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):
# 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):
