L2 regularization in caffe, conversion from lasagne

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I have a lasgane code. I want to create the same network using caffe. I could conver the network.But i need help with the hyperparameters in lasagne. The hyperparameters look like:

lr = 1e-2
weight_decay = 1e-5

prediction = lasagne.layers.get_output(net['out'])
loss = T.mean(lasagne.objectives.squared_error(prediction, target_var))

weightsl2 = lasagne.regularization.regularize_network_params(net['out'], lasagne.regularization.l2)
loss += weight_decay * weightsl2

How do i perform the L2 regularization part in caffe? Relevant parts from my solver.prototxt is as below:

base_lr: 0.01
lr_policy: "fixed"
weight_decay: 0.00001
regularization_type: "L2"
stepsize: 300
gamma: 0.1  
max_iter: 2000
momentum: 0.9

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