[GitHub] danache commented on issue #7758: How can I use UpSampling or add_n function in gluon?

[gi...@git.apache.org]

danache commented on issue #7758: How can I use UpSampling or add_n function in gluon?
URL: https://github.com/apache/incubator-mxnet/issues/7758#issuecomment-327390743
 
 
   Thanks for your reply !I follow your instruction and build the net as this:
   from mxnet import gluon
   ```
   class poolLayer(gluon.HybridBlock):
       def __init__(self, numOut,**kwargs):
           super(poolLayer, self).__init__(**kwargs)
           with self.name_scope():
               self.bn1 = gluon.nn.BatchNorm(axis=1, center=True, scale=True)
               self.relu1 = gluon.nn.Activation(activation='relu')
               self.pool1 = gluon.nn.MaxPool2D(pool_size=2)
               
               self.conv1 = gluon.nn.Conv2D(channels=numOut, kernel_size=3, strides=1, padding=1)
               self.bn2 = gluon.nn.BatchNorm(axis=1, center=True, scale=True)
               self.relu2 = gluon.nn.Activation(activation='relu')
               
               self.conv2 = gluon.nn.Conv2D(channels=numOut, kernel_size=3, strides=1, padding=1)
       def hybrid_forward(self, F, x):
           # F is a function space that depends on the type of x
           # If x's type is NDArray, then F will be mxnet.nd
           # If x's type is Symbol, then F will be mxnet.sym
           print('type(x): {}, F: {}'.format(
                   type(x).__name__, F.__name__))
           x = self.bn1(x)
           x = self.relu1(x)
           x = self.pool1(x)
           x = self.conv1(x)
           x = self.bn2(x)
           x = self.relu2(x)
           
           x = self.conv2(x)
           x = F.UpSampling(x,scale=2,sample_type = 'nearest')
           
           return x
   ```
   could you tell me if it is right? Thx a lot!
 
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