tensorflow 강좌6. Linear Regression with multi-variables

++ Linear Regression with multi-variables

X의 feature들이 여러 개 x1, x2, ...

import tensorflow as tf

# y = x1*2+x2+x3 training

# W => 2,1,1, b=0

x_train = [[1,2,3],[2,3,4],[3,4,5],[4,5,6], [1,3,5]]

y_train = [[7],[11],[15],[19], [10]]

#placeholder

x = tf.placeholder(tf.float32, shape=[None,3])

y = tf.placeholder(tf.float32, shape=[None,1])

# variables

W = tf.Variable(tf.random_normal(shape=[3,1]), name='W')

b = tf.Variable(tf.random_normal(shape=[1]), name='b')

# model

hypothesis = tf.matmul(x, W) + b

cost = tf.reduce_mean( tf.square(hypothesis-y) )

# train

optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)

train = optimizer.minimize(cost)

sess = tf.Session()

sess.run(tf.global_variables_initializer())

# test

# hres= sess.run(hypothesis, feed_dict={x:x_train, y:y_train})

for i in range(3001):

    _, pcost, _W, _b= sess.run([train, cost, W, b], feed_dict={x:x_train, y:y_train})

    if i%500==0:

        print (i, pcost, _W, _b)

# test

print( sess.run(hypothesis, feed_dict={x: [[4, 2, 3]]}))