Fashion MNIST で雰囲気を掴む

Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.

Here's an example how the data looks (each class takes three-rows):

引用：https://github.com/zalandoresearch/fashion-mnist

Get the Data

You can use direct links to download the dataset. The data is stored in the same format as the original MNIST data.

Name	Content	Examples	Size	Link	MD5 Checksum
`train-images-idx3-ubyte.gz`	training set images	60,000	26 MBytes	Download	`8d4fb7e6c68d591d4c3dfef9ec88bf0d`
`train-labels-idx1-ubyte.gz`	training set labels	60,000	29 KBytes	Download	`25c81989df183df01b3e8a0aad5dffbe`
`t10k-images-idx3-ubyte.gz`	test set images	10,000	4.3 MBytes	Download	`bef4ecab320f06d8554ea6380940ec79`
`t10k-labels-idx1-ubyte.gz`	test set labels	10,000	5.1 KBytes	Download	`bb300cfdad3c16e7a12a480ee83cd310`

Labels

Each training and test example is assigned to one of the following labels:

Label	Description
0	T-shirt/top
1	Trouser
2	Pullover
3	Dress
4	Coat
5	Sandal
6	Shirt
7	Sneaker
8	Bag
9	Ankle boot

Let's see the dataset



In [1]:

    
# -*- coding: utf-8 -*-
import sys
import numpy as np
from keras.utils import np_utils
from PIL import Image









    



Using TensorFlow backend.

データ読み込み用の関数



In [2]:

    
def load_mnist(path, kind='train'):
    import os
    import gzip
    import numpy as np

    """Load MNIST data from `path`"""
    labels_path = os.path.join(path,
                               '%s-labels-idx1-ubyte.gz'
                               % kind)
    images_path = os.path.join(path,
                               '%s-images-idx3-ubyte.gz'
                               % kind)

    with gzip.open(labels_path, 'rb') as lbpath:
        labels = np.frombuffer(lbpath.read(), dtype=np.uint8,
                               offset=8)

    with gzip.open(images_path, 'rb') as imgpath:
        images = np.frombuffer(imgpath.read(), dtype=np.uint8,
                               offset=16).reshape(len(labels), 784)

    return images, labels

データセットの読み込み



In [3]:

    
X_train, y_train = load_mnist('./data/fashion', kind='train')
X_test, y_test = load_mnist('./data/fashion', kind='t10k')
 
#訓練画像
train_no = 0
outImg = Image.fromarray(X_train[train_no].reshape((28,28))*255).convert("RGB")
outImg.save("out/train.png")
 
#テスト画像
test_no = 0
outImg = Image.fromarray(X_test[test_no].reshape((28,28))).convert("RGB")
outImg.save("out/test.png")

抜き出した画像は ‘train.png’と’test.png’という名前で保存されます。

28×28サイズで格納されている。

参考

画像サイズと色について

無数の回数を試行するため、可能なかぎり小さい方が、計算効率上は良い。色も白黒で済むなら、256階級値となるので、計算効率上は良い。

計算力があるなら、画像サイズやRGBを大きくして、精度を比較するのも、良い実験になるだろう。（もしかしたら、この議論に関しては論文があるかもしれない）

MNISTの略称

Mixed National Institute of Standards and Technology database

さらなる学習のために

CIFAR-10 Dataset

引用：http://www.cs.toronto.edu/%7Ekriz/cifar.html

CIFAR-10データセットは以下の10カテゴリの画像が学習用，テスト用それぞれに50000枚，10000枚含まれるデータセット

airplane
automobile
bird
cat
deer
dog
frog
horse
ship
truck

Download

http://www.cs.toronto.edu/%7Ekriz/cifar.html



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