cnn_cascade_lasagne.py 文件源码

def __build_loss_train__fn__(self):
        # create loss function
        prediction = layers.get_output(self.net)
        loss = objectives.categorical_crossentropy(prediction, self.__target_var__)
        loss = loss.mean() + 1e-4 * regularization.regularize_network_params(self.net, regularization.l2)

        val_acc = T.mean(T.eq(T.argmax(prediction, axis=1), self.__target_var__),dtype=theano.config.floatX)

        # create parameter update expressions
        params = layers.get_all_params(self.net, trainable=True)
        self.eta = theano.shared(sp.array(sp.float32(0.05), dtype=sp.float32))
        update_rule = updates.nesterov_momentum(loss, params, learning_rate=self.eta,
                                                    momentum=0.9)

        # compile training function that updates parameters and returns training loss
        self.__train_fn__ = theano.function([self.__input_var__,self.__target_var__], loss, updates=update_rule)
        self.__predict_fn__ = theano.function([self.__input_var__], layers.get_output(self.net,deterministic=True))
        self.__val_fn__ = theano.function([self.__input_var__,self.__target_var__], [loss,val_acc])