Python chainer 模块，initializers() 实例源码

def __init__(self,
                 n_class=None, pretrained_model=None, mean=None,
                 initialW=None, initial_bias=None):
        if n_class is None:
            if pretrained_model in self._models:
                n_class = self._models[pretrained_model]['n_class']
            else:
                n_class = 1000

        if mean is None:
            if pretrained_model in self._models:
                mean = self._models[pretrained_model]['mean']
            else:
                mean = _imagenet_mean
        self.mean = mean

        if initialW is None:
            # Employ default initializers used in the original paper.
            initialW = normal.Normal(0.01)
        if pretrained_model:
            # As a sampling process is time-consuming,
            # we employ a zero initializer for faster computation.
            initialW = constant.Zero()
        kwargs = {'initialW': initialW, 'initial_bias': initial_bias}

        super(VGG16, self).__init__()
        with self.init_scope():
            self.conv1_1 = Conv2DActiv(None, 64, 3, 1, 1, **kwargs)
            self.conv1_2 = Conv2DActiv(None, 64, 3, 1, 1, **kwargs)
            self.pool1 = _max_pooling_2d
            self.conv2_1 = Conv2DActiv(None, 128, 3, 1, 1, **kwargs)
            self.conv2_2 = Conv2DActiv(None, 128, 3, 1, 1, **kwargs)
            self.pool2 = _max_pooling_2d
            self.conv3_1 = Conv2DActiv(None, 256, 3, 1, 1, **kwargs)
            self.conv3_2 = Conv2DActiv(None, 256, 3, 1, 1, **kwargs)
            self.conv3_3 = Conv2DActiv(None, 256, 3, 1, 1, **kwargs)
            self.pool3 = _max_pooling_2d
            self.conv4_1 = Conv2DActiv(None, 512, 3, 1, 1, **kwargs)
            self.conv4_2 = Conv2DActiv(None, 512, 3, 1, 1, **kwargs)
            self.conv4_3 = Conv2DActiv(None, 512, 3, 1, 1, **kwargs)
            self.pool4 = _max_pooling_2d
            self.conv5_1 = Conv2DActiv(None, 512, 3, 1, 1, **kwargs)
            self.conv5_2 = Conv2DActiv(None, 512, 3, 1, 1, **kwargs)
            self.conv5_3 = Conv2DActiv(None, 512, 3, 1, 1, **kwargs)
            self.pool5 = _max_pooling_2d
            self.fc6 = Linear(None, 4096, **kwargs)
            self.fc6_relu = relu
            self.fc6_dropout = dropout
            self.fc7 = Linear(None, 4096, **kwargs)
            self.fc7_relu = relu
            self.fc7_dropout = dropout
            self.fc8 = Linear(None, n_class, **kwargs)
            self.prob = softmax

        if pretrained_model in self._models:
            path = download_model(self._models[pretrained_model]['url'])
            chainer.serializers.load_npz(path, self)
        elif pretrained_model:
            chainer.serializers.load_npz(pretrained_model, self)