TensorFlow에서 input : 100, output : 100 네트워크 학습을 고려 v0.5 > 백업 기능/100 패턴 출력

15925 단어 borgWarp#migrated
운영 환경
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

v0.1 ぃ tp // m / 7, f9 / ms / 8b43357b, 1f1b, 4b

TensorFlow를 사용하여 input : 100, output : 100 정도의 네트워크 학습을 할까 검토 중.

v0.5



구현 내용


  • v0.5
  • csv 파일을 덮어 쓰기 전에 bak 파일을 만들도록했습니다.
  • 오작동 방지

  • 5 패턴 만 표시하고 100 패턴 파일 출력
  • PEP8 coding rule 준수


  • code



    Jupyter 코드

    in100_out100.ipynb
    import numpy as np
    import matplotlib.pyplot as plt
    import matplotlib.cm as cm
    import os
    
    '''
    v0.5 Feb. 06, 2017
      - add [FILENAME_*_CSV],[FILENAME_*_BAK]
      - output to csv with append mode
    v0.4 Jan. 21, 2017
      - set size of figures
    v0.3 Jan. 21, 2017
      - show 2 images in one figure
    v0.2 Jan. 14, 2017
      - calcOutput() return in numpy.array
      - add saveToCsvFile()
    v0.1 Jan. 14, 2017
      - add calcOutput()
      - add showIn2D()
      - show 1d in 2d format
    '''
    
    '''
    codingrule:PEP8
    '''
    
    XDIM = 10
    YDIM = 10
    INDIM = XDIM * YDIM
    
    FILENAME_IN_CSV = 'test_in.csv'
    FILENAME_OUT_CSV = 'test_out.csv'
    FILENAME_IN_BAK = 'test_in.bak'
    FILENAME_OUT_BAK = 'test_out.bak'
    
    
    def saveToCsvFile(data_1d, filename):
        wrk_1d = data_1d.reshape(1, INDIM)
        # np.savetxt(filename, wrk_1d, delimiter=',')
        with open(filename, 'a') as fd:
            np.savetxt(fd, wrk_1d)
    
    
    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 np.array(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()
    
    
    def showIn2D_2image_subplot_each(data1_1d, data2_1d):
        data1_2d = np.reshape(data1_1d, (XDIM, YDIM))
        data2_2d = np.reshape(data2_1d, (XDIM, YDIM))
        fig1 = plt.figure(1)
        fig1.set_size_inches(3.14, 3.14)
        plt.subplot(121)
        plt.title('input node')
        plt.imshow(data1_2d, extent=(0, XDIM, 0, YDIM), cmap=cm.gist_rainbow)
        plt.subplot(122)
        plt.title('output node')
        plt.imshow(data2_2d, extent=(0, XDIM, 0, YDIM), cmap=cm.gist_rainbow)
        plt.show()
    
    
    def showIn2D_2image_subplot_first(data1_1d, data2_1d):
        data1_2d = np.reshape(data1_1d, (XDIM, YDIM))
        data2_2d = np.reshape(data2_1d, (XDIM, YDIM))
        fig, (axL, axR) = plt.subplots(ncols=2, figsize=(10, 4))
        axL.imshow(data1_2d, extent=(0, XDIM, 0, YDIM), cmap=cm.gist_rainbow)
        axL.grid(True)
        axR.imshow(data2_2d, extent=(0, XDIM, 0, YDIM), cmap=cm.gist_rainbow)
        axR.grid(True)
        fig.show()
    
    
    if __name__ == '__main__':
        # backup the original csv files
        if os.path.exists(FILENAME_IN_CSV):
            os.rename(FILENAME_IN_CSV, FILENAME_IN_BAK)
        if os.path.exists(FILENAME_OUT_CSV):
            os.rename(FILENAME_OUT_CSV, FILENAME_OUT_BAK)
    
        # append csv
        NUM_FILEOUT = 100
        NUM_DISPLAY = 5
        for loop in range(NUM_FILEOUT):
            in_1d = np.random.rand(INDIM)
            out_1d = calcOutput(in_1d)
            saveToCsvFile(in_1d, FILENAME_IN_CSV)
            saveToCsvFile(out_1d, FILENAME_OUT_CSV)
            if loop < NUM_DISPLAY:
                showIn2D_2image_subplot_each(in_1d, out_1d)
                showIn2D_2image_subplot_first(in_1d, out_1d)
    

    결과



    input node의 내용을 180도 회전한 output node를 출력. 노드 수를 변경해, 노드수 증대시의 Deep Learning 학습의 요령을 잡는다 (to get the hang of it).
    5개만 이미지 표시.
    test_in.csv와 test_out.csv가 생성된다.
    (백업 파일 test_in.bak 및 test_out.bak)

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