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add G,H
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@@ -484,13 +484,23 @@ def resnet(units, num_stages, filter_list, num_classes, bottle_neck, **kwargs):
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if fc_type=='A':
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fc1 = flat
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else:
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#B
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fc1 = mx.sym.FullyConnected(data=flat, num_hidden=num_classes, name='pre_fc1')
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if fc_type=='C':
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fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1')
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elif fc_type=='D':
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fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1')
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fc1 = Act(data=fc1, act_type='relu', name='fc1_relu')
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if fc_type=='G' or fc_type=='H':
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fc1 = mx.symbol.Dropout(data=flat, p=0.2)
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fc1 = mx.sym.FullyConnected(data=fc1, num_hidden=num_classes, name='pre_fc1')
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if fc_type=='G':
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return fc1
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else:
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fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1')
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return fc1
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else:
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#B-D
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#B
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fc1 = mx.sym.FullyConnected(data=flat, num_hidden=num_classes, name='pre_fc1')
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if fc_type=='C':
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fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1')
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elif fc_type=='D':
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fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1')
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fc1 = Act(data=fc1, act_type='relu', name='fc1_relu')
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return fc1
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def get_symbol(num_classes, num_layers, **kwargs):
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