- 주어진 x와 y 값을 가지고 서로 간의 관계를 파악
- 새로운 x값이 주어졌을 때 y값을 쉽게 알 수 있음
In [1]:
import tensorflow as tf
In [2]:
x_data = [1, 2, 3]
y_data = [1, 2, 3]
data 생성¶
In [3]:
W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
b = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
placeholder 설정¶
In [4]:
X = tf.placeholder(tf.float32, name="X")
Y = tf.placeholder(tf.float32, name="Y")
model 설정¶
In [5]:
model = W*X + b
cost function¶
In [6]:
cost = tf.reduce_mean(tf.square(model- Y))
gradient desent¶
In [7]:
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1)
train_op = optimizer.minimize(cost)
modeling¶
In [8]:
sess = tf.Session()
sess.run(tf.global_variables_initializer())
for step in range(500):
_, cost_val = sess.run([train_op, cost], feed_dict={X: x_data, Y: y_data})
if step % 25 == 0:
print("step: {}, cost_val: {:.5f}, W: {}, b: {}".format(step, cost_val, sess.run(W), sess.run(b)))
test¶
In [9]:
class prediction:
def run(self, input):
self.input = input
output = sess.run(model, feed_dict={X: self.input})
print("X: {}, Y-result: {}".format(self.input, output))
pred = prediction()
In [10]:
pred.run(2.5)
pred.run(5)
pred.run(10)
In [11]:
from IPython.core.display import HTML, display
display(HTML("<style> .container{width:100% !important;}</style>"))
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