TensorFlow 선형회귀

텐서플로우 선형회귀분석

1 단순

import numpy as np
import tensorflow as tf

x_train = [1, 2, 3, 4]
y_train = [0, -1, -2, -3]

tf.model = tf.keras.Sequential()
tf.model.add(tf.keras.layers.Dense(units=1, input_dim=1))

sgd = tf.keras.optimizers.SGD(lr=0.1)
tf.model.compile(loss='mse', optimizer=sgd)
tf.model.summary()
tf.model.fit(x_train, y_train, epochs=200)

y_predict = tf.model.predict(np.array([5, 4]))
print(y_predict)

2 다중

import tensorflow as tf

x_data = [[1,1],[2,2],[3,3]]
y_data = [[1],[2],[3]]
X = tf.placeholder(tf.float32, shape=[None,2])
Y = tf.placeholder(tf.float32, shape=[None,1])
W = tf.Variable(tf.random_normal([2,1]), name='weight')
b = tf.Variable(tf.random_normal([1]), name='bias')

hypothesis = tf.matmul(X,W) + b
cost = tf.reduce_mean(tf.square(hypothesis - Y))
train = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for step in range(2001):
        cost_val, W_val, b_val, _ = sess.run([cost, W, b, train], feed_dict={X: x_data, Y: y_data})
        if step % 200 == 0:
            print(step, cost_val, W_val, b_val)

실행결과

0 0.630719 [[ 1.96448588]
 [-1.46632338]] [ 0.50560945]
200 0.0159201 [[ 2.1430881 ]
 [-1.28772187]] [ 0.33275333]
400 0.00558863 [[ 2.17255759]
 [-1.25825167]] [ 0.19715267]
600 0.00196186 [[ 2.19001746]
 [-1.24079025]] [ 0.11681108]
800 0.000688694 [[ 2.20036244]
 [-1.23044467]] [ 0.06920907]
1000 0.000241762 [[ 2.20649195]
 [-1.22431529]] [ 0.04100555]
1200 8.48685e-05 [[ 2.21012259]
 [-1.22068286]] [ 0.02429538]
1400 2.97948e-05 [[ 2.21227384]
 [-1.21853077]] [ 0.01439506]
1600 1.04602e-05 [[ 2.21354699]
 [-1.21725464]] [ 0.00852949]
1800 3.67325e-06 [[ 2.2142992 ]
 [-1.21649647]] [ 0.00505449]
2000 1.29013e-06 [[ 2.21474481]
 [-1.21604705]] [ 0.0029954]

3 같이 보기

• TensorFlow 단순선형회귀
• 텐서플로우
• 선형회귀