resnet50.py 文件源码

def build_model(self):

        import theano.tensor as T
        self.x = T.ftensor4('x')
        self.y = T.lvector('y')
        self.lr = T.scalar('lr')

        net = build_model_resnet50(input_shape=(None, 3, 224, 224))

        if self.verbose: print('Total number of layers:', len(lasagne.layers.get_all_layers(net['prob'])))

        self.output_layer = net['prob']

        from lasagne.layers import get_output
        self.output = lasagne.layers.get_output(self.output_layer, self.x, deterministic=False)
        self.cost = lasagne.objectives.categorical_crossentropy(self.output, self.y).mean()
        from lasagne.objectives import categorical_accuracy
        self.error = 1-categorical_accuracy(self.output, self.y, top_k=1).mean()
        self.error_top_5 = 1-categorical_accuracy(self.output, self.y, top_k=5).mean()