老师你好，想请问下 strategy = tf.distribute.MirroredStrategy () 支持 Model 子类化创建自定义模型使用多 gpu 分布式训练么，我自定义了一个 Resnet18 模型进行 cifar100 的多分布训练，但训练效果很差，测试集 acc 才 1%左右，好像参数无法更新，而在改变 Sequential 按层顺序创建模型，其他不变，效果就提升很多，
构建模型的代码如下，麻烦看下有什么问题：
with strategy.scope ():
# 定义一个 3x3 卷积
def regularized_padded_conv (*args,**kwargs):
return layers.Conv2D (*args,**kwargs,padding='same',kernel_regularizer=regularizers.l2 (5e-5),
use_bias=False,kernel_initializer='glorot_normal')
# 定义 Basic Block 模块。对应 Resnet18 和 Resnet34
class BasecBlock (layers.Layer):
expansion=1
def __init__(self,in_channels,out_channels,stride=1):
super (BasecBlock,self).__init__()
#1
self.conv1=regularized_padded_conv (out_channels,kernel_size=3,strides=stride)
self.bn1=layers.BatchNormalization ()
#2
self.conv2=regularized_padded_conv (out_channels,kernel_size=3,strides=1)
self.bn2=layers.BatchNormalization ()
#3
if stride!=1 or in_channels!=self.expansion * out_channels:
self.shortcut= Sequential ([regularized_padded_conv (self.expansion * out_channels,kernel_size=3,strides=stride),
layers.BatchNormalization ()])
else :
self.shortcut= lambda x ,_ : x
@tf.function
def call (self,inputs,training=False):
x=self.conv1 (inputs)
x=self.bn1 (x,training=training)
x=tf.nn.relu (x)
x=self.conv2 (x)
x=self.bn2 (x,training=training)
x_short=self.shortcut (inputs,training)
x=x+x_short
out=tf.nn.relu (x)
return out
# 自定义模型，ResBlock 模块。继承 keras.Model 或者 keras.Layer 都可以
class ResNet (tf.keras.Model):
# 第 1 个参数 blocks：对应 2 个自定义模块 BasicBlock 和 Bottleneck, 其中 BasicBlock 对应 res18 和 res34，Bottleneck 对应 res50,res101 和 res152，
# 第 2 个参数 layer_dims：[2, 2, 2, 2] 4 个 Res Block，每个包含 2 个 Basic Block
# 第 3 个参数 num_classes：我们的全连接输出，取决于输出有多少类
def __init__(self,blocks,layer_dims,initial_filters=64,num_classes=100):
super (ResNet,self).__init__()
self.in_channels=initial_filters
#
self.stem=Sequential ([regularized_padded_conv (initial_filters,kernel_size=3,strides=1),
layers.BatchNormalization ()])
#
self.layer1=self.build_resblock (blocks,initial_filters,layer_dims [0],stride=1)
self.layer2=self.build_resblock (blocks,initial_filters*2,layer_dims [1],stride=2)
self.layer3=self.build_resblock (blocks,initial_filters*4,layer_dims [2],stride=2)
self.layer4=self.build_resblock (blocks,initial_filters*8,layer_dims [3],stride=2)
self.final_bn=layers.BatchNormalization ()
self.avg_pool=layers.GlobalAveragePooling2D ()
self.dense=layers.Dense (num_classes)
def build_resblock (self,blocks,out_channels,num_blocks,stride):
strides=[stride]+[1]*(num_blocks-1)
res_block=Sequential ()
for stride in strides:
res_block.add (blocks (self.in_channels,out_channels,stride))
self.in_channels=out_channels
return res_block
@tf.function
def call (self,inputs,training):
x=self.stem (inputs,training)
x=tf.nn.relu (x)
x=self.layer1 (x,training=training)
x=self.layer2 (x,training=training)
x=self.layer3 (x,training=training)
x=self.layer4 (x,training=training)
x=self.final_bn (x,training=training)
x=tf.nn.relu (x)
x=self.avg_pool (x)
out=self.dense (x)
return out
def resnet18 ():
return ResNet (BasecBlock,[2,2,2,2])
model=resnet18 ()
model.build (input_shape=(None,32,32,3))