NAIAHD

Linear Regression 의 cost 최소화의 TensorFlow 구현(new)

X = [1, 2, 3]
Y = [1, 2, 3]

W= tf.Variable(5.0) # 말도 안되는 값을 준다.
hypothesis = X * W
gradient = tf.reduce_mean((W * X - Y) * X) * 2

cost = tf.reduce_mean(tf.square(hypothesis - Y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)

gvs = optimizer.compute_gradients(cost,[W])
apply_gradients = optimizer.apply_gradients(gvs)

sess = tf.Session()
sess.run(tf.global_variables_initializer())
for step in range(101):
    print(step, sess.run([gradient, W,gvs]))
    sess.run(apply_gradients)

gvs = optimizer.compute_gradients(cost) -> gvs = optimizer.compute_gradients(cost,[W]) 으로 수정

참고 :

https://kswims.tistory.com/97