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dbn.py
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dbn.py
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# -*- coding: utf-8 -*-
import time
import os
from cnn import CNN
from utility import load_image, makeFolder, saveImage, saveFeatures, saveW, load_result_image
from rbm import RBM
if __name__ == '__main__':
data_path = 'data/kouryu_room/image_gray'
# file_num = 526
file_num = 100
isRGB = False
pre_train_lr = 0.1
pre_train_epoch = 1000
node_shape = ((64, 48), (58, 42), (26, 18))
# node_shape = ((80,52), (74,46), (34,20))
filter_shift_list = ((1, 1), (2, 2))
input_shape = [64, 48]
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')
# 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)
# rbm_size_list = (468, 234, 117, 58, 29, 14, 7, 7)
rbm_size_list = (468, 234, 117, 58, 30, 14, 7, 7)
result_path = 'data/kouryu_room/cnn2_after_training'
result_data = load_result_image(result_path, file_num, isRGB)
# result_path = 'data/4position_rumba/image7000/rbm1_train3434'
# result_W = loadW(result_path)
makeFolder()
# def __init__(self, W, input, data_size,input_size, output_size, isDropout):
rbm1 = RBM(None, result_data, file_num, rbm_size_list[0], rbm_size_list[1])
for i in xrange(pre_train_epoch):
print 'rbm1 pre_train:' + str(i)
rbm1.contrast_divergence(i)
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])
for i in xrange(pre_train_epoch):
print 'rbm2 pre_train:' + str(i)
rbm2.contrast_divergence(i)
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])
for i in xrange(pre_train_epoch):
print 'rbm3 pre_train:' + str(i)
rbm3.contrast_divergence(i)
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])
for i in xrange(pre_train_epoch):
print 'rbm4 pre_train:' + str(i)
rbm4.contrast_divergence(i)
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(i)
# 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(i)
# 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(i)
# 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(rbm4.output(), 'feature_RBM30.txt')
# saveFeatures(result_output, 'feature_normal.txt')
os.chdir('../../')
# f = open('data.csv', 'ab')
# csvWriter = csv.writer(f)
# csvWriter.writerow(result_output)
# f.close()
time2 = time.clock()
time = time2-time1
time = int(time)
time = str(time)
print 'total_time:' + time