training/test data set, learning rate, normalization

Training and Test data set으로 분류

- Training data set : 학습에만 사용

- Test data set : 모델이 한번도 본 적 없기에 새로운 데이터

Tensorflow

Training data set과 Test data set분류 예제

import tensorflow as tf
 
# 학습 데이터
x_data = [[1, 2, 1], [1, 3, 2], [1, 3, 4], [1, 5, 5], [1, 7, 5], [1, 2, 5], [1, 6, 6], [1, 7, 7]]
y_data = [[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 1, 0], [0, 1, 0], [0, 1, 0], [1, 0, 0], [1, 0, 0]]
 
# Test 데이터
x_test = [[2, 1, 1], [3, 1, 2], [3, 3, 4]]
y_test = [[0, 0, 1], [0, 0, 1], [0, 0, 1]]
 
X = tf.placeholder("float", [None, 3])
Y = tf.placeholder("float", [None, 3])
W = tf.Variable(tf.random_normal([3, 3]))
b = tf.Variable(tf.random_normal([3]))
 
# hypothesis
hypothesis = tf.nn.softmax(tf.matmul(X, W)+b)
cost = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(hypothesis), axis=1))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)
 
# Correct prediction Test model
prediction = tf.arg_max(hypothesis, 1)
is_correct = tf.equal(prediction, tf.arg_max(Y, 1))
accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))
 
# Launch graph
with tf.Session() as sess:
   # Initialize TensorFlow variables
   sess.run(tf.global_variables_initializer())
   for step in range(201):
       # 학습에는 학습 데이터 이용
       cost_val, W_val, _ = sess.run([cost, W, optimizer],
                       feed_dict={X: x_data, Y: y_data})
       print(step, cost_val, W_val)
       # 200  0.582307
       # [[-0.97919977 - 0.42496243  2.13029099]
       # [-0.84562796 - 1.03543162 - 1.07710946]
       # [0.66253269  0.44326913 - 0.41879809]]
 
   # predict : 테스트에는 테스트 데이터 이용
   print("Prediction:", sess.run(prediction, feed_dict={X: x_test}))
   # Prediction: [2 2 2]
 
   # Calculate the accuracy
   print("Accuracy: ", sess.run(accuracy, feed_dict={X: x_test, Y: y_test}))
   # Accuracy: 1.0

learning rate를 1.5로 준 예제

import tensorflow as tf
 
# 학습 데이터
x_data = [[1, 2, 1], [1, 3, 2], [1, 3, 4], [1, 5, 5], [1, 7, 5], [1, 2, 5], [1, 6, 6], [1, 7, 7]]
y_data = [[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 1, 0], [0, 1, 0], [0, 1, 0], [1, 0, 0], [1, 0, 0]]
 
# Test 데이터
x_test = [[2, 1, 1], [3, 1, 2], [3, 3, 4]]
y_test = [[0, 0, 1], [0, 0, 1], [0, 0, 1]]
 
X = tf.placeholder("float", [None, 3])
Y = tf.placeholder("float", [None, 3])
W = tf.Variable(tf.random_normal([3, 3]))
b = tf.Variable(tf.random_normal([3]))
 
# hypothesis
hypothesis = tf.nn.softmax(tf.matmul(X, W)+b)
cost = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(hypothesis), axis=1))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.5).minimize(cost)
 
# Correct prediction Test model
prediction = tf.arg_max(hypothesis, 1)
is_correct = tf.equal(prediction, tf.arg_max(Y, 1))
accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))
 
# Launch graph
with tf.Session() as sess:
   # Initialize TensorFlow variables
   sess.run(tf.global_variables_initializer())
   for step in range(201):
       # 학습에는 학습 데이터 이용
       cost_val, W_val, _ = sess.run([cost, W, optimizer],
                       feed_dict={X: x_data, Y: y_data})
       print(step, cost_val, W_val)
       # 200  0.582307
       # [[-0.97919977 - 0.42496243  2.13029099]
       # [-0.84562796 - 1.03543162 - 1.07710946]
       # [0.66253269  0.44326913 - 0.41879809]]
 
   # predict : 테스트에는 테스트 데이터 이용
   print("Prediction:", sess.run(prediction, feed_dict={X: x_test}))
   # Prediction: [2 2 2]
 
   # Calculate the accuracy
   print("Accuracy: ", sess.run(accuracy, feed_dict={X: x_test, Y: y_test}))
   # Accuracy: 1.0

learning rate를 1e-10으로 작게 준 예제

import tensorflow as tf
 
# 학습 데이터
x_data = [[1, 2, 1], [1, 3, 2], [1, 3, 4], [1, 5, 5], [1, 7, 5], [1, 2, 5], [1, 6, 6], [1, 7, 7]]
y_data = [[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 1, 0], [0, 1, 0], [0, 1, 0], [1, 0, 0], [1, 0, 0]]
 
# Test 데이터
x_test = [[2, 1, 1], [3, 1, 2], [3, 3, 4]]
y_test = [[0, 0, 1], [0, 0, 1], [0, 0, 1]]
 
X = tf.placeholder("float", [None, 3])
Y = tf.placeholder("float", [None, 3])
W = tf.Variable(tf.random_normal([3, 3]))
b = tf.Variable(tf.random_normal([3]))
 
# hypothesis
hypothesis = tf.nn.softmax(tf.matmul(X, W)+b)
cost = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(hypothesis), axis=1))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)
 
# Correct prediction Test model
prediction = tf.arg_max(hypothesis, 1)
is_correct = tf.equal(prediction, tf.arg_max(Y, 1))
accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))
 
# Launch graph
with tf.Session() as sess:
   # Initialize TensorFlow variables
   sess.run(tf.global_variables_initializer())
   for step in range(201):
       # 학습에는 학습 데이터 이용
       cost_val, W_val, _ = sess.run([cost, W, optimizer],
                       feed_dict={X: x_data, Y: y_data})
       print(step, cost_val, W_val)
       # 200  0.582307
       # [[-0.97919977 - 0.42496243  2.13029099]
       # [-0.84562796 - 1.03543162 - 1.07710946]
       # [0.66253269  0.44326913 - 0.41879809]]
 
   # predict : 테스트에는 테스트 데이터 이용
   print("Prediction:", sess.run(prediction, feed_dict={X: x_test}))
   # Prediction: [2 2 2]
 
   # Calculate the accuracy
   print("Accuracy: ", sess.run(accuracy, feed_dict={X: x_test, Y: y_test}))
   # Accuracy: 1.0

Non-normalized inputs data의 학습 에제

import tensorflow as tf
import numpy as np
 
xy = np.array([[828.659973, 833.450012, 908100, 828.349976, 831.659973],
              [823.02002, 828.070007, 1828100, 821.655029, 828.070007],
              [819.929993, 824.400024, 1438100, 818.97998, 824.159973],
              [816, 820.958984, 1008100, 815.48999, 819.23999],
              [819.359985, 823, 1188100, 818.469971, 818.97998],
              [819, 823, 1198100, 816, 820.450012],
              [811.700012, 815.25, 1098100, 809.780029, 813.669983],
              [809.51001, 816.659973, 1398100, 804.539978, 809.559998]])
 
# 0 ~ 3 : x_data
# 4 : y_data
x_data = xy[:, 0:-1]
y_data = xy[:, [-1]]
 
# placeholder
X = tf.placeholder(tf.float32, shape=[None, 4])
Y = tf.placeholder(tf.float32, shape=[None, 1])
W = tf.Variable(tf.random_normal([4, 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))
 
# Minimize
optimizer = tf.train.GradientDescentOptimizer(learning_rate=1e-5)
train = optimizer.minimize(cost)
 
sess = tf.Session()
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})
   print(step, "Cost: ", cost_val, "\nPrediction:\n", hy_val)
   # learning_rate를 이것저것 바꿔봐도 input data의 값들간의 차이가 너무 커서 학습이 이루어지지 않음
   # 2000
   # Cost: nan
   # Prediction:
   # [[nan]
   #  [nan]
   #  [nan]
   #  [nan]
   #  [nan]
   #  [nan]
   #  [nan]
   #  [nan]]

-min-max scale을 이용한 normalize 학습 예제

import tensorflow as tf
import numpy as np
 
xy = np.array([[828.659973, 833.450012, 908100, 828.349976, 831.659973],
              [823.02002, 828.070007, 1828100, 821.655029, 828.070007],
              [819.929993, 824.400024, 1438100, 818.97998, 824.159973],
              [816, 820.958984, 1008100, 815.48999, 819.23999],
              [819.359985, 823, 1188100, 818.469971, 818.97998],
              [819, 823, 1198100, 816, 820.450012],
              [811.700012, 815.25, 1098100, 809.780029, 813.669983],
              [809.51001, 816.659973, 1398100, 804.539978, 809.559998]])
 
# Min-Max Scale을 이용한 normalize
def MinMaxScaler(data):
    numerator = data - np.min(data, 0)
    denominator = np.max(data, 0) - np.min(data, 0)
    # noise term prevents the zero division
    return numerator / (denominator + 1e-7)
xy = MinMaxScaler(xy)
 
# 0 ~ 3 : x_data
# 4 : y_data
x_data = xy[:, 0:-1]
y_data = xy[:, [-1]]
 
# placeholder
X = tf.placeholder(tf.float32, shape=[None, 4])
Y = tf.placeholder(tf.float32, shape=[None, 1])
W = tf.Variable(tf.random_normal([4, 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))
 
# Minimize
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
train = optimizer.minimize(cost)
 
sess = tf.Session()
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})
   print(step, "Cost: ", cost_val, "\nPrediction:\n", hy_val)
   # MinMaxScaler를 이용하여 normalize & 학습
   # 2000
   # Cost: 0.0116639
   # Prediction:
   # [[1.09614301]
   #  [0.77814764]
   #  [0.56500143]
   #  [0.38963056]
   #  [0.47361973]
   #  [0.4958491]
   #  [0.0413903]
   #  [0.21422961]]

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