[MDS-01] モジュールをインポートします。
In [1]:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
[MDS-02] MNISTのデータセットをダウンロードして、オブジェクトに格納します。
In [2]:
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
Extracting /tmp/data/train-images-idx3-ubyte.gz
Extracting /tmp/data/train-labels-idx1-ubyte.gz
Extracting /tmp/data/t10k-images-idx3-ubyte.gz
Extracting /tmp/data/t10k-labels-idx1-ubyte.gz
[MDS-03] トレーニングセットから、10個分のデータを取り出して、画像データとラベルを別々の変数に格納します。
In [3]:
images, labels = mnist.train.next_batch(10)
[MDS-04] 1つめの画像データを確認します。各ピクセルの濃度が並んだリスト（arrayオブジェクト）になっています。
In [4]:
print (images[0])
[ 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0.15294118 0.90588242 0.99215692 0.56078434
0.02352941 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0.25882354 0.97254908 0.98823535
0.98823535 0.36078432 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.80392164
0.98823535 0.98823535 0.98823535 0.07058824 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.42745101 0.96078438 0.98823535 0.98823535 0.56078434 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0.50980395 0.98823535 0.98823535 0.95686281 0.10196079
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0.27058825 0.96078438 0.98823535 0.99215692
0.55686277 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.51372552 0.98823535
0.99215692 0.89411771 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.26666668
0.97254908 0.99215692 0.92941183 0.14509805 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0.9450981 0.99215692 0.98823535 0.57647061 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0.9450981 0.99215692 0.98823535 0.84705889 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0.4784314 1. 0.99215692 0.92549026 0.17254902 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0.43137258 0.99215692 0.98823535 0.98823535 0.32941177 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0.99215692 0.98823535 0.98823535 0.32941177 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0.90196085 0.98823535 0.98823535 0.41960788 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0.51764709 0.98823535 0.98823535 0.80000007 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0.51764709 0.98823535 0.98823535 0.80000007 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0.51764709 0.98823535 0.98823535 0.80000007 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0.51764709 0.98823535 0.98823535 0.96078438 0.24313727
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0.51764709 0.98823535 0.98823535 0.98823535
0.28235295 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0.02352941 0.58431375 0.98823535
0.58039218 0.01568628 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. ]
[MDS-05] 対応するラベルを確認します。先頭を0として、7番目の要素が1になっているので、「7」の画像である事を示します。
In [5]:
print (labels[0])
[ 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
[MDS-06] 画像データを実際の画像として表示してみます。
In [6]:
fig = plt.figure(figsize=(8,4))
for c, (image, label) in enumerate(zip(images, labels)):
subplot = fig.add_subplot(2,5,c+1)
subplot.set_xticks([])
subplot.set_yticks([])
subplot.set_title('%d' % np.argmax(label))
subplot.imshow(image.reshape((28,28)), vmin=0, vmax=1,
cmap=plt.cm.gray_r, interpolation="nearest")