MLlab04-2.py

import numpy as np
import tensorflow as tf
tf.set_random_seed(777)
xy = np.loadtxt('data-01-test-score.csv',delimiter=',', dtype=np.float32)
x_data = xy[:,0:-1]
y_data = xy[:,[-1]]

print(x_data.shape, x_data, len(x_data))
print(y_data.shape, y_data, len(y_data))

X = tf.placeholder(tf.float32,shape=[None, 3])
Y = tf.placeholder(tf.float32, shape=[None, 1])

W = tf.Variable(tf.random_normal([3,1]), name = 'weight')
b = tf.Variable(tf.random_normal([1]), name = 'bias')

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

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

optimizer = tf.train.GradientDescentOptimizer(learning_rate=1e-5)
train = optimizer.minimize(cost)

sess = tf.Session()
sess.run(tf.global_variables_initializer())

for step in range(2001):
    cost_val, hy_val, _ = sess.run([cost, hypothesis, train],feed_dict={X : x_data, Y : y_data})
    if step % 20 == 0 :
        print(step, "Cost : ", cost_val, "\nPrediction:\n", hy_val)
print("Your score will be ", sess.run(hypothesis,feed_dict={X:[[100,70,101]]}))
print("Other score will be ", sess.run(hypothesis,feed_dict={X:[[60,70,110],[90,100,80]]}))