# 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,feed_dict={X:X_data , y:y_data[:,None]})
        vals.append(val)

print(vals)

[None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
...
None]