theano初始化VGG网络的参数

要初始化VGG网络的参数，可以按照以下步骤：

1. 导入必要的库和模块：

   import numpy as np
   import theano
   import theano.tensor as T
   import lasagne

2. 定义VGG网络的结构，可以参考如下代码：

   def build_model(input_var=None):
       network = lasagne.layers.InputLayer(shape=(None, 3, 224, 224), input_var=input_var)
       network = lasagne.layers.Conv2DLayer(network, num_filters=64, filter_size=(3, 3), stride=1, pad=1, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
       network = lasagne.layers.Conv2DLayer(network, num_filters=64, filter_size=(3, 3), stride=1, pad=1, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
       network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2), stride=2)
       network = lasagne.layers.Conv2DLayer(network, num_filters=128, filter_size=(3, 3), stride=1, pad=1, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
       network = lasagne.layers.Conv2DLayer(network, num_filters=128, filter_size=(3, 3), stride=1, pad=1, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
       network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2), stride=2)
       network = lasagne.layers.Conv2DLayer(network, num_filters=256, filter_size=(3, 3), stride=1, pad=1, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
       network = lasagne.layers.Conv2DLayer(network, num_filters=256, filter_size=(3, 3), stride=1, pad=1, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
       network = lasagne.layers.Conv2DLayer(network, num_filters=256, filter_size=(3, 3), stride=1, pad=1, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
       network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2), stride=2)
       network = lasagne.layers.Conv2DLayer(network, num_filters=512, filter_size=(3, 3), stride=1, pad=1, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
       network = lasagne.layers.Conv2DLayer(network, num_filters=512, filter_size=(3, 3), stride=1, pad=1, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
       network = lasagne.layers.Conv2DLayer(network, num_filters=512, filter_size=(3, 3), stride=1, pad=1, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
       network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2), stride=2)
       network = lasagne.layers.Conv2DLayer(network, num_filters=512, filter_size=(3, 3), stride=1, pad=1, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
       network = lasagne.layers.Conv2DLayer(network, num_filters=512, filter_size=(3, 3), stride=1, pad=1, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
       network = lasagne.layers.Conv2DLayer(network, num_filters=512, filter_size=(3, 3), stride=1, pad=1, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
       network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2), stride=2)
       return network

这里定义了一个包含13个卷积层和5个最大池化层的VGG网络，每个卷积层都使用了GlorotUniform初始化方法。

3. 加载预训练的权重文件：

   def load_weights():
       # Load the pre-trained weights
       url = 'https://s3.amazonaws.com/lasagne/recipes/pretrained/imagenet/vgg19_normalized.pkl'
       filename = 'vgg19_normalized.pkl'
       if not os.path.exists(filename):
           urllib.urlretrieve(url, filename)
       with open(filename, 'rb') as f:
           model = pickle.load(f)
       return model['param values']

这里使用了pickle库来加载预训练的权重文件，并返回其中的参数值。

4. 初始化参数：

   input_var = T.tensor4('inputs')
   target_var = T.ivector('targets')
   network = build_model(input_var)
   weights = load_weights()
   lasagne.layers.set_all_param_values(network, weights)

这里首先定义了输入和目标变量，然后调用了前面定义的build_model函数来构建VGG网络，接着调用load_weights函数加载预训练的权重文件，并使用lasagne.layers.set_all_param_values函数将参数值设置到网络中。

5. 编译模型：

   prediction = lasagne.layers.get_output(network)
   loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)
   loss = loss.mean()
   params = lasagne.layers.get_all_params(network, trainable=True)
   updates = lasagne.updates.momentum(loss, params, learning_rate=0.01, momentum=0.9)
   train_fn = theano.function([input_var, target_var], loss, updates=updates)

这里首先定义了预测值和损失函数，然后使用lasagne.layers.get_all_params函数获取所有可训练的参数，接着使用lasagne.updates.momentum函数定义更新规则，最后使用theano.function函数编译模型。

现在，我们就完成了VGG网络参数的初始化。