from mxnet.gluon import nn
from mxnet import nd
class MyDense(nn.HybridBlock):
def __init__(self, units, in_units, **kwargs):
super().__init__(**kwargs)
self.embedding = nn.Embedding(3, 5)
self.weight = self.params.get('weight', shape=(in_units, units))
self.bias = self.params.get('bias', shape=(units,))
def hybrid_forward(self, F, x, weight,bias):
#linear = np.dot(x, self.weight.data(ctx=x.ctx)) + self.bias.data(ctx=x.ctx)
# print(self.embedding)
linear = F.dot(x, weight) + bias
return F.relu(linear)
dense = MyDense(units=3,in_units=5)
dense.initialize()
print(dense(nd.random.uniform(shape=(2, 5))))
print('hybrid_forward success!')
from mxnet.gluon import nn
from mxnet import nd
class MyDense(nn.Block):
def __init__(self, units, in_units, **kwargs):
super().__init__(**kwargs)
self.embedding = nn.Embedding(3, 5)
self.weight = self.params.get('weight', shape=(in_units, units))
self.bias = self.params.get('bias', shape=(units,))
def forward(self, x, weight,bias):
# 这种注释方式可以
#linear = np.dot(x, self.weight.data(ctx=x.ctx)) + self.bias.data(ctx=x.ctx)
# print(self.embedding)
linear = nd.dot(x, weight) + bias
return nd.relu(linear)
dense = MyDense(units=3,in_units=5)
dense.initialize()
try:
print(dense(nd.random.uniform(shape=(2, 5))))
except Exception as e:
print('some error:',e)
print('forward fail!')
结果：
[[0. 0.14012612 0.0058622 ]
[0. 0.12333627 0.063691 ]]
<NDArray 2x3 @cpu(0)>
hybrid_forward success!
some error: forward() missing 2 required positional arguments: 'weight' and 'bias'
forward fail!
本文地址：https://blog.csdn.net/sinat_24395003/article/details/109644137
