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
GNU bash, version 4.3.8(1)-release (x86_64-pc-linux-gnu)
概要
http://qiita.com/7of9/items/09262a2ab01d037d169b
にて学習したネットワークをもとに、入力データを与えてpredictionに相当するものを出力する。
後々の画像化を考慮して、入力データのファイル（input.csv)と同じ形式で出力するようにした。
code v0.1
reproduce_170429.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import math
import sys
'''
v0.1 Apr. 29, 2017
- output by replacing the learned [Exr]
- branched from [check_resultmap_170329.ipynb]
'''
# to ON/OFF debug print at one place
def output_debugPrint(str):
#print(str)
pass # no operation
def calc_sigmoid(x):
return 1.0 / (1.0 + math.exp(-x))
def calc_conv(src, weight, bias, applyActFnc):
wgt = weight.shape
# print wgt # debug
#conv = list(range(bias.size))
conv = [0.0] * bias.size
# weight
for idx1 in range(wgt[0]):
for idx2 in range(wgt[1]):
conv[idx2] = conv[idx2] + src[idx1] * weight[idx1, idx2]
# bias
for idx2 in range(wgt[1]):
conv[idx2] = conv[idx2] + bias[idx2]
# activation function
if applyActFnc:
for idx2 in range(wgt[1]):
conv[idx2] = calc_sigmoid(conv[idx2])
return conv # return list
model_var = np.load('model_variables_170429.npy')
learn_data = np.loadtxt('input.csv', delimiter=',')
prd_data = np.array([])
for idx, items in enumerate(learn_data):
if len(items) < 1: # avoid empty line
break
#if idx >= 5: # for debug
# break
print(idx)
# input layer (3 node)
inlist = (items[0], items[1], items[2])
# hidden layer 1
outdata = calc_conv(inlist, model_var[0], model_var[1], applyActFnc=True)
# hidden layer 2
outdata = calc_conv(outdata, model_var[2], model_var[3], applyActFnc=True)
# hidden layer 3
outdata = calc_conv(outdata, model_var[4], model_var[5], applyActFnc=True)
# output layer
outdata = calc_conv(outdata, model_var[6], model_var[7], applyActFnc=False)
wrk = [
items[0], items[1], items[2], # x,y,z
outdata[0], items[4], # Exr, Exi
items[5], items[6], # Eyr, Eyi
items[7], items[8] # Ezr, Ezi
]
#print(wrk)
prd_data = np.append(prd_data, wrk)
print('idx=',idx)
NUM_ITEMS = 9
ncols = len(prd_data) / NUM_ITEMS
prd_data = np.array(prd_data).reshape(ncols, NUM_ITEMS)
np.savetxt("input_Exr_replaced_170429.csv", prd_data, delimiter=","
, fmt="%.9f")
結果
$ head input_Exr_replaced_170429.csv
-0.209439510,-1.466076572,-5.235987756,0.068316654,0.113688322,0.519136168,0.356408494,-0.109750412,0.005652638
0.209439510,-1.466076572,-5.235987756,0.088643790,0.075268157,0.806765822,0.313605899,-0.087339272,0.002273401
-1.047197551,-1.047197551,-5.235987756,0.025312938,0.105477041,-0.151919209,0.284581139,-0.040284662,0.014306838
-0.628318531,-1.047197551,-5.235987756,0.040992646,0.090998304,0.132621814,0.389916050,-0.049129976,0.030650874
-0.209439510,-1.047197551,-5.235987756,0.050157650,0.044499152,0.422640054,0.416238663,-0.054321067,0.030099672
0.209439510,-1.047197551,-5.235987756,0.058198942,0.081592287,0.696098762,0.228374544,-0.029275715,0.000805544
0.628318531,-1.047197551,-5.235987756,0.065595694,-0.026843276,0.797857025,0.040918210,-0.023928849,-0.034062430
1.047197551,-1.047197551,-5.235987756,0.065374163,-0.055698835,0.728159227,-0.100947639,-0.034184970,-0.041232617
-1.047197551,-0.628318531,-5.235987756,0.016307764,0.083149606,-0.275568958,0.256085316,-0.040395470,0.006757636
-0.628318531,-0.628318531,-5.235987756,0.027492900,0.041832652,0.087782092,0.394477943,-0.037243314,0.020279789
遅い
numpyをうまく使えていないので、すごく遅い。
9327行の処理で３分かかる。
real 2m57.543s
user 2m56.590s
sys 0m0.485s
numpyの扱いをもっと学ばないといけない。