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dbn_slotcar.py
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dbn_slotcar.py
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# -*- coding: utf-8 -*-
import time
# import numpy
# import csv
import os
from cnn import CNN
from utility import load_image, makeFolder, saveImage, saveFeatures, saveW
from rbm import RBM
if __name__ == '__main__':
data_path = 'data/4position_rumba/image_gray'
file_num = 3
isRGB = False
pre_train_lr = 0.1
pre_train_epoch = 10
# node_shape = ((80,52), (74,46), (35,21))
node_shape = ((80, 52), (74, 46), (34, 20))
filter_shift_list = ((1, 1), (2, 2))
input_shape = [80, 52]
filter_shape = [7, 7]
data_list = load_image(data_path, file_num, isRGB)
makeFolder()
# 時間計測
time1 = time.clock()
cnn1 = CNN(data_list, filter_shape, filter_shift_list[0], input_shape, node_shape[1], pre_train_lr, pre_train_epoch)
output_list = cnn1.output()
saveImage(output_list, node_shape[1], 'cnn1_before_training')
cnn1.pre_train()
output_list = cnn1.output()
saveImage(output_list, node_shape[1], 'cnn1_after_training')
# for i in xrange(pre_train_epoch):
# cnn1.pre_train()
# output_list = cnn1.output()
# saveImage(output_list, (74,46))
cnn2 = CNN(cnn1.output(), filter_shape, filter_shift_list[1], node_shape[1], node_shape[2], pre_train_lr, pre_train_epoch)
output_list = cnn2.output()
saveImage(output_list, node_shape[2], 'cnn2_before_train')
cnn2.pre_train()
output_list = cnn2.output()
saveImage(output_list, node_shape[2], 'cnn2_after_train')
rbm_size_list = (680, 340, 170, 85, 42, 21, 10, 3)
# def __init__(self, W, input, data_size,input_size, output_size, isDropout):
rbm1 = RBM(None, cnn2.output(), file_num, rbm_size_list[0], rbm_size_list[1], False)
for i in xrange(pre_train_epoch):
print 'rbm1 pre_train:' + str(i)
rbm1.contrast_divergence()
reinput = rbm1.reconstruct_from_input(rbm1.input)
saveImage(reinput, node_shape[2], 'rbm1_after_train')
saveW(rbm1.getW(), 'rbm1_after_train')
rbm2 = RBM(None, rbm1.output(), file_num, rbm_size_list[1], rbm_size_list[2], False)
for i in xrange(pre_train_epoch):
print 'rbm2 pre_train:' + str(i)
rbm2.contrast_divergence()
reinput = rbm2.reconstruct_from_input(rbm2.input)
reinput = rbm1.reconstruct_from_output(reinput)
saveImage(reinput, node_shape[2], 'rbm2_after_train')
saveW(rbm2.getW(), 'rbm2_after_train')
rbm3 = RBM(None, rbm2.output(), file_num, rbm_size_list[2], rbm_size_list[3], False)
for i in xrange(pre_train_epoch):
print 'rbm3 pre_train:' + str(i)
rbm3.contrast_divergence()
reinput = rbm3.reconstruct_from_input(rbm3.input)
reinput = rbm2.reconstruct_from_output(reinput)
reinput = rbm1.reconstruct_from_output(reinput)
saveImage(reinput, node_shape[2], 'rbm3_after_train')
saveW(rbm3.getW(), 'rbm3_after_train')
rbm4 = RBM(None, rbm3.output(), file_num, rbm_size_list[3], rbm_size_list[4], False)
for i in xrange(pre_train_epoch):
print 'rbm4 pre_train:' + str(i)
rbm4.contrast_divergence()
reinput = rbm4.reconstruct_from_input(rbm4.input)
reinput = rbm3.reconstruct_from_output(reinput)
reinput = rbm2.reconstruct_from_output(reinput)
reinput = rbm1.reconstruct_from_output(reinput)
saveImage(reinput, node_shape[2], 'rbm4_after_train')
saveW(rbm4.getW(), 'rbm4_after_train')
rbm5 = RBM(None, rbm4.output(), file_num, rbm_size_list[4], rbm_size_list[5], False)
for i in xrange(pre_train_epoch):
print 'rbm5 pre_train:' + str(i)
rbm5.contrast_divergence()
reinput = rbm5.reconstruct_from_input(rbm5.input)
reinput = rbm4.reconstruct_from_output(reinput)
reinput = rbm3.reconstruct_from_output(reinput)
reinput = rbm2.reconstruct_from_output(reinput)
reinput = rbm1.reconstruct_from_output(reinput)
saveImage(reinput, node_shape[2], 'rbm5_after_train')
saveW(rbm5.getW(), 'rbm5_after_train')
rbm6 = RBM(None, rbm5.output(), file_num, rbm_size_list[5], rbm_size_list[6], False)
for i in xrange(pre_train_epoch):
print 'rbm6 pre_train:' + str(i)
rbm6.contrast_divergence()
reinput = rbm6.reconstruct_from_input(rbm6.input)
reinput = rbm5.reconstruct_from_output(reinput)
reinput = rbm4.reconstruct_from_output(reinput)
reinput = rbm3.reconstruct_from_output(reinput)
reinput = rbm2.reconstruct_from_output(reinput)
reinput = rbm1.reconstruct_from_output(reinput)
saveImage(reinput, node_shape[2], 'rbm6_after_train')
saveW(rbm6.getW(), 'rbm6_after_train')
rbm7 = RBM(None, rbm6.output(), file_num, rbm_size_list[6], rbm_size_list[7], False)
for i in xrange(pre_train_epoch):
print 'rbm7 pre_train:' + str(i)
rbm7.contrast_divergence()
reinput = rbm7.reconstruct_from_input(rbm7.input)
reinput = rbm6.reconstruct_from_output(reinput)
reinput = rbm5.reconstruct_from_output(reinput)
reinput = rbm4.reconstruct_from_output(reinput)
reinput = rbm3.reconstruct_from_output(reinput)
reinput = rbm2.reconstruct_from_output(reinput)
reinput = rbm1.reconstruct_from_output(reinput)
saveImage(reinput, node_shape[2], 'rbm7_after_train')
saveW(rbm7.getW(), 'rbm7_after_train')
result_output = rbm7.output()
print result_output
saveFeatures(result_output)
os.chdir('../../')
# f = open('data.csv', 'ab') #ファイルが無ければ作る、の'a'を指定します
# csvWriter = csv.writer(f)
# csvWriter.writerow(result_output) #1行書き込み
# f.close()
time2 = time.clock()
time = time2-time1
time = int(time)
time = str(time)
print 'total_time:' + time