title: Multiple Regression on Tensorflow
body: <p>I'm really new at StackOverflow, so please forgive me if I say something stupid. I was coding this multiple linear regression in tensorflow library but due to some reason it just doesn't work, the loss just increases and then becomes none.</p>
  
  <pre><code># coding: utf-8
  
  import numpy as np
  import tensorflow as tf
  import matplotlib.pyplot as plt
  import seaborn as sns
  get_ipython().magic('matplotlib inline')
  
  from sklearn.datasets import load_boston    
  
  data=load_boston()
  
  X_data = data.data
  y_data = data.target
  m = len(X_data)
  n = len(X_data[0])
  
  X = tf.placeholder(tf.float32,[m,n])
  y = tf.placeholder(tf.float32,[m,1])
  
  W = tf.Variable(tf.ones([n,1]))
  b = tf.Variable(tf.ones([1]))
  
  y_ = tf.matmul(X,W)+b
  
  loss = tf.reduce_mean(tf.square( y - y_))
  
  optimizer = tf.train.GradientDescentOptimizer(0.01)
  train = optimizer.minimize(loss)
  
  with tf.Session() as sess:
      init = tf.global_variables_initializer()
      sess.run(init)
      vals = []
      for i in range(100):
          val = sess.run([train,loss],feed_dict={X:X_data , y:y_data[:,None]})
          vals.append(val)
  
  print(vals)
  </code></pre>
  
  <p>Output is :</p>
  
  <pre><code>[[None, 823712.88],
   [None, 3.2238912e+13],
   [None, 1.2631078e+21],
   [None, 4.9488092e+28],
   [None, 1.9389255e+36],
   [None, inf],
   [None, inf],
   [None, inf],
   [None, inf],
   [None, inf],
   [None, inf],
   [None, nan],
   [None, nan],
   [None, nan],
   [None, nan],
   [None, nan],
  ...
   [None, nan],
   [None, nan]]
  </code></pre>
  
  <p>I can't find where it's going wrong...Help? Anyone?</p>