TF MNIST 예제가 원래 데이터와 함께 작동하지 않는 이유는 무엇입니까?

Question

TensorFlow MNIST example의 데이터로드를 original MNIST data으로 조정했습니다. 원래의 예제는 100 개 에포크 이후 0.80 이상의 정확도를 갖습니다. 내 조정 예제 (use_original = False 세트 사용)는 약 0.09 - 0.10 정확도 (단지 랜덤 임)를 얻습니다. 이유를 설명해 주시겠습니까?

#!/usr/bin/env python 

"""MNIST with Tensorflow.""" 

from tensorflow.examples.tutorials.mnist import input_data 
import tensorflow as tf 

from struct import unpack 
import gzip 
from numpy import zeros, uint8 

from sklearn.preprocessing import OneHotEncoder 

use_original = True 


def get_labeled_data(imagefile, labelfile): 
    """ 
    Read input-vector (image) and target class (label, 0-9). 

    Return 
    ------ 
    tuple of lists 
    """ 
    # Open the images with gzip in read binary mode 
    images = gzip.open(imagefile, 'rb') 
    labels = gzip.open(labelfile, 'rb') 

    # Read the binary data 

    # We have to get big endian unsigned int. So we need '>I' 

    # Get metadata for images 
    images.read(4) # skip the magic_number 
    number_of_images = images.read(4) 
    number_of_images = unpack('>I', number_of_images)[0] 
    rows = images.read(4) 
    rows = unpack('>I', rows)[0] 
    cols = images.read(4) 
    cols = unpack('>I', cols)[0] 

    # Get metadata for labels 
    labels.read(4) # skip the magic_number 
    N = labels.read(4) 
    N = unpack('>I', N)[0] 

    if number_of_images != N: 
     raise Exception('number of labels did not match the number of images') 

    # Get the data 
    x = zeros((N, rows * cols), dtype=uint8) # Initialize numpy array 
    y = zeros((N, 1), dtype=uint8) # Initialize numpy array 
    for i in range(N): 
     if i % 1000 == 0: 
      print("%s: %i" % (imagefile, i)) 
     j = 0 
     for row in range(rows): 
      for col in range(cols): 
       tmp_pixel = images.read(1) # Just a single byte 
       tmp_pixel = unpack('>B', tmp_pixel)[0] 
       x[i][j] = tmp_pixel 
       j += 1 
     tmp_label = labels.read(1) 
     y[i] = unpack('>B', tmp_label)[0] 
    enc = OneHotEncoder() 
    enc.fit(y) 
    y = enc.transform(y).toarray() 
    return (x, y) 


epochs = 20000 


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 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 eval_network(dataset, correct_prediction): 
    correct_sum = 0 
    total_test = 0 
    for i in range(dataset.labels.shape[0]/1000): 
     feed_dict = {x: dataset.images[i * 1000:(i + 1) * 1000], 
        y_: dataset.labels[i * 1000:(i + 1) * 1000], 
        keep_prob: 1.0} 
     test_correct = correct_prediction.eval(feed_dict=feed_dict) 
     correct_sum += sum(test_correct) 
     total_test += len(test_correct) 
    return float(correct_sum)/total_test 


def add_score(filename, mnist, scoring, epoch, other=''): 
    with open(filename, "a") as myfile: 
     train = eval_network(mnist.train, scoring) 
     test = eval_network(mnist.test, scoring) 
     myfile.write("%i;%0.6f;%0.6f;%s\n" % (epoch, train, test, other)) 


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

sess = tf.InteractiveSession() 
x = tf.placeholder(tf.float32, shape=[None, 784]) 
y_ = tf.placeholder(tf.float32, shape=[None, 10]) 

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

sess.run(tf.initialize_all_variables()) 

y = tf.nn.softmax(tf.matmul(x, W) + b) 

W_conv1 = weight_variable([5, 5, 1, 32]) 
b_conv1 = bias_variable([32]) 
x_image = tf.reshape(x, [-1, 28, 28, 1]) 
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) 
h_pool1 = max_pool_2x2(h_conv1) 

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) 

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) 

keep_prob = tf.placeholder(tf.float32) 
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) 

W_fc2 = weight_variable([1024, 10]) 
b_fc2 = bias_variable([10]) 

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

cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), 
               reduction_indices=[1])) 
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.initialize_all_variables()) 

# Data loading 

if use_original: 
    mnist = input_data.read_data_sets('MNIST_data', one_hot=True) 
else: 
    mnist = lambda: None 
    setattr(mnist, 'train', lambda: None) 
    setattr(mnist, 'test', lambda: None) 
    setattr(mnist.train, 'images', lambda: None) 
    setattr(mnist.train, 'labels', lambda: None) 
    setattr(mnist.test, 'images', lambda: None) 
    setattr(mnist.test, 'labels', lambda: None) 
    xs, ys = get_labeled_data('mnist/train-images-idx3-ubyte.gz', 
           'mnist/train-labels-idx1-ubyte.gz') 
    mnist.train.images = xs 
    mnist.train.labels = ys 
    xst, yst = get_labeled_data('mnist/t10k-images-idx3-ubyte.gz', 
           'mnist/t10k-labels-idx1-ubyte.gz') 
    mnist.test.images = xst 
    mnist.test.labels = yst 
for i in range(epochs): 
    if use_original: 
     batch = mnist.train.next_batch(50) # This works 
    else: 
     # This doesnt work 
     batch = (xs[i * 50:(i + 1) * 50], ys[i * 50:(i + 1) * 50]) 
    if i % 100 == 0: 
     add_score('accuracy.csv', 
        mnist, 
        correct_prediction, 
        i) 
    train_step.run(feed_dict={x: batch[0], 
           y_: batch[1], 
           keep_prob: 0.5}) 
add_score('accuracy.csv', mnist, correct_prediction, epochs) 

Answer 1

이 문제점을 발견했습니다. 데이터는 numpy.float32으로로드해야하며 255으로 나눠서 정규화해야합니다.