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Jupyter / matplotlib > 15x15のデータ > 拡大表示されるようだ / TensorFlowで input:100, output:100のネットワークでの学習を検討 v0.1

Last updated at Posted at 2017-01-14
動作環境
GeForce GTX 1070 (8GB)
ASRock Z170M Pro4S [Intel Z170chipset]
Ubuntu 14.04 LTS desktop amd64
TensorFlow v0.11
cuDNN v5.1 for Linux
CUDA v8.0
Python 2.7.6
IPython 5.1.0 -- An enhanced Interactive Python.
gcc (Ubuntu 4.8.4-2ubuntu1~14.04.3) 4.8.4

TensorFlowを使って、input:100, output:100程度のネットワークの学習をしようかと検討中。

学習用データとして、入力データを「反転する」処理を実装してみた。

Jupyterのコード

in100_out100.ipynb
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm

'''
v0.1 Jan. 14, 2017
  - add calcOutput()
  - add showIn2D()
  - show 1d in 2d format
'''

'''
codingrule:PEP8
'''

XDIM = 15
YDIM = 15


def calcOutput(in_1d):
    len_1d = XDIM * YDIM
    out_1d = [0.0] * len_1d
    for idx in range(0, in_1d.size):
        out_1d[idx] = in_1d[len_1d - idx - 1]
    return out_1d


def showIn2D(data_1d):
    # print(data_1d)
    data_2d = np.reshape(data_1d, (XDIM, YDIM))
    plt.imshow(data_2d, extent=(0, XDIM, 0, YDIM), cmap=cm.gist_rainbow)
    plt.show()

if __name__ == '__main__':
    in_1d = np.random.rand(XDIM*YDIM)
    showIn2D(in_1d)
    out_1d = calcOutput(in_1d)
    showIn2D(out_1d)

結果 (15x15)

入力ノードが(15x15 = 225)の場合。
縦横方向に自動的に拡大表示されるようだ。これは便利。

qiita.png

結果 (1000x1000)

入力ノードが(1000x1000 = 1million)の場合。

qiita.png

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