Multi Variable Regression Model

3개의 Feature를 가진 회귀모델

# Lab 4 Multi-variable linear regression
import tensorflow as tf
tf.set_random_seed(777)  # for reproducibility
 
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.]
 
# placeholders 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:  19614.8
Prediction:
 [ 21.69748688  39.10213089  31.82624626  35.14236832  32.55316544]
10 Cost:  14.0682
Prediction:
 [ 145.56100464  187.94958496  178.50236511  194.86721802  146.08096313]
 
 ...
 
1990 Cost:  4.9197
Prediction:
 [ 148.15084839  186.88632202  179.6293335   195.81796265  144.46044922]
2000 Cost:  4.89449
Prediction:
 [ 148.15931702  186.8805542   179.63194275  195.81971741  144.45298767]

Matrix Form

$$(x_1;x_2;x_3)\cdot \left(\begin{array}{c}{w}_1\\ w_2\\ w_3\end{array}\right);=;(x_1{w}_1+x_2{w}_2+x_3{w}_3)$$

# Lab 4 Multi-variable linear regression
import tensorflow as tf
tf.set_random_seed(777)  # for reproducibility
 
# 데이터 형식도 이런식으로 받는다.
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.
# 이제 Matrix 형태로 넣어줄 거니까, Shape이 중요해진다.
# X는 column이 3개여야 한다.
# 그리고 row는 계속 늘어나기 때문에 값이 늘어날 수 있다. 그래서 None을 적어준다.
X = tf.placeholder(tf.float32, shape=[None, 3])
Y = tf.placeholder(tf.float32, shape=[None, 1])
 
# weight matrix는 3행 1열짜리 행렬이되야 한다.
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()
# 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={X: x_data, Y: y_data})
    if step % 10 == 0:
        print(step, "Cost: ", cost_val, "\nPrediction:\n", hy_val)
 

0 Cost:  7105.46
Prediction:
 [[ 80.82241058]
 [ 92.26364136]
 [ 93.70250702]
 [ 98.09217834]
 [ 72.51759338]]
10 Cost:  5.89726
Prediction:
 [[ 155.35159302]
 [ 181.85691833]
 [ 181.97254944]
 [ 194.21760559]
 [ 140.85707092]]
 
...
 
1990 Cost:  3.18588
Prediction:
 [[ 154.36352539]
 [ 182.94833374]
 [ 181.85189819]
 [ 194.35585022]
 [ 142.03240967]]
2000 Cost:  3.1781
Prediction:
 [[ 154.35881042]
 [ 182.95147705]
 [ 181.85035706]
 [ 194.35533142]
 [ 142.036026  ]]