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
```
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