network3py.docx

network3py.docx

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network3py

"""network3.py

~~~~~~~~~~~~~~

ATheano-basedprogramfortrainingandrunningsimpleneural

networks.

Supportsseverallayertypes（fullyconnected,convolutional,max

pooling,softmax）,andactivationfunctions（sigmoid,tanh,and

rectifiedlinearunits,withmoreeasilyadded）.

WhenrunonaCPU,thisprogramismuchfasterthannetwork.pyand

network2.py.However,unlikenetwork.pyandnetwork2.pyitcanalso

berunonaGPU,whichmakesitfasterstill.

BecausethecodeisbasedonTheano,thecodeisdifferentinmany

waysfromnetwork.pyandnetwork2.py.However,wherepossibleIhave

triedtomaintainconsistencywiththeearlierprograms.In

particular,theAPIissimilartonetwork2.py.NotethatIhave

focusedonmakingthecodesimple,easilyreadable,andeasily

modifiable.Itisnotoptimized,andomitsmanydesirablefeatures.

ThisprogramincorporatesideasfromtheTheanodocumentationon

convolutionalneuralnets（notably,

）,fromMishaDenil's

implementationofdropout（）,and

fromChrisOlah（http:

//colah.github.io）.

"""

####Libraries

#Standardlibrary

importcPickle

importgzip

#Third-partylibraries

importnumpyasnp

importtheano

importtheano.tensorasT

fromtheano.tensor.nnetimportconv

fromtheano.tensor.nnetimportsoftmax

fromtheano.tensorimportshared_randomstreams

fromtheano.tensor.signalimportdownsample

#Activationfunctionsforneurons

deflinear（z）:

returnz

defReLU（z）:

returnT.maximum（0.0,z）

fromtheano.tensor.nnetimportsigmoid

fromtheano.tensorimporttanh

####Constants

GPU=False

ifGPU:

print"TryingtorununderaGPU.Ifthisisnotdesired,thenmodify"+\

"network3.py\ntosettheGPUflagtoFalse."

try:

theano.config.device='gpu'

except:

pass#it'salreadyset

theano.config.floatX='float32'

else:

print"RunningwithaCPU.Ifthisisnotdesired,thenthemodify"+\

"network3.pytoset\ntheGPUflagtoTrue."

####LoadtheMNISTdata

defload_data_shared（filename="../data/mnist.pkl.gz"）:

f=gzip.open（filename,'rb'）

training_data,validation_data,test_data=cPickle.load（f）

f.close（）

defshared（data）:

"""Placethedataintosharedvariables.ThisallowsTheanotocopy

thedatatotheGPU,ifoneisavailable.

"""

shared_x=theano.shared（

np.asarray（data[0][0:

10],dtype=theano.config.floatX）,borrow=True）

shared_y=theano.shared（

np.asarray（data[1][0:

10],dtype=theano.config.floatX）,borrow=True）

returnshared_x,T.cast（shared_y,"int32"）

return[shared（training_data）,shared（validation_data）,shared（test_data）]

####Mainclassusedtoconstructandtrainnetworks

classNetwork（object）:

def__init__（self,layers,mini_batch_size）:

"""Takesalistof`layers`,describingthenetworkarchitecture,and

avalueforthe`mini_batch_size`tobeusedduringtraining

bystochasticgradientdescent.

"""

self.layers=layers

self.mini_batch_size=mini_batch_size

self.params=[paramforlayerinself.layersforparaminlayer.params]

self.x=T.matrix（"x"）

self.y=T.ivector（"y"）

init_layer=self.layers[0]

init_layer.set_inpt（self.x,self.x,self.mini_batch_size）

forjinxrange（1,len（self.layers））:

prev_layer,layer=self.layers[j-1],self.layers[j]

layer.set_inpt（

prev_layer.output,prev_layer.output_dropout,self.mini_batch_size）

self.output=self.layers[-1].output

self.output_dropout=self.layers[-1].output_dropout

defSGD（self,training_data,epochs,mini_batch_size,eta,

validation_data,test_data,lmbda=0.0）:

"""Trainthenetworkusingmini-batchstochasticgradientdescent."""

training_x,training_y=training_data

validation_x,validation_y=validation_data

test_x,test_y=test_data

#computenumberofminibatchesfortraining,validationandtesting

num_training_batches=size（training_data）/mini_batch_size

num_validation_batches=size（validation_data）/mini_batch_size

num_test_batches=size（test_data）/mini_batch_size

#definethe（regularized）costfunction,symbolicgradients,andupdates

l2_norm_squared=sum（[（layer.w**2）.sum（）forlayerinself.layers]）

cost=self.layers[-1].cost（self）+0.5*lmbda*l2_norm_squared/num_training_batches

grads=T.grad（cost,self.params）

updates=[（param,param-eta*grad）

forparam,gradinzip（self.params,grads）]

#definefunctionstotrainamini-batch,andtocomputethe

#accuracyinvalidationandtestmini-batches.

i=T.lscalar（）#mini-batchindex

train_mb=theano.function（

[i],cost,updates=updates,

givens={

self.x:

training_x[i*self.mini_batch_size:

（i+1）*self.mini_batch_size],

self.y:

training_y[i*self.mini_batch_size:

（i+1）*self.mini_batch_size]

}）

validate_mb_accuracy=theano.function（

[i],self.layers[-1].accuracy（self.y）,

givens={

self.x:

validation_x[i*self.mini_batch_size:

（i+1）*self.mini_batch_size],

self.y:

validation_y[i*self.mini_batch_size:

（i+1）*self.mini_batch_size]

}）

test_mb_accuracy=theano.function（

[i],self.layers[-1].accuracy（self.y）,

givens={

self.x:

test_x[i*self.mini_batch_size:

（i+1）*self.mini_batch_size],

self.y:

test_y[i*self.mini_batch_size:

（i+1）*self.mini_batch_size]

}）

self.test_mb_predictions=theano.function（

[i],self.layers[-1].y_out,

givens={

self.x:

test_x[i*self.mini_batch_size:

（i+1）*self.mini_batch_size]

}）

#Dotheactualtraining

best_validation_accuracy=0.0

forepochinxrange（epochs）:

forminibatch_indexinxrange（num_training_batches）:

iteration=num_training_batches*epoch+minibatch_index

ifiteration%1000==0:

print（"Trainingmini-batchnumber{0}".format（iteration））

cost_ij=train_mb（minibatch_index）

if（iteration+1）%num_training_batches==0:

validation_accuracy=np.mean（

[validate_mb_accuracy（j）forjinxrange（num_validation_batches）]）

print（"Epoch{0}:

validationaccuracy{1:

.2%}".format（

epoch,validation_accuracy））

ifvalidation_accuracy>=best_validation_accuracy:

print（"Thisisthebestvalidationaccuracytodate."）

best_validation_accuracy=validation_accuracy

best_iteration=iteration

iftest_data:

test_accuracy=np.mean（

[test_mb_accuracy（j）forjinxrange（num_test_batches）]）

print（'Thecorrespondingtestaccuracyis{0:

.2%}'.format（

test_accuracy））

print（"Finishedtrainingnetwork."）

print（"Bestvalidationaccuracyof{0:

.2%}obtainedatiteration{1}".format（

best_validation_accuracy,best_iteration））

print（"Correspondingtestaccuracyof{0:

.2%}".format（test_accuracy））

####Definelayertypes

classConvPoolLayer（object）:

"""Usedtocreateacombinationofaconvolutionalandamax-pooling

layer.Amoresophisticatedimplementationwouldseparatethe

two,butforourpurposeswe'llalwaysusethemtogether,andit

simplifiesthecode,soitmakessensetocombinethem.

"""

def__init__（self,filter_shape,image_shape,poolsize=（2,2）,

activation_fn=sigmoid）:

"""`filter_shape`isatupleoflength4,whoseentriesarethenumber

offilters,thenumberofinputfeaturemaps,thefilterheight,andthe

filterwidth.

`image_shape`isatupleoflength4,whoseentriesarethe

mini-batchsize,thenumberofinputfeaturemaps,theimage

height,andtheimagewidth.

`poolsize`isatupleoflength2,whoseentriesaretheyand

xpoolingsizes.

"""

self.filter_shape=filter_shape

self.image_shape=image_shape

self.poolsize=poolsize

self.activation_fn=activation_fn

#initializeweightsandbiases

n_out=（filter_shape[0]*np.prod（filter_shape[2:

]）/np.prod（poolsize））

self.w=theano.shared（

np.asarray（

np.random.normal（loc=0,scale=np.sqrt（1.0/n_out）,size=filter_shape）,

dtype=theano.config.floatX）,

borrow=True）

self.b=theano.shared（

np.asarray（

np.random.normal（loc=0,scale=1.0,size=（filter_shape[0],））,

dtype=theano.config.floatX）,

borrow=True）

self.params=[self.w,self.b]

defset_inpt（self,inpt,inpt_dropout,mini_batch_size）:

self.inpt=inpt.reshape（self.image_shape）

conv_out=conv.conv2d（

input=self.inpt,filters=self.w,filter_shape=self.filter_shape,

image_shape=self.image_shape）

pooled_out=downsample.max_pool_2d（

input=conv_out,ds=self.poolsize,ignore_border=True）

self.output=self.activation_fn（

pooled_out+self.b.dimshuffle（'x',0,'x','x'））

self.output_dropout=self.output#nodropoutintheconvolutionallayers

classFullyConnectedLayer（object）:

def__init__（self,n_in,n_out,activation_fn=sigmoid,p_dropout=0.0）:

self.n_in=n_in

self.n_out=n_out

self.activation_fn=activation_fn

self.p_dropout=p_dropout

#Initializeweightsandbiases

self.w=theano.shared（

np.asarray（

np.random.normal（

loc=0.0,scale=np.sqrt（1.0/n_out）,size=（n_in,n_out））,

dtype=theano.config.floatX）,

name='w',borrow=True）

self.b=theano.shared（

np.asarray（np.random.normal（loc=0.0,scale=1.0,size=（n_out,））,

dtype=theano.config.floatX）,

name='b',borrow=True）

self.params=[self.w,self.b]

defset_inpt（self,inpt,inpt_dropout,mini_batch_size）:

self.inpt=inpt.reshape（（mini_batch_size,self.n_in））

self.output=self.activation_fn（

（1-self.p_dropout）*T.dot（self.inpt,self.w）+self.b）

self.y_out=T.argmax（self.output,axis=1）

self.inpt_dropout=dropout_layer（

inpt_dropout.reshape（（mini_batch_size,self.n_in））,self.p_dropout）

self.output_dropout=self.activation_fn（

T.dot（self.inpt_dropout,self.w）+self.b）

defaccuracy（self,y）:

"Returntheaccuracyforthemini-batch."

returnT.mean（T.eq（y,self.y_out））

classSoftmaxLayer（object）:

def__init__（self,n_in,n_out,p_dropout=0.0）:

self.n_in=n_in

self.n_out=n_out

self.p_dropout=p_dropout

#Initializeweightsandbiases

self.w=theano.shared（

np.zeros（（n_in,n_out）,