model.py 文件源码

def setup_loss_net(self):
        """
        Create a network of convolution layers based on the VGG16 architecture from the paper:
        "Very Deep Convolutional Networks for Large-Scale Image Recognition"

        Original source: https://gist.github.com/ksimonyan/211839e770f7b538e2d8
        License: see http://www.robots.ox.ac.uk/~vgg/research/very_deep/

        Based on code in the Lasagne Recipes repository: https://github.com/Lasagne/Recipes
        """
        loss_net = self.network['loss_net']
        loss_net['input'] = InputLayer(shape=self.shape)
        loss_net['conv1_1'] = ConvLayer(loss_net['input'], 64, 3, pad=1, flip_filters=False)
        loss_net['conv1_2'] = ConvLayer(loss_net['conv1_1'], 64, 3, pad=1, flip_filters=False)
        loss_net['pool1'] = PoolLayer(loss_net['conv1_2'], 2)
        loss_net['conv2_1'] = ConvLayer(loss_net['pool1'], 128, 3, pad=1, flip_filters=False)
        loss_net['conv2_2'] = ConvLayer(loss_net['conv2_1'], 128, 3, pad=1, flip_filters=False)
        loss_net['pool2'] = PoolLayer(loss_net['conv2_2'], 2)
        loss_net['conv3_1'] = ConvLayer(loss_net['pool2'], 256, 3, pad=1, flip_filters=False)
        loss_net['conv3_2'] = ConvLayer(loss_net['conv3_1'], 256, 3, pad=1, flip_filters=False)
        loss_net['conv3_3'] = ConvLayer(loss_net['conv3_2'], 256, 3, pad=1, flip_filters=False)
        loss_net['pool3'] = PoolLayer(loss_net['conv3_3'], 2)
        loss_net['conv4_1'] = ConvLayer(loss_net['pool3'], 512, 3, pad=1, flip_filters=False)
        loss_net['conv4_2'] = ConvLayer(loss_net['conv4_1'], 512, 3, pad=1, flip_filters=False)
        loss_net['conv4_3'] = ConvLayer(loss_net['conv4_2'], 512, 3, pad=1, flip_filters=False)
        loss_net['pool4'] = PoolLayer(loss_net['conv4_3'], 2)
        loss_net['conv5_1'] = ConvLayer(loss_net['pool4'], 512, 3, pad=1, flip_filters=False)
        loss_net['conv5_2'] = ConvLayer(loss_net['conv5_1'], 512, 3, pad=1, flip_filters=False)
        loss_net['conv5_3'] = ConvLayer(loss_net['conv5_2'], 512, 3, pad=1, flip_filters=False)