vgg19.py

# VGG-19, 19-layer model from the paper:
# "Very Deep Convolutional Networks for Large-Scale Image Recognition"
# Original source: https://gist.github.com/ksimonyan/3785162f95cd2d5fee77
# License: non-commercial use only

# Download pretrained weights from:
# https://s3.amazonaws.com/lasagne/recipes/pretrained/imagenet/vgg19.pkl

from lasagne.layers import InputLayer, DenseLayer, NonlinearityLayer
from lasagne.layers.dnn import Conv2DDNNLayer as ConvLayer
from lasagne.layers import Pool2DLayer as PoolLayer
from lasagne.nonlinearities import softmax


def build_model():
    net = {}
    net['input'] = InputLayer((None, 3, 224, 224))
    net['conv1_1'] = ConvLayer(net['input'], 64, 3, pad=1)
    net['conv1_2'] = ConvLayer(net['conv1_1'], 64, 3, pad=1)
    net['pool1'] = PoolLayer(net['conv1_2'], 2)
    net['conv2_1'] = ConvLayer(net['pool1'], 128, 3, pad=1)
    net['conv2_2'] = ConvLayer(net['conv2_1'], 128, 3, pad=1)
    net['pool2'] = PoolLayer(net['conv2_2'], 2)
    net['conv3_1'] = ConvLayer(net['pool2'], 256, 3, pad=1)
    net['conv3_2'] = ConvLayer(net['conv3_1'], 256, 3, pad=1)
    net['conv3_3'] = ConvLayer(net['conv3_2'], 256, 3, pad=1)
    net['conv3_4'] = ConvLayer(net['conv3_3'], 256, 3, pad=1)
    net['pool3'] = PoolLayer(net['conv3_4'], 2)
    net['conv4_1'] = ConvLayer(net['pool3'], 512, 3, pad=1)
    net['conv4_2'] = ConvLayer(net['conv4_1'], 512, 3, pad=1)
    net['conv4_3'] = ConvLayer(net['conv4_2'], 512, 3, pad=1)
    net['conv4_4'] = ConvLayer(net['conv4_3'], 512, 3, pad=1)
    net['pool4'] = PoolLayer(net['conv4_4'], 2)
    net['conv5_1'] = ConvLayer(net['pool4'], 512, 3, pad=1)
    net['conv5_2'] = ConvLayer(net['conv5_1'], 512, 3, pad=1)
    net['conv5_3'] = ConvLayer(net['conv5_2'], 512, 3, pad=1)
    net['conv5_4'] = ConvLayer(net['conv5_3'], 512, 3, pad=1)
    net['pool5'] = PoolLayer(net['conv5_4'], 2)
    net['fc6'] = DenseLayer(net['pool5'], num_units=4096)
    net['fc7'] = DenseLayer(net['fc6'], num_units=4096)
    net['fc8'] = DenseLayer(net['fc7'], num_units=1000, nonlinearity=None)
    net['prob'] = NonlinearityLayer(net['fc8'], softmax)

    return net