关于#深度学习#的问题：如何将两个数据集经过CNN的VGG模型处理后的softmax分类器的概率加在一起呢

Question

现在有两个数据集，一个是原始的，一个是经过处理的，如何将两个数据集经过CNN的VGG模型处理后的softmax分类器的概率加在一起呢？有具体的代码过程吗？

```python
class ConvPool(fluid.dygraph.Layer):
    '''卷积+池化'''
    def __init__(self,
                 num_channels,
                 num_filters,
                 filter_size,
                 pool_size,
                 pool_stride,
                 groups,
                 conv_stride=1,
                 conv_padding=1,
                 act=None,
                 pool_type='max'
                 ):
        super(ConvPool, self).__init__()  

        self._conv2d_list = []

        for i in range(groups):
            conv2d = self.add_sublayer(   #返回一个由所有子层组成的列表。
                'bb_%d' % i,
                fluid.dygraph.Conv2D(
                num_channels=num_channels, #通道数
                num_filters=num_filters,   #卷积核个数
                filter_size=filter_size,   #卷积核大小
                stride=conv_stride,        #步长
                padding=conv_padding,      #padding大小，默认为0
                act=act)
            )
            num_channels = num_filters
            self._conv2d_list.append(conv2d)   

        self._pool2d = fluid.dygraph.Pool2D(
            pool_size=pool_size,           #池化核大小
            pool_type=pool_type,           #池化类型，默认是最大池化
            pool_stride=pool_stride        #池化步长
            )

    def forward(self, inputs):
        x = inputs
        for conv in self._conv2d_list:
            x = conv(x)
        x = self._pool2d(x)
        return x

class VGGNet(fluid.dygraph.Layer):
'''
VGG网络
'''
def init(self):
super(VGGNet, self).init()
self.convpool01 = ConvPool(
3,64,3,2,2,2,act="relu")
self.convpool02 = ConvPool(
64,128,3,2,2,2,act="relu")
self.convpool03 = ConvPool(
128,256,3,2,2,3,act="relu")
self.convpool04 = ConvPool(
256,512,3,2,2,3,act="relu")
self.convpool05 = ConvPool(
512,512,3,2,2,3,act="relu")
self.pool_5_shape = 51277
self.fc01 = fluid.dygraph.Linear(self.pool_5_shape,4096,act="relu")
self.fc02 = fluid.dygraph.Linear(4096,4096,act="relu")
self.fc03 = fluid.dygraph.Linear(4096,train_parameters['class_dim'],act="softmax")

def forward(self, inputs, label=None):
    """前向计算"""
    out = self.convpool01(inputs)
    out = self.convpool02(out)
    out = self.convpool03(out)
    out = self.convpool04(out)
    out = self.convpool05(out)

    out = fluid.layers.reshape(out,shape=[-1,512*7*7])
    out = self.fc01(out)
    out = self.fc02(out)
    out = self.fc03(out)

    if label is not None:
        acc = fluid.layers.accuracy(input=out, label=label)
        return out, acc
    else:
        return out

```

Answer 1

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