How to reset weights of lasagne network?

I want to reset the weights of my convolutional neural network if a "nan" is detected.

Im not sure how to do it.

Im also confused if i should change the seed as well in this case.

        if np.isnan(trainingLoss):
            print "..Training Loss is NaN"
            self.reset_network()

        if np.isnan(validationLoss):
            print "..Validation Loss is NaN"
            self.reset_network()

How should i implement reset_network() ?

I'm not sure this is the intended way of resetting network weights, but here's how I did it. In the following code network is a reference to a CNN with 2 convolutional layers followed by max pooling layers. I believe it should work with other architectures as well.

The trick here is to update all trainable parameters of the network with initializer functions.

def reset_weights(network):
    params = lasagne.layers.get_all_params(network, trainable=True)
    for v in params:
        val = v.get_value()
        if(len(val.shape) < 2):
            v.set_value(lasagne.init.Constant(0.0)(val.shape))
        else:
            v.set_value(lasagne.init.GlorotUniform()(val.shape))

I hope it helps!