def __init__(self, num_features, num_dims, **kwargs):
super(BatchNorm, self).__init__(**kwargs)
if num_dims == 2:
shape = (1, num_features)
else:
shape = (1, num_features, 1, 1)
# 参与求梯度和迭代的拉伸和偏移参数，分别初始化成0和1
self.gamma = self.params.get('gamma', shape=shape, init=init.One())
self.bate = self.params.get('beta', shape=shape, init=init.One())
# 不参与求梯度和迭代的标量
self.moving_mean = nd.zeros(shape)
self.moving_var = nd.zeros(shape)
def __init__(self,num_feature, num_dims, **kwargs):
super(BtachNorm,self).__init__(**kwargs)
shape = (1,num_feature) if num_dims == 2 else (1, num_feature, 1, 1)
self.beta = self.params.get('beta', shape=shape, init = init.Zero())
self.gamma = self.params.get('gamma', shape = shape, init = init.One())
self.moving_mean = nd.zeros(shape)
self.moving_var = nd.ones(shape)
def __init__(self, num_features, num_dims, **kwargs):
super(BatchNorm, self).__init__(**kwargs)
if num_dims == 2:
shape = (1, num_features)
else:
shape = (1, num_features, 1, 1)
self.gamma = self.params.get('gamma', shape=shape, init=init.One())
self.beta = self.params.get('beta', shape=shape, init=init.Zero())
# 不参与求梯度和迭代的变量，在CPU上初始化为0
self.moving_mean = nd.zeros(shape)
self.moving_var = nd.zeros(shape)
def __init__(self, num_fearures, num_dims, **kwargs):
super().__init__(**kwargs)
if num_dims == 2:
shape = (1, num_fearures) # 全连接层
else:
shape = (1, num_fearures, 1, 1) # 二维卷基层
# 参与求梯度和迭代的 拉伸和偏移,分别初始化为0或１
self.gamma = self.params.get('gamma', shape=shape, init=init.One())
self.beta = self.params.get('beta',
grad_req='null',
shape=shape,
init=init.Zero())
# 不参与求梯度和迭代的变量,在内存上全吃书啊为0,先在内存上初始化之后搬到显存上
self.moving_mean = nd.zeros(shape)
self.moving_var = nd.zeros(shape)
b = net.hidden.bias
print('hidden layer name: ', net.hidden.name, '\nweight: ', w, '\nbias: ', b)
print('weight:', w.data(), '\nweight grad:', w.grad(), '\nbias:', b.data(),'\nbias grad:', b.grad())
params = net.collect_params()
print(params)
print(params['mlp0_dense0_bias'].data())
print(params.get('dense0_bias').data())
params = net.collect_params()
params.initialize(init=init.Normal(sigma=0.02), force_reinit=True)
print('hidden weight: ', net.hidden.weight.data(), '\nhidden bias: ',
net.hidden.bias.data(), '\noutput weight: ', net.output.weight.data(),
'\noutput bias: ',net.output.bias.data())
net.hidden.bias.initialize(init=init.One(), force_reinit=True)
class MyInit(init.Initializer):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self._verbosity = True
def _init_weight(self, name, arr):
nd.random.uniform(low=10, high=20, out=arr)
net = MLP()
net.initialize(MyInit())
net(x)
print(net.hidden.weight.data())
print(net.hidden.bias.data())
b.grad()
#collect_params访问Block所有的参数，返回一个名字到对应Parameter的dict
params = net.collect_params()
params['sequential0_dense0_weight'].data()
params['sequential0_dense0_bias'].data()
params.get(dense1.weight).data()
params.get(dense1.bias).data()
'''---------------------------------------------------'''
#定制初始化方法
from mxnet import init
#（weight正态分布）
params.initialize(init=init.Normal(sigma=0.02), force_reinit=True)
#or（会将weight初始化为1）
params.initialize(init=init.One(), force_reinit=True)
#自定义初始化
class MyInit(init.Initializer):
def __init__(self):
super(MyInit,self).__init__()
self._verbose = True
def _init_weight(self, _, arr):
#初始化权重，使用out=arr后不需指定形状
nd.random_uniform(low=5, high=10, out=arr)
def _init_bais(self, _, arr):
arr[:] = 2
#未初始化bias是gluon的bug
params.initialize(init=MyInit(), force_reinit=True)
# print('net name %s weight %s bias %s' %(net[0].name, w, b))
# print('weight: ', w.data())
# print('weight gradient: ', w.grad())
# print('bias: ', b.data())
# print('bias gradient: ', b.grad())
params = net.collect_params()
# for param in params:
# print('param: ', param)
# Different Initialization
params.initialize(init=init.Normal(sigma=0.02), force_reinit=True)
# print(net[0].weight.data(), net[0].bias.data())
params.initialize(init=init.One(), force_reinit=True)
# print(net[0].weight.data(), net[0].bias.data())
net = build_net()
params = net.collect_params()
# print(params)
net.initialize()
params2 = net.collect_params()
# print(params2)
net(x)
params3 = net.collect_params()
# print(params3)
# Share Parameters