alexnet_batch_normalization.py

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import chainer
import chainer.functions as F
import chainer.links as L


class AlexNetBatchNormalization(chainer.Chain):
    def __init__(self, n_class=1000):
        super(AlexNetBatchNormalization, self).__init__(
            conv1=L.Convolution2D(3,  96, 11, stride=4),
            bn1=L.BatchNormalization(96),
            conv2=L.Convolution2D(96, 256,  5, pad=2),
            bn2=L.BatchNormalization(256),
            conv3=L.Convolution2D(256, 384,  3, pad=1),
            conv4=L.Convolution2D(384, 384,  3, pad=1),
            conv5=L.Convolution2D(384, 256,  3, pad=1),
            fc6=L.Linear(9216, 4096),
            fc7=L.Linear(4096, 4096),
            fc8=L.Linear(4096, n_class))

    def __call__(self, x, t=None):
        h = self.bn1(self.conv1(x))
        h = F.max_pooling_2d(F.relu(h), 3, stride=2)
        h = self.bn2(self.conv2(h))
        h = F.max_pooling_2d(F.relu(h), 3, stride=2)
        h = F.relu(self.conv3(h))
        h = F.relu(self.conv4(h))
        h = F.max_pooling_2d(F.relu(self.conv5(h)), 3, stride=2)
        h = F.dropout(F.relu(self.fc6(h)))
        h = F.dropout(F.relu(self.fc7(h)))
        h = self.fc8(h)

        self.pred = F.softrmax(h)
        if t is None:
            assert not chainer.config.train
            return

        self.loss = F.softmax_cross_entropy(h, t)
        self.accuracy = F.accuracy(h, t)
        return self.loss