https://www.cnblogs.com/hellcat/p/9047618.html
『MXNet』第四弹_Gluon自定义层
一、不含参数层
通过继承Block自定义了一个将输入减掉均值的层:CenteredLayer类,并将层的计算放在forward
函数里,
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from mxnet import nd, gluon from mxnet.gluon import nn class CenteredLayer(nn.Block): def __init__( self , * * kwargs): super (CenteredLayer, self ).__init__( * * kwargs) def forward( self , x): return x - x.mean() # 直接使用这个层 layer = CenteredLayer() # layer(nd.array([1, 2, 3, 4, 5])) # 构建更复杂模型 net = nn.Sequential() net.add(nn.Dense( 128 )) net.add(nn.Dense( 10 )) net.add(CenteredLayer()) # 初始化、运行…… net.initialize() y = net(nd.random.uniform(shape = ( 4 , 8 ))) |
二、含参数层
注意,本节实现的自定义层不能自动推断输入尺寸,需要手动指定
见上节『MXNet』第三弹_Gluon模型参数在自定义层的时候我们常使用Block自带的ParameterDict类添加成员变量params,如下,
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from mxnet import gluon from mxnet.gluon import nn class MyDense(nn.Block): def __init__( self , units, in_units, * * kwargs): super (MyDense, self ).__init__( * * kwargs) self .weight = self .params.get( 'weight' , shape = (in_units, units)) self .bias = self .params.get( 'bias' , shape = (units,)) def forward( self , x): linear = nd.dot(x, self .weight.data()) + self .bias.data() return nd.relu(linear) # 实际运行 dense = MyDense( 5 , in_units = 10 ) |
如果不想使用ParameterDict类则需要一下操作
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# self.weight = self.params.get('weight', shape=(in_units, units)) self .weight = gluon.Parameter( 'weight' , shape = (in_units, units)) self .params.update({ 'weight' : self .weight}) |
否则在net.initialize()初始化时是初始化不到ParameterDict外变量的。
有关这一点详见下面:
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def __init__( self , conv_arch, dropout_keep_prob, * * kwargs): super (SSD, self ).__init__( * * kwargs) self .vgg_conv = nn.Sequential() self .vgg_conv.add(repeat( * conv_arch[ 0 ], pool = False )) [ self .vgg_conv.add(repeat( * conv_arch[i])) for i in range ( 1 , len (conv_arch))] # 迭代器对象只能进行单次迭代,所以将之转化为tuple,否则识别参数处迭代后forward再次迭代直接跳出循环 # self.vgg_conv = tuple([repeat(*conv_arch[i]) # for i in range(len(conv_arch))]) # 只能识别实例属性直接为mx层函数或者mx序列对象的参数,如果使用其他容器,需要将参数收集进参数字典 # _ = [self.params.update(block.collect_params()) for block in self.vgg_conv] def forward( self , x, feat_layers): end_points = { 'block0' : x} for (index, block) in enumerate ( self .vgg_conv): end_points.update({ 'block{:d}' . format (index + 1 ): block(end_points[ 'block{:d}' . format (index)])}) return end_points |
属性对象是mxnet的对象时才能默认识别层中的参数,否则需要显式收集进self.params中。
测试代码:
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if __name__ = = '__main__' : ssd = SSD(conv_arch = (( 2 , 64 ), ( 2 , 128 ), ( 3 , 256 ), ( 3 , 512 ), ( 3 , 512 )), dropout_keep_prob = 0.5 ) ssd.initialize() X = mx.ndarray.random.uniform(shape = ( 1 , 1 , 304 , 304 )) import pprint as pp pp.pprint([x[ 1 ].shape for x in ssd(X).items()]) |
自行验证即可。