lab:multi-variable linear regression을 TensorFlow에서 구현하기
January 05, 2019
해당 게시물은 Edwith에서 제공하는
머신러닝과 딥러닝 BASIC을 듣고 요약 정리한 글입니다.
Hypothesis using matrix
| x1 | x2 | x3 | Y |
|---|---|---|---|
| 73 | 80 | 75 | 152 |
| 93 | 88 | 93 | 185 |
| 89 | 91 | 90 | 180 |
| 96 | 98 | 100 | 196 |
| 73 | 66 | 70 | 142 |
변수 3개(x1, x2, x3)을 이용하여 Y(결과)를 예측
import tensorflow as tf
x1_data = [73., 93., 89., 96., 73.]
x2_data = [80., 88., 91., 98., 66.]
x3_data = [75., 93., 90., 100., 70.]
y_data = [152., 185., 180., 196., 142.]
# placeholder for a tensor that will be always fed.
x1 = tf.placeholder(tf.float32)
x2 = tf.placeholder(tf.float32)
x3 = tf.placeholder(tf.float32)
Y = tf.placeholder(tf.float32)
w1 = tf.Variable(tf.random_normal([1]), name='weight1')
w2 = tf.Variable(tf.random_normal([1]), name='weight2')
w3 = tf.Variable(tf.random_normal([1]), name='weight3')
b = tf.Variable(tf.random_normal([1]), name='bias')
hypothesis = x1 * w1 + x2 *w2 + x3 * w3 + b
# cost/Loss function
cost = tf.reduce_mean(tf.square(hypothesis - Y))
# Minimize. Need a very small learning rate for this data set
optimizer = tf.train.GradientDescentOptimizer(learning_rate=1e-5)
train = optimizer.minimize(cost)
# 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(2001):
cost_val, hy_val, _ = sess.run([cost, hypothesis, train],
feed_dict={x1: x1_data,
x2: x2_data,
x3: x3_data,
Y : y_data})
if step % 10 == 0:
print(step, "Cost:", cost_val, "\nPrediction:\n", hy_val)0 Cost: 7444.396
Prediction:
[73.21022 93.897156 89.57611 96.45675 73.962616]
10 Cost: 2.2866924
Prediction:
[149.52972 185.61728 179.95482 194.87642 143.91982]
20 Cost: 2.2078712
Prediction:
[149.76602 185.8911 180.2299 195.17554 144.12651]
30 Cost: 2.1972535
Prediction:
[149.77211 185.88824 180.23238 195.17775 144.1222 ]
40 Cost: 2.186692
Prediction:
[149.77747 185.88455 180.234 195.17906 144.11723]
...
1960 Cost: 0.9266957
Prediction:
[150.57715 185.33379 180.47571 195.37918 143.37326]
1970 Cost: 0.922966
Prediction:
[150.5803 185.33162 180.47667 195.38 143.3703 ]
1980 Cost: 0.9192538
Prediction:
[150.58345 185.32944 180.47762 195.38081 143.36734]
1990 Cost: 0.915562
Prediction:
[150.5866 185.32727 180.47856 195.38162 143.3644 ]
2000 Cost: 0.9118868
Prediction:
[150.5897 185.3251 180.47948 195.38242 143.36143]Matrix
x_data = [
[73., 80., 75.],
[93., 88., 93.],
[89., 91., 90.],
[96., 98., 100.],
[73., 66., 70.],
]
y_data = [
[152.],
[185.],
[180.],
[196.],
[142.],
]
# placeholders for a tensor that will be always fed.
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
hypothesis = tf.matmul(X, W) + b
# Simplified cost/Loss function
cost = tf.reduce_mean(tf.square(hypothesis - Y))
# Minimize
optimizer = tf.train.GradientDescentOptimizer(learning_rate=1e-5)
train = optimizer.minimize(cost)
# Launch the graph in a session.
sess = tf.Session()
# Initalizes global variables in the graph.
sess.run(tf.global_variables_initializer())
for step in range(2001):
cost_val, hy_val, _ = sess.run([cost, hypothesis, train],
feed_dict={X: x_data, Y: y_data})
if step % 10 == 0:
print(step, "Cost:", cost_val, "\nPrediction:\n", hy_val)0 Cost: 84.2611
Prediction:
[[147.19199]
[172.82959]
[172.4113 ]
[187.48358]
[131.04774]]
10 Cost: 7.225972
Prediction:
[[154.9442 ]
[182.16638]
[181.601 ]
[197.4919 ]
[138.17336]]
...
1990 Cost: 2.6620722
Prediction:
[[153.41551]
[183.26245]
[181.15785]
[197.14584]
[139.62639]]
2000 Cost: 2.6492188
Prediction:
[[153.4096 ]
[183.26651]
[181.15607]
[197.14436]
[139.63187]]
