ML lab 03 - Linear Regression 의 cost 최소화의 TensorFlow 구현 (new)

lab-03-2-minimizing_cost_gradient_update.py

# Lab 3 Minimizing Cost
import tensorflow as tf

tf.set_random_seed(777)  # for reproducibility

x_data = [1, 2, 3]
y_data = [1, 2, 3]

# Try to find values for W and b to compute y_data = W * x_data
# We know that W should be 1
# But let's use TensorFlow to figure it out
W = tf.Variable(tf.random_normal([1]), name="weight")

X = tf.placeholder(tf.float32)
Y = tf.placeholder(tf.float32)

# Our hypothesis for linear model X * W
hypothesis = X * W

# cost/loss function
cost = tf.reduce_mean(tf.square(hypothesis - Y))

# Minimize: Gradient Descent using derivative: W -= learning_rate * derivative
learning_rate = 0.1
gradient = tf.reduce_mean((W * X - Y) * X)
descent = W - learning_rate * gradient
update = W.assign(descent)

# Launch the graph in a session.
with tf.Session() as sess:
    # Initializes global variables in the graph.
    sess.run(tf.global_variables_initializer())

    for step in range(21):
        _, cost_val, W_val = sess.run(
            [update, cost, W], feed_dict={X: x_data, Y: y_data}
        )
        print(step, cost_val, W_val)

Result

0 1.93919 [ 1.64462376]
1 0.551591 [ 1.34379935]
2 0.156897 [ 1.18335962]
3 0.0446285 [ 1.09779179]
4 0.0126943 [ 1.05215561]
5 0.00361082 [ 1.0278163]
6 0.00102708 [ 1.01483536]
7 0.000292144 [ 1.00791216]
8 8.30968e-05 [ 1.00421977]
9 2.36361e-05 [ 1.00225055]
10 6.72385e-06 [ 1.00120032]
11 1.91239e-06 [ 1.00064015]
12 5.43968e-07 [ 1.00034142]
13 1.54591e-07 [ 1.00018203]
14 4.39416e-08 [ 1.00009704]
15 1.24913e-08 [ 1.00005174]
16 3.5322e-09 [ 1.00002754]
17 9.99824e-10 [ 1.00001466]
18 2.88878e-10 [ 1.00000787]
19 8.02487e-11 [ 1.00000417]
20 2.34053e-11 [ 1.00000226]

 

lab-03-3-minimizing_cost_tf_optimizer.py

# Lab 3 Minimizing Cost
import tensorflow as tf

# tf Graph Input
X = [1, 2, 3]
Y = [1, 2, 3]

# Set wrong model weights
W = tf.Variable(5.0)

# Linear model
hypothesis = X * W

# cost/loss function
cost = tf.reduce_mean(tf.square(hypothesis - Y))

# Minimize: Gradient Descent Optimizer
train = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)

# Launch the graph in a session.
with tf.Session() as sess:
    # Initializes global variables in the graph.
    sess.run(tf.global_variables_initializer())

    for step in range(101):
        _, W_val = sess.run([train, W])
        print(step, W_val)

Result

0 5.0
1 1.2666664
2 1.0177778
3 1.0011852
4 1.000079
...
97 1.0
98 1.0
99 1.0
100 1.0

 

lab-03-X-minimizing_cost_tf_gradient.py

# Lab 3 Minimizing Cost
# This is optional
import tensorflow as tf

# 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 Optimizer
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)

# Get gradients
gvs = optimizer.compute_gradients(cost)

# 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.
with tf.Session() as sess:
    # Initializes global variables in the graph.
    sess.run(tf.global_variables_initializer())

    for step in range(101):
        gradient_val, gvs_val, _ = sess.run([gradient, gvs, apply_gradients])
        print(step, gradient_val, gvs_val)

Result

0 37.333332 [(37.333336, 5.0)]
1 33.84889 [(33.84889, 4.6266665)]
2 30.689657 [(30.689657, 4.2881775)]
3 27.825289 [(27.825289, 3.981281)]
...
97 0.0027837753 [(0.0027837753, 1.0002983)]
98 0.0025234222 [(0.0025234222, 1.0002704)]
99 0.0022875469 [(0.0022875469, 1.0002451)]
100 0.0020739238 [(0.0020739238, 1.0002222)]