MNIST - 기본 1 layer 머신러닝

import tensorflow as tf
import random
from tensorflow.examples.tutorials.mnist import input_data
 
mnist = input_data.read_data_sets("tmp/data/", one_hot=True)
 
tf.set_random_seed(777)
 
x=tf.placeholder(tf.float32, [None, 784])
y=tf.placeholder(tf.float32, [None, 10])
 
w=tf.Variable(tf.random_normal([784, 10], stddev=0.1))
b=tf.Variable(tf.random_normal([10], stddev=0.1))
 
hypothesis = tf.nn.softmax(tf.matmul(x,w)+b)
 
cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=hypothesis, labels=y))
optimizer = tf.train.GradientDescentOptimizer(0.1).minimize(cost)
 
is_correct = tf.equal(tf.arg_max(hypothesis,1), tf.arg_max(y,1))
accuracy =  tf.reduce_mean(tf.cast(is_correct, tf.float32))
 
print(hypothesis)
print(cost)
print(optimizer)
 
t_epoch=10
batch_size=100
 
with tf.Session() as sess:
  start = tf.global_variables_initializer()
  sess.run(start)
 
  total_batch = int(mnist.train.num_examples/batch_size)
  #total_batch = int(size(mnist)/batch_size)
  print(total_batch)  
  
  for epoch in range(t_epoch):
 
    avg_cost=0
 
    for i in range(total_batch):
      batch_x, batch_y = mnist.train.next_batch(batch_size)
      
      c, _ = sess.run([cost, optimizer], feed_dict={x:batch_x, y:batch_y})
      avg_cost+=c
 
    print('{:.9f}'.format(avg_cost/total_batch))
 
  print("정확도 : ", accuracy.eval(session=sess, feed_dict={x:mnist.test.images, y:mnist.test.labels})*100)    
 
  idx = random.randint(0, mnist.test.num_examples-1)
  print("Label : ", sess.run(tf.argmax(mnist.test.labels[idx: idx+1], 1)))
  print("Prediction : ", sess.run(tf.argmax(hypothesis, 1), feed_dict={x:mnist.test.images[idx:idx+1]}))
 
 

1. 데이터 셋 : MNIST 를 사용했다

2. 레이어 갯수 : 레이어 1개로 학습했다

3. cost function : 크로스 엔트로피를 활용했다

4. optimizer : Gradient Descent 를 활용했다

5. 전체적인 라이브러리 : 텐서플로우 하나로만 실습했다