03-4 Minimizing Cost tf gradient

■ 직접구한 Minimizing cost 와 텐서플로우 사용 Minimizing cost 비교

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

- gradient 값을 얻어오는 함수

apply_graidents = optimizer.apply_gradients(gvs)

- gradient 값을 통하여 W 값을 얻어내기 위한 그래프를 생성함

sess.run(apply_gradients)

- apply_gradients를 실행하여 W 값을 얻어옴

# Lab03-X Minimizing Cost tf gradient
 
# This is optional
 
import tensorflow as tf
 
tf.set_random_seed(777)  # for reproducibility
 
 
# tf Graph Input
 
X = [1, 2, 3]
 
Y = [1, 2, 3]
 
 
# Set wrong model weights
 
W = tf.Variable(5.)
 
 
# Linear model
 
hypothesis = X * W
 
 
# Manual gradient
 
gradient = tf.reduce_mean((W * X - Y) * X) * 2
 
 
# cost/loss function
 
cost = tf.reduce_mean(tf.square(hypothesis - Y))
 
 
# Minimize: Gradient Descent Magic
 
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
 
train = optimizer.minimize(cost)
 
 
# Get gradients
 
gvs = optimizer.compute_gradients(cost, [W])
 
# Optional: modify gradient if necessary
 
# gvs = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gvs]
 
# Apply gradients
 
apply_gradients = optimizer.apply_gradients(gvs)
 
 
# Launch the graph in a session.
 
sess = tf.Session()
 
# Initializes global variables in the graph.
 
sess.run(tf.global_variables_initializer())
 
 
for step in range(100):
 
    print(step, sess.run([gradient, W, gvs]))
 
    sess.run(apply_gradients)
 
    # Same as sess.run(train)