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ChainerDeep Learning

ID: jnishi 2006 Preferred Infrastructure2016

Deep Learning(DL)2015 3DL2015 6Chainer NesterovAG ClippedReLU Connectionist Temporal Classification

Torch7: Baidu20161TensorFlow: 20162Chainer: 201510

Deep Learning!

Deep Learning!

Deep Learning!

Deep Learning!

OK!

Deep Learning!

OK!

BaiduDeep Specch2

Google

Microsoft

Deep Learning

x1x2xnn1w1w2wnu = w1x1 + w2x2 + + wnxn

x1x2xnnm

()

x1x2xnnm

Linear

x1x2xnuReLU: 0sigmoid: 01tanh: -11

LinearReLU

LinearConvolutionetc.

BaiduDeep Specch2

BaiduDeep Specch2

BaiduDeep Specch2

Convolution3

BaiduDeep Specch2

RNN7

BaiduDeep Specch2

BatchNormalization

BaiduDeep Specch2

Linear1

BaiduDeep Specch2

CTC

Deep Learningforwardback propagation

Chainerexampleclass MLP(Chain): def __init__(self, n_units=100, n_out=10): super(MLP, self).__init__( l1=L.Linear(None, n_units), l2=L.Linear(None, n_units), l3=L.Linear(None, n_out), ) def __call__(self, x): h1 = F.relu(self.l1(x)) h2 = F.relu(self.l2(h1)) y = self.l3(h2) return yLinearMNISTLinearLinear784(28x28)100100010

layer1class MLP(Chain): def __init__(self, n_units=100, n_out=10): super(MLP, self).__init__( l1=L.Linear(None, n_units), l2=L.Linear(None, n_units), l3=L.Linear(None, n_out), ) def __call__(self, x): h1 = F.relu(self.l1(x)) h2 = F.relu(self.l2(h1)) y = self.l3(h2) return yLinearMNISTLinearLinear784(28x28)100100010l1

layer2class MLP(Chain): def __init__(self, n_units=100, n_out=10): super(MLP, self).__init__( l1=L.Linear(None, n_units), l2=L.Linear(None, n_units), l3=L.Linear(None, n_out), ) def __call__(self, x): h1 = F.relu(self.l1(x)) h2 = F.relu(self.l2(h1)) y = self.l3(h2) return yLinearMNISTLinearLinear784(28x28)100100010l1l2

layer3class MLP(Chain): def __init__(self, n_units=100, n_out=10): super(MLP, self).__init__( l1=L.Linear(None, n_units), l2=L.Linear(None, n_units), l3=L.Linear(None, n_out), ) def __call__(self, x): h1 = F.relu(self.l1(x)) h2 = F.relu(self.l2(h1)) y = self.l3(h2) return yLinearMNISTLinearLinear784(28x28)100100010l1l2l3

forwardclass MLP(Chain): def __init__(self, n_units=100, n_out=10): super(MLP, self).__init__( l1=L.Linear(None, n_units), l2=L.Linear(None, n_units), l3=L.Linear(None, n_out), ) def __call__(self, x): h1 = F.relu(self.l1(x)) h2 = F.relu(self.l2(h1)) y = self.l3(h2) return yLinearMNISTLinearLinearxh1h20yl1l2l3

forwardclass MLP(Chain): def __init__(self, n_units=100, n_out=10): super(MLP, self).__init__( l1=L.Linear(None, n_units), l2=L.Linear(None, n_units), l3=L.Linear(None, n_out), ) def __call__(self, x): h1 = F.relu(self.l1(x)) h2 = F.relu(self.l2(h1)) y = self.l3(h2) return yLinearMNISTLinearLinearxh1h20yl1l2l3

forwardclass MLP(Chain): def __init__(self, n_units=100, n_out=10): super(MLP, self).__init__( l1=L.Linear(None, n_units), l2=L.Linear(None, n_units), l3=L.Linear(None, n_out), ) def __call__(self, x): h1 = F.relu(self.l1(x)) h2 = F.relu(self.l2(h1)) y = self.l3(h2) return yLinearMNISTLinearLinearxh1h20yl1l2l3

Deep Learning

ChainerMNISTtrain_example.pyChainer