[MDS-01] モジュールをインポートします。
In [1]:
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)
Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
Extracting /tmp/data/train-images-idx3-ubyte.gz
Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
Extracting /tmp/data/train-labels-idx1-ubyte.gz
Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
Extracting /tmp/data/t10k-images-idx3-ubyte.gz
Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
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. 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. 0.38039219 0.37647063
0.3019608 0.46274513 0.2392157 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.35294119 0.5411765
0.92156869 0.92156869 0.92156869 0.92156869 0.92156869 0.92156869
0.98431379 0.98431379 0.97254908 0.99607849 0.96078438 0.92156869
0.74509805 0.08235294 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.54901963 0.98431379 0.99607849 0.99607849 0.99607849 0.99607849
0.99607849 0.99607849 0.99607849 0.99607849 0.99607849 0.99607849
0.99607849 0.99607849 0.99607849 0.99607849 0.74117649 0.09019608
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0.88627458 0.99607849 0.81568635
0.78039223 0.78039223 0.78039223 0.78039223 0.54509807 0.2392157
0.2392157 0.2392157 0.2392157 0.2392157 0.50196081 0.8705883
0.99607849 0.99607849 0.74117649 0.08235294 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.14901961 0.32156864 0.0509804 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0.13333334 0.83529419 0.99607849 0.99607849 0.45098042 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0.32941177 0.99607849 0.99607849 0.91764712 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0.32941177 0.99607849 0.99607849 0.91764712 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0.41568631 0.6156863 0.99607849 0.99607849 0.95294124 0.20000002
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0.09803922 0.45882356 0.89411771
0.89411771 0.89411771 0.99215692 0.99607849 0.99607849 0.99607849
0.99607849 0.94117653 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0.26666668 0.4666667 0.86274517
0.99607849 0.99607849 0.99607849 0.99607849 0.99607849 0.99607849
0.99607849 0.99607849 0.99607849 0.55686277 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0.14509805 0.73333335 0.99215692
0.99607849 0.99607849 0.99607849 0.87450987 0.80784321 0.80784321
0.29411766 0.26666668 0.84313732 0.99607849 0.99607849 0.45882356
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.44313729
0.8588236 0.99607849 0.94901967 0.89019614 0.45098042 0.34901962
0.12156864 0. 0. 0. 0. 0.7843138
0.99607849 0.9450981 0.16078432 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0.66274512 0.99607849 0.6901961 0.24313727 0. 0.
0. 0. 0. 0. 0. 0.18823531
0.90588242 0.99607849 0.91764712 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0.07058824 0.48627454 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.32941177 0.99607849 0.99607849 0.65098041 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0.54509807 0.99607849 0.9333334 0.22352943 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.82352948 0.98039222 0.99607849 0.65882355 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0.94901967 0.99607849 0.93725497 0.22352943 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.34901962 0.98431379 0.9450981 0.33725491 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.01960784 0.80784321 0.96470594 0.6156863 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0.01568628 0.45882356 0.27058825 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. 0. 0. 0. 0. 0. 0. 1. 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")