CIFAR10.py 文件源码

def build_cnn(self, input_var=None):
        # Building the network
        layer_in = InputLayer(shape=(None, 3, 32, 32), input_var=input_var)

        # Conv1
        # [NOTE]: normal vs. truncated normal?
        # [NOTE]: conv in lasagne is not same as it in TensorFlow.
        layer = ConvLayer(layer_in, num_filters=64, filter_size=(3, 3), stride=(1, 1), nonlinearity=rectify,
                          pad='same', W=lasagne.init.HeNormal(), flip_filters=False)
        # Pool1
        layer = MaxPool2DLayer(layer, pool_size=(3, 3), stride=(2, 2))
        # Norm1
        layer = LocalResponseNormalization2DLayer(layer, alpha=0.001 / 9.0, k=1.0, beta=0.75)

        # Conv2
        layer = ConvLayer(layer, num_filters=64, filter_size=(5, 5), stride=(1, 1), nonlinearity=rectify,
                          pad='same', W=lasagne.init.HeNormal(), flip_filters=False)
        # Norm2
        # [NOTE]: n must be odd, but n in Chang's code is 4?
        layer = LocalResponseNormalization2DLayer(layer, alpha=0.001 / 9.0, k=1.0, beta=0.75)
        # Pool2
        layer = MaxPool2DLayer(layer, pool_size=(3, 3), stride=(2, 2))

        # Reshape
        layer = lasagne.layers.ReshapeLayer(layer, shape=([0], -1))

        # Dense3
        layer = DenseLayer(layer, num_units=384, W=lasagne.init.HeNormal(), b=lasagne.init.Constant(0.1))

        # Dense4
        layer = DenseLayer(layer, num_units=192, W=lasagne.init.Normal(std=0.04), b=lasagne.init.Constant(0.1))

        # Softmax
        layer = DenseLayer(layer, num_units=self.output_size,
                           W=lasagne.init.Normal(std=1. / 192.0), nonlinearity=softmax)

        return layer