import?theano
import?theano.tensor?as?T
import?numpy?as?np
import?math
import?time

class?BatchNormalization（object）?:
	def?__init__（self?input_shape?mode=0??momentum=0.9）?:
		‘‘‘
		#?params?:
		input_shape?:
			when?mode?is?0?we?assume?2D?input.?（mini_batch_size?#?features）
			when?mode?is?1?we?assume?4D?input.?（mini_batch_size?#?of?channel?#?row?#?column）
		mode?:?
			0?:?feature-wise?mode?（normal?BN）
			1?:?window-wise?mode?（CNN?mode?BN）
		momentum?:?momentum?for?exponential?average
		‘‘‘
		self.input_shape?=?input_shape
		self.mode?=?mode
		self.momentum?=?momentum
		self.run_mode?=?0?#?run_mode?:?0?means?training?1?means?inference

		self.insize?=?input_shape[1]
		
		#?random?setting?of?gamma?and?beta?setting?initial?mean?and?std
		rng?=?np.random.RandomState（int（time.time（）））
		self.gamma?=?theano.shared（np.asarray（rng.uniform（low=-1.0/math.sqrt（self.insize）?high=1.0/math.sqrt（self.insize）?size=（input_shape[1]））?dtype=theano.config.floatX）?name=‘gamma‘?borrow=True）
		self.beta?=?theano.shared（np.zeros（（input_shape[1]）?dtype=theano.config.floatX）?name=‘beta‘?borrow=True）
		self.mean?=?theano.shared（np.zeros（（input_shape[1]）?dtype=theano.config.floatX）?name=‘mean‘?borrow=True）
		self.var?=?theano.shared（np.ones（（input_shape[1]）?dtype=theano.config.floatX）?name=‘var‘?borrow=True）

		#?parameter?save?for?update
		self.params?=?[self.gamma?self.beta]

	def?set_runmode（self?run_mode）?:
		self.run_mode?=?run_mode

	def?get_result（self?input）?:
		#?returns?BN?result?for?given?input.
		epsilon?=?1e-06

		if?self.mode==0?:
			if?self.run_mode==0?:
				now_mean?=?T.mean（input?axis=0）
				now_var?=?T.var（input?axis=0）
				now_normalize?=?（input?-?now_mean）?/?T.sqrt（now_var+epsilon）?#?should?be?broadcastable..
				output?=?self.gamma?*?now_normalize?+?self.beta
				#?mean?var?update
				self.mean?=?self.momentum?*?self.mean?+?（1.0-self.momentum）?*?now_mean
				self.var?=?self.momentum?*?self.var?+?（1.0-self.momentum）?*?（self.input_shape[0]/（self.input_shape[0]-1）*now_var）
			else?:?
				output?=?self.gamma?*?（input?-?self.mean）?/?T.sqrt（self.var+epsilon）?+?self.beta

		else?:?
			#?in?CNN?mode?gamma?and?beta?exists?for?every?single?channel?separately.
			#?for?each?channel?calculate?mean?and?std?for?（mini_batch_size?*?row?*?column）?elements.
			#?then?each?channel?has?own?scalar?gamma/beta?parameters.
			if?self.run_mode==0?:
				now_mean?=?T.mean（input?axis=（023））
				now_var?=?T.var（input?axis=（023））
				#?mean?var?update
				self.mean?=?self.momentum?*?self.mean?+?（1.0-self.momentum）?*?now_mean
				self.var?=?self.momentum?*?self.var?+?（1.0-self.momentum）?*?（self.input_shape[0]/（self.input_shape[0]-1）*now_var）
			else?:
				now_mean?=?self.mean
				now_var?=?self.var
			#?change?shape?to?fit?input?shape
			now_mean?=?self.change_shape（now_mean）
			now_var?=?self.change_shape（now_var）
			now_gamma?=?self.change_shape（self.gamma）
			now_beta?=?self.change_shape（self.beta）
			
			output?=?now_gamma?*?（input?-?now

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????目錄???????????0??2016-01-12?05:44??Batch-Normalization-master\
?????文件????????2432??2016-01-12?05:44??Batch-Normalization-master\BNConvlayer.py
?????文件????????2020??2016-01-12?05:44??Batch-Normalization-master\BNMLP.py
?????文件????????4733??2016-01-12?05:44??Batch-Normalization-master\BNaddedCNN.py
?????文件????????3263??2016-01-12?05:44??Batch-Normalization-master\BatchNormalization.py
?????文件????????1415??2016-01-12?05:44??Batch-Normalization-master\Convlayer.py
?????文件?????????698??2016-01-12?05:44??Batch-Normalization-master\Dropout.py
?????文件????????2517??2016-01-12?05:44??Batch-Normalization-master\MLP.py
?????文件?????????636??2016-01-12?05:44??Batch-Normalization-master\Poollayer.py
?????文件????????1117??2016-01-12?05:44??Batch-Normalization-master\README.md
?????文件????????4448??2016-01-12?05:44??Batch-Normalization-master\normalCNN.py