Densenet_MNIST.py 文件源码

def Dense_net(self, input_x):
        x = conv_layer(input_x, filter=2 * self.filters, kernel=[7,7], stride=2, layer_name='conv0')
        x = Max_Pooling(x, pool_size=[3,3], stride=2)



        for i in range(self.nb_blocks) :
            # 6 -> 12 -> 48
            x = self.dense_block(input_x=x, nb_layers=4, layer_name='dense_'+str(i))
            x = self.transition_layer(x, scope='trans_'+str(i))


        """
        x = self.dense_block(input_x=x, nb_layers=6, layer_name='dense_1')
        x = self.transition_layer(x, scope='trans_1')

        x = self.dense_block(input_x=x, nb_layers=12, layer_name='dense_2')
        x = self.transition_layer(x, scope='trans_2')

        x = self.dense_block(input_x=x, nb_layers=48, layer_name='dense_3')
        x = self.transition_layer(x, scope='trans_3')
        """

        x = self.dense_block(input_x=x, nb_layers=32, layer_name='dense_final')

        # 100 Layer
        x = Batch_Normalization(x, training=self.training, scope='linear_batch')
        x = Relu(x)
        x = Global_Average_Pooling(x)
        x = flatten(x)
        x = Linear(x)


        # x = tf.reshape(x, [-1, 10])
        return x