[section_4_lab2] 텐서플로우 파일에서 데이터 읽어오기

텐서플로우 파일에서 데이터 읽어오기

• 우선 csv 파일을 저장해야한다.
  -> csv 파일 저장 방버을 모른다면 -> csv 파일 만들기

import tensorflow as tf
import numpy as np
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
tf.set_random_seed(777)  # for reproducibility

xy = np.loadtxt('data-01-test-score.csv', delimiter=',', dtype=np.float32)
x_data = xy[:, 0:-1]
y_data = xy[:, [-1]]

# Make sure the shape and data are OK
print(x_data.shape, x_data, len(x_data))
print(y_data.shape, y_data)

# 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())
# Set up feed_dict variables inside the loop.
for step in range(2001):
   cost_val, hy_val, _ = sess.run(
       [cost, hypothesis, train],
       feed_dict={X: x_data, Y: y_data})
   if step % 1000 == 0:
       print(step, "Cost: ", cost_val,
                  "\nPrediction:\n", hy_val)


# Ask my score
print("Your score will be ", sess.run(hypothesis,
           feed_dict={X: [[100, 70, 101]]}))

print("Other scores will be ", sess.run(hypothesis,
           feed_dict={X: [[60, 70, 110], [90, 100, 80]]}))

• 실행 결과

• Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
  다음과 같은 에러가 발생한다면 -> Your CPU supports instructions