땅 밑 검은 하늘

# MNIST 데이터 불러오기.

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

# tensorflow 세션 구동.

import tensorflow as tf

sess = tf.InteractiveSession()

# x  : 트레이닝 입력 벡터.  

# y_ : 결과 레이블.

x = tf.placeholder(tf.float32, shape=[None, 784])

y_ = tf.placeholder(tf.float32, shape=[None, 10])

# 입력 벡터를 28*28 행렬로 전환. 

x_image = tf.reshape(x, [-1,28,28,1])

# Weight 초기화 함수. 

def weight_variable(shape):

  initial = tf.truncated_normal(shape, stddev=0.1)

  return tf.Variable(initial)

# Bias 초기화 함수. 

def bias_variable(shape):

  initial = tf.constant(0.1, shape=shape)

  return tf.Variable(initial)

# 2D convolution 함수.

def conv2d(x, W):

  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

# 2*2 max pooling 함수.

def max_pool_2x2(x):

  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

# 첫번째 Convolutional Layer

W_conv1 = weight_variable([5, 5, 1, 32])

b_conv1 = bias_variable([32])

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)

h_pool1 = max_pool_2x2(h_conv1)

# 두번째 Convolutional Layer

W_conv2 = weight_variable([5, 5, 32, 64])

b_conv2 = bias_variable([64])

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)

h_pool2 = max_pool_2x2(h_conv2)

# fully-Connected Layer

W_fc1 = weight_variable([7 * 7 * 64, 1024])

b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])

h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

# Dropout Layer

keep_prob = tf.placeholder(tf.float32)

h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

# Readout Layer

W_fc2 = weight_variable([1024, 10])

b_fc2 = bias_variable([10])

y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2

# 훈련, 측정 모델.

cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))

train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

sess.run(tf.global_variables_initializer())

for i in range(10000):

  batch = mnist.train.next_batch(50)

  if i%1000 == 0:

    train_accuracy = accuracy.eval(feed_dict={

        x:batch[0], y_: batch[1], keep_prob: 1.0})

    print("step %d, training accuracy %g"%(i, train_accuracy))

  train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

print("test accuracy %g"%accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

# -*- coding: utf-8 -*-

# MNIST 데이터 다운로드.

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets( "MNIST_data/", one_hot=True )

# TensorFlow 라이브러리 추가.
import tensorflow as tf

# 테스트를 위한 라이브러리 추가.
import matplotlib.pyplot as plt
import numpy as np

# 변수들 설정.
train_data = tf.placeholder( tf.float32, [None, 784] )

Weights = tf.Variable( tf.zeros( [784, 10] ) )

Bias = tf.Variable( tf.zeros([10] ) )

Hypothesis = tf.nn.softmax( tf.matmul( num_data, Weights ) + Bias )

# cross-entropy 모델 설정.

learning_rate = 0.25

num_label = tf.placeholder( tf.float32, [None, 10] )

cost = tf.reduce_mean( -tf.reduce_sum( num_label * tf.log( Hypothesis ), reduction_indices=[1] ) )

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

# cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits( logits=Hypothesis, labels=num_label ))

# optimizer = tf.train.AdamOptimizer( learning_rate=learning_rate ).minimize( cost )  # Gradient Descen

# 경사하강법으로 모델 학습.

batch_size = 100

with tf.Session( ) as sess :

    sess.run( tf.global_variables_initializer( ) )

    for i in range( 2000 ) :

        batch_xs, batch_ys = mnist.train.next_batch( batch_size )

        sess.run( optimizer, feed_dict={num_data:batch_xs, num_label:batch_ys} )

        if i % 200 == 0 :

            print( "{0} step, Accuracy {1} ".format( i, sess.run( cost, feed_dict={num_data:batch_xs, num_label:batch_ys} ) ) )

    # 학습된 모델이 얼마나 정확한지를 출력한다.

    correct_prediction = tf.equal( tf.argmax(Hypothesis,1), tf.argmax(num_label,1) )

    Accuracy = tf.reduce_mean( tf.cast( correct_prediction, tf.float32 ) )

    print( sess.run( Accuracy, feed_dict={num_data:mnist.test.images, num_label:mnist.test.labels} ) )