#?-*-?coding:?utf-8?-*-

‘‘‘
?DBN??w/?continuous-valued?inputs?（Linear?Energy）

?References?:
???-?Y.?Bengio?P.?Lamblin?D.?Popovici?H.?Larochelle:?Greedy?layer-Wise
???Training?of?Deep?Networks?Advances?in?Neural?Information?Processing
???Systems?19?2007

‘‘‘

import?sys
import?numpy
from?Hiddenlayer?import?Hiddenlayer
from?LogisticRegression?import?LogisticRegression
from?RBM?import?RBM
from?CRBM?import?CRBM
from?DBN?import?DBN
from?utils?import?*



?
class?CDBN（DBN）:
????def?__init__（self?input=None?label=None\
?????????????????n_ins=2?hidden_layer_sizes=[3?3]?n_outs=2\
?????????????????numpy_rng=None）:
????????
????????self.x?=?input
????????self.y?=?label

????????self.sigmoid_layers?=?[]
????????self.rbm_layers?=?[]
????????self.n_layers?=?len（hidden_layer_sizes）??#?=?len（self.rbm_layers）

????????if?numpy_rng?is?None:
????????????numpy_rng?=?numpy.random.RandomState（1234）

????????
????????assert?self.n_layers?>?0


????????#?construct?multi-layer
????????for?i?in?xrange（self.n_layers）:
????????????#?layer_size
????????????if?i?==?0:
????????????????input_size?=?n_ins
????????????else:
????????????????input_size?=?hidden_layer_sizes[i?-?1]

????????????#?layer_input
????????????if?i?==?0:
????????????????layer_input?=?self.x
????????????else:
????????????????layer_input?=?self.sigmoid_layers[-1].sample_h_given_v（）
????????????????
????????????#?construct?sigmoid_layer
????????????sigmoid_layer?=?Hiddenlayer（input=layer_input
????????????????????????????????????????n_in=input_size
????????????????????????????????????????n_out=hidden_layer_sizes[i]
????????????????????????????????????????numpy_rng=numpy_rng
????????????????????????????????????????activation=sigmoid）
????????????self.sigmoid_layers.append（sigmoid_layer）

????????????#?construct?rbm_layer
????????????if?i?==?0:
????????????????rbm_layer?=?CRBM（input=layer_input?????#?continuous-valued?inputs
?????????????????????????????????n_visible=input_size
?????????????????????????????????n_hidden=hidden_layer_sizes[i]
?????????????????????????????????W=sigmoid_layer.W?????#?W?b?are?shared
?????????????????????????????????hbias=sigmoid_layer.b）
????????????else:
????????????????rbm_layer?=?RBM（input=layer_input
????????????????????????????????n_visible=input_size
????????????????????????????????n_hidden=hidden_layer_sizes[i]
????????????????????????????????W=sigmoid_layer.W?????#?W?b?are?shared
????????????????????????????????hbias=sigmoid_layer.b）
????????????????
????????????self.rbm_layers.append（rbm_layer）


????????#?layer?for?output?using?Logistic?Regression
????????self.log_layer?=?LogisticRegression（input=self.sigmoid_layers[-1].sample_h_given_v（）
????????????????????????????????????????????label=self.y
????????????????????????????????????????????n_in=hidden_layer_sizes[-1]
????????????????????????????????????????????n_out=n_outs）

????????#?finetune?cost:?the?negative?log?likelihood?of?the?logistic?regression?layer
????????self.finetune_cost?=?self.lo

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????目錄???????????0??2019-01-02?15:50??深度信念網(wǎng)絡(luò)（DBN）代碼\
?????文件????????4264??2019-01-02?15:50??深度信念網(wǎng)絡(luò)（DBN）代碼\CDBN.py
?????文件????????1831??2019-01-02?15:50??深度信念網(wǎng)絡(luò)（DBN）代碼\CRBM.py
?????文件????????6615??2019-01-02?15:51??深度信念網(wǎng)絡(luò)（DBN）代碼\DBN.py
?????文件????????2058??2019-01-02?15:51??深度信念網(wǎng)絡(luò)（DBN）代碼\Hiddenlayer.py
?????文件????????3455??2019-01-02?15:51??深度信念網(wǎng)絡(luò)（DBN）代碼\LogisticRegression.py
?????文件????????6080??2019-01-02?15:51??深度信念網(wǎng)絡(luò)（DBN）代碼\RBM.py
?????文件????????5855??2019-01-02?15:51??深度信念網(wǎng)絡(luò)（DBN）代碼\SdA.py
?????文件????????4846??2019-01-02?15:51??深度信念網(wǎng)絡(luò)（DBN）代碼\dA.py
?????文件?????????538??2019-01-02?15:51??深度信念網(wǎng)絡(luò)（DBN）代碼\utils.py