Jupyter + Matplotlib > 横軸の表記がscientific exponential notation(例: 1e7)になるのを抑止する > ax1.get_xaxis().get_major_formatter().set_scientific(False)

動作環境

GeForce GTX 1070 (8GB)
ASRock Z170M Pro4S [Intel Z170chipset]
Ubuntu 16.04 LTS desktop amd64
TensorFlow v1.1.0
cuDNN v5.1 for Linux
CUDA v8.0
Python 3.5.2
IPython 6.0.0 -- An enhanced Interactive Python.
gcc (Ubuntu 5.4.0-6ubuntu1~16.04.4) 5.4.0 20160609
GNU bash, version 4.3.48(1)-release (x86_64-pc-linux-gnu)

TensorFlowの学習step数を増やしたことにより、グラフの横軸の表記が1e7のようなscientific exponential notationになってしまうようになった。

元の数字の表記方法は以下で見つけました。
https://stackoverflow.com/questions/14711655/how-to-prevent-numbers-being-changed-to-exponential-form-in-python-matplotlib-fi

ax1.get_xaxis().get_major_formatter().set_scientific(False)

Jupyter code.

check_result_170722.ipynb

%matplotlib inline

# learning [Exr,Exi,Eyr,Eyi,Ezr,Ezi] from ADDA
# Jul. 31, 2017

import numpy as np
import matplotlib.pyplot as plt


def moving_average(a, n=3) :
    # from
    # https://stackoverflow.com/questions/14313510/how-to-calculate-moving-average-using-numpy
    ret = np.cumsum(a, dtype=float)
    ret[n:] = ret[n:] - ret[:-n]
    return ret[n - 1:] / n


def add_lineplot(filepath, ax1, alabel):
    data1 = np.loadtxt(filepath, delimiter=',')
    input1 = data1[:,0]
    output1 = data1[:,1]

    # moving average
    NUM_AVERAGE = 500
    output1 = moving_average(output1, n=NUM_AVERAGE)
    for loop in range(NUM_AVERAGE - 1):
        output1 = np.append(output1, 1e-7)  # dummy

    ax1.plot(input1, output1, label=alabel)
    
fig = plt.figure(figsize=(10,10),dpi=200)
ax1 = fig.add_subplot(2,1,1)
ax1.grid(True)
ax1.set_xlabel('step')
ax1.set_ylabel('loss')
ax1.set_yscale('log')
# ax1.set_xlim([0, 3000000])
ax1.set_xlim([0e6, 3e7])  # Jul. 31, 2017
ax1.set_ylim([1e-5, 1.0])

# --- learning rate=0.001, batch_size=4
FILE_PATH1 = 'RES_170728_t0716//log_learn.170727_t2308'
# --- learning rate=0.0001, batch_size=4
# FILE_PATH1 = 'RES_170728_t2119/log_learn.170728_t0720'
# FILE_PATH1 = 'RES_170728_t2119/log_learn.170728_t0751'
# FILE_PATH1 = 'RES_170728_t2119/log_learn.170728_t0821'
# FILE_PATH1 = 'RES_170728_t2119/log_learn.170728_t0850'
# FILE_PATH1 = 'RES_170728_t2119/log_learn.170728_t0921'
# FILE_PATH1 = 'RES_170728_t2119/log_learn.170728_t0951'
# FILE_PATH2 = 'RES_170728_t2119/log_learn.170728_t1050'
# --- batch_size=2
FILE_PATH2 = 'RES_170729_t0744//log_learn.170728_t2311'
# --- batch_size=20
# FILE_PATH3 = 'RES_170728_t2311/log_learn.170728_t2208'

# for [10x30x100] 2017/07/29 18:45
FILE_PATH3 = 'log_learn.170731_t2045'

# for learning rage=0.001, 30x100x100
add_lineplot(FILE_PATH2, ax1, alabel="batch size=2")
add_lineplot(FILE_PATH1, ax1, alabel="batch size=4")
add_lineplot(FILE_PATH3, ax1, alabel="batch size=2")

ax1.legend()

ax1.get_xaxis().get_major_formatter().set_scientific(False)

step=3e7の頃にはlossは1e-6になるだろう(hopefully)。