file_input_multi_variable_linear_regression.py

# file_input_multi_variable_linear_regression.py

import tensorflow as tf
import numpy as np

xy = np.loadtxt('Data/data-01-test-score.csv', delimiter=',')
x_data = xy[:, 0:-1] ####### 원래 xy[0:25, 0:-1] --> 행 : 앞 열 : 뒤 #######
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(10001):
    sess.run(train, feed_dict={X:x_data, Y:y_data})
    if step % 20 == 0:
        print(step, sess.run(cost, feed_dict={X:x_data, Y:y_data}))

x_data = [[73.,80.,75.],
          [93.,88.,93.],
          [89.,91.,90.],
          [96.,98.,100.],
          [73.,66.,70.]]
print(sess.run(hypothesis, feed_dict={X:x_data}))