sgd_using_inputs.py 文件源码

def __init__(self, classifier, criterion, learning_rate, trainset, clip_threshold=0):
        self.eta = learning_rate
        self.is_weighted = trainset.is_weighted
        self.trainset = trainset

        if clip_threshold > 0:
            gparams = [T.clip(T.grad(criterion.cost, param), -clip_threshold, clip_threshold) for param in classifier.params]
        else:
            gparams = [T.grad(criterion.cost, param) for param in classifier.params]

        lr = T.fscalar()

        updates = [
            (param, param - lr * gparam)
            for param, gparam in zip(classifier.params, gparams)
        ]

        x = classifier.input
        y = criterion.y

        if self.is_weighted: 
            w = criterion.w
            self.step_func = theano.function(
                inputs=[x, y, w, lr],
                outputs=[criterion.cost] + gparams,
                updates=updates,
            )
        else:
            self.step_func = theano.function(
                inputs=[x, y, lr],
                outputs=[criterion.cost] + gparams,
                updates=updates,
            )