smartwell opened a new issue #15470: it maye be is a bug, how to explain URL: https://github.com/apache/incubator-mxnet/issues/15470 mxnet 1.5 ubuntu 16.04 ``` from mxnet import gluon from mxnet import nd from mxnet import autograd import mxnet.gluon.nn as nn class Generator(gluon.HybridBlock): def __init__(self, alloc_size=(5, 5), **kwargs): super(Generator, self).__init__(**kwargs) self.cnnblock = nn.HybridSequential() self.cnnblock.add( nn.Conv2D(32, kernel_size=3, strides=1, padding=1), nn.BatchNorm(), nn.LeakyReLU(0.2), ) anchors = self._generate_anchors(alloc_size) self._key = 'anchor_1' self.anchors = self.params.get_constant(self._key, anchors) def _generate_anchors(self, alloc_size): return nd.random.uniform(shape=alloc_size) def hybrid_forward(self, F, x, anchors): if autograd.is_training(): x = self.cnnblock(x) return x a = self.cnnblock(x) return a generator = Generator() generator.hybridize() generator.initialize() x = nd.random.uniform(shape=(1, 3, 512, 512)) with autograd.train_mode(): o = generator(x) print(o) ``` error： `mxnet.gluon.parameter.DeferredInitializationError: Parameter 'conv0_weight' has not been initialized yet because initialization was deferred. Actual initialization happens during the first forward pass` if you delete all things about anchor ,it can run successfully
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