DeepLearning_Lab02_1 - 8BitsCoding/RobotMentor GitHub Wiki

• 참고사이트

---

# Lab 2 Linear Regression
import tensorflow as tf
tf.set_random_seed(777)  # for reproducibility

# Try to find values for W and b to compute Y = W * X + b
W = tf.Variable(tf.random_normal([1]), name="weight")
b = tf.Variable(tf.random_normal([1]), name="bias")

아는 부분이라 설명 생략

# placeholders for a tensor that will be always fed using feed_dict
# See http://stackoverflow.com/questions/36693740/
X = tf.placeholder(tf.float32, shape=[None])
Y = tf.placeholder(tf.float32, shape=[None])

Placeholder : X, Y값을 runtime에 넣고 싶다

# Our hypothesis is X * W + b
hypothesis = X * W + b

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

# optimizer
train = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)

아는 부분이라 설명 생략

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

    # Fit the line
    for step in range(2001):
        _, cost_val, W_val, b_val = sess.run(
            [train, cost, W, b], feed_dict={X: [1, 2, 3], Y: [1, 2, 3]}
        )
        if step % 20 == 0:
            print(step, cost_val, W_val, b_val)

    # Testing our model
    print(sess.run(hypothesis, feed_dict={X: [5]}))
    print(sess.run(hypothesis, feed_dict={X: [2.5]}))
    print(sess.run(hypothesis, feed_dict={X: [1.5, 3.5]}))

학습데이터를 손 쉽게 수정할 수 있게 되었다!

    # Fit the line with new training data
    for step in range(2001):
        _, cost_val, W_val, b_val = sess.run(
            [train, cost, W, b],
            feed_dict={X: [1, 2, 3, 4, 5], Y: [2.1, 3.1, 4.1, 5.1, 6.1]},
        )
        if step % 20 == 0:
            print(step, cost_val, W_val, b_val)

    # Testing our model
    print(sess.run(hypothesis, feed_dict={X: [5]}))
    print(sess.run(hypothesis, feed_dict={X: [2.5]}))
    print(sess.run(hypothesis, feed_dict={X: [1.5, 3.5]}))