#!/usr/bin/env python
# coding: utf-8

# In[1]:


import urllib2   
import tensorflow as tf  
import numpy as np  
import findspark     
 
findspark.init()  
from pyspark import SparkContext, SparkFiles, SQLContext
 
if not 'sc' in locals():  
    sc = SparkContext()  
 
if not 'sqlContext' in locals():
 sqlContext = SQLContext(sc)
 
wordsDF = sqlContext.createDataFrame([('cat',), ('elephant',), ('rat',), ('rat',), ('cat', )], ['word'])   
wordCountsDF = wordsDF.groupBy("word").count()   
wordCountsDF.show() 
 
x_data = np.random.rand(100).astype(np.float32)  
y_data = x_data * 0.1 + 0.3  
 
W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))  
b = tf.Variable(tf.zeros([1]))  
y = W * x_data + b  
 
loss = tf.reduce_mean(tf.square(y - y_data))  
optimizer = tf.train.GradientDescentOptimizer(0.5)  
train = optimizer.minimize(loss)  
 
init = tf.initialize_all_variables()  
 
sess = tf.Session()  
sess.run(init)  
 
for step in range(201):  
    sess.run(train)  
    if step % 20 == 0:  
        print(step, sess.run(W), sess.run(b)) 


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