tensorflow.distinguish mnist

# 절대 임포트 설정
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# 필요한 라이브러리들을 임포트
import argparse
import sys
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
FLAGS = None

def deepnn(x):
    x_image = tf.reshape(x, [-1, 28, 28, 1])
    # 첫번째 convolutional layer - 하나의 grayscale 이미지를 32개의 특징들(feature)으로 맵핑(maping)한다.
    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)
    # Pooling layer - 2X만큼 downsample한다.
    h_pool1 = max_pool_2x2(h_conv1)
    # 두번째 convolutional layer -- 32개의 특징들(feature)을 64개의 특징들(feature)로 맵핑(maping)한다.
    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)
    # 두번째 pooling layer.
    h_pool2 = max_pool_2x2(h_conv2)
    # Fully Connected Layer 1 -- 2번의 downsampling 이후에, 우리의 28x28 이미지는 7x7x64 특징들(feature map)이 된다.
    # 이를 1024개의 특징들로 맵핑(maping)한다.
    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 - 모델의 복잡도를 컨트롤한다. 특징들의 co-adaptation을 방지한다.
    keep_prob = tf.placeholder(tf.float32)
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
    # 1024개의 특징들(feature)을 10개의 클래스-숫자 0-9-로 맵핑(maping)한다.
    W_fc2 = weight_variable([1024, 10])
    b_fc2 = bias_variable([10])
    y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
    return y_conv, keep_prob

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

def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
    strides=[1, 2, 2, 1], padding='SAME')

def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)
def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)
def main(_):
    # data를 import한다.
    mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
    # 모델을 생성한다.
    x = tf.placeholder(tf.float32, [None, 784])
    # loss와 optimizer를 정의한다.
    y_ = tf.placeholder(tf.float32, [None, 10])
    # Deep Neural Networks 그래프를 생성한다.
    y_conv, keep_prob = deepnn(x)
    # Cross Entropy를 비용함수(loss function)으로 정의하고, AdamOptimizer를 이용해서 비용 함수를 최소화한다.
    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)
    # 정확도를 측정한다.
    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for i in range(20000):
            batch = mnist.train.next_batch(50)
        # 100 Step마다 training 데이터셋에 대한 정확도를 출력한다.
            if i % 100 == 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))
            # 50% 확률의 Dropout을 이용해서 학습을 진행한다.
            train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

            # 테스트 데이터에 대한 정확도를 출력한다.
        print('test accuracy %g' % accuracy.eval(feed_dict={
            x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--data_dir', type=str,
        default='/tmp/tensorflow/mnist/input_data',
        help='Directory for storing input data')
    FLAGS, unparsed = parser.parse_known_args()
    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

 

Extracting /tmp/tensorflow/mnist/input_data\train-images-idx3-ubyte.gz
Extracting /tmp/tensorflow/mnist/input_data\train-labels-idx1-ubyte.gz
Extracting /tmp/tensorflow/mnist/input_data\t10k-images-idx3-ubyte.gz
Extracting /tmp/tensorflow/mnist/input_data\t10k-labels-idx1-ubyte.gz
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test accuracy 0.9914
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