nin.py

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import chainer
import chainer.functions as F
import chainer.initializers as I
import chainer.links as L
 
# mainly copied from https://github.com/chainer/chainer/blob/master/examples/imagenet/nin.py
 
 
class NIN(chainer.Chain):
 
    """Network-in-Network example model."""
 
    insize = 227
 
    def __init__(self, n_class=1000):  # 1000 is for ImageNet
        super(NIN, self).__init__()
        conv_init = I.HeNormal()  # MSRA scaling
        self.n_class = n_class
 
        with self.init_scope():
            self.mlpconv1 = L.MLPConvolution2D(
                None, (96, 96, 96), 11, stride=4, conv_init=conv_init)
            self.mlpconv2 = L.MLPConvolution2D(
                None, (256, 256, 256), 5, pad=2, conv_init=conv_init)
            self.mlpconv3 = L.MLPConvolution2D(
                None, (384, 384, 384), 3, pad=1, conv_init=conv_init)
            self.mlpconv4 = L.MLPConvolution2D(
                None, (1024, 1024, self.n_class), 3, pad=1, conv_init=conv_init)
 
    def forward(self, x, t=None):
        h = F.max_pooling_2d(F.relu(self.mlpconv1(x)), 3, stride=2)
        h = F.max_pooling_2d(F.relu(self.mlpconv2(h)), 3, stride=2)
        h = F.max_pooling_2d(F.relu(self.mlpconv3(h)), 3, stride=2)
        h = self.mlpconv4(F.dropout(h))
        h = F.reshape(F.average_pooling_2d(h, 6), (len(x), self.n_class))
 
        self.pred = F.softmax(h)
 
        if t is None:
            assert not chainer.config.train
            return
 
        self.loss = F.softmax_cross_entropy(h, t)
        self.acc = F.accuracy(self.pred, t)
 
        chainer.report({'loss': self.loss, 'accuracy': self.acc}, self)
 
        return self.loss