DeepLearning_L04 - 8BitsCoding/RobotMentor GitHub Wiki

• 이론
• 구현1
• 구현1_전체코드

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이론

여러개의 input일때는??

예를들어서 퀴즈1, 퀴즈2, 중간고사 점수를 바탕으로 결과를 도출해본다.

가설을 다음과 같은 방식으로 Matrix를 표현이 가능하다.

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구현1

• Example Code(Matrix를 쓰지 않음)
• Example Code(Matrix를 사용)

예제의 설명은 Matrix를 사용한 버전으로 설명

# Lab 4 Multi-variable linear regression
import tensorflow as tf
tf.set_random_seed(777)  # for reproducibility

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()
# 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)

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구현1_전체코드

# Lab 4 Multi-variable linear regression
import tensorflow as tf
tf.set_random_seed(777)  # for reproducibility

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()
# 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  ]]
'''