    w_out, b_out = None, None
    #############################
    ########## TO DO ############
    #############################

print('Took: %f seconds' %(time.time() - start))

# uncomment the following lines to see the plot 
# plt.plot(data[:,0], data[:,1], 'bo', label='Real data')
# plt.plot(data[:,0], data[:,0] * w_out + b_out, 'r', label='Predicted data')
# plt.legend()
# plt.show()