03_linear_regression_starter.py

""" Simple linear regression example in TensorFlow
This program tries to predict the number of thefts from 
the number of fire in the city of Chicago
Author: Chip Huyen
Prepared for the class CS 20SI: "TensorFlow for Deep Learning Research"
cs20si.stanford.edu
"""
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'

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

import utils

DATA_FILE = 'data/fire_theft.xls'

# Phase 1: Assemble the graph
# Step 1: read in data from the .xls file
book = xlrd.open_workbook(DATA_FILE, encoding_override='utf-8')
sheet = book.sheet_by_index(0)
data = np.asarray([sheet.row_values(i) for i in range(1, sheet.nrows)])
n_samples = sheet.nrows - 1

# Step 2: create placeholders for input X (number of fire) and label Y (number of theft)
# Both have the type float32


# Step 3: create weight and bias, initialized to 0
# name your variables w and b


# Step 4: predict Y (number of theft) from the number of fire
# name your variable Y_predicted


# Step 5: use the square error as the loss function
# name your variable loss


# Step 6: using gradient descent with learning rate of 0.01 to minimize loss
 
# Phase 2: Train our model
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 i in range(50): # run 100 epochs
		total_loss = 0
		for x, y in data:
			# Session runs optimizer to minimize loss and fetch the value of loss. Name the received value as l
			# TO DO: write sess.run()

			total_loss += l
		print("Epoch {0}: {1}".format(i, total_loss/n_samples))
	
# plot the results
# X, Y = data.T[0], data.T[1]
# plt.plot(X, Y, 'bo', label='Real data')
# plt.plot(X, X * w + b, 'r', label='Predicted data')
# plt.legend()
# plt.show()