_init_weights 方法用于初始化模型的权重,在模型创建时会自动调用该方法。将模型中的线性层和嵌入层的权重进行初始化,初始化方式是从一个均值为 0、方差为 self.config.intializer_range 的正态分布中采样得到,偏置则初始化为零。_set_gradient_checkpointing 方法用于设置是否启用梯度检查点技术。如果输入的模型是 ...
defweights_init_normal(m): classname = m.__class__.__name__ ifclassname.find("Conv") !=-1: torch.nn.init.normal_(m.weight.data,0.0,0.02) elifclassname.find("BatchNorm2d") !=-1: torch.nn.init.normal_(m.weight.data,1.0,0.02) torch.nn.init.constant_(m.bias.data,0.0) 这里的意...
classLlamaPreTrainedModel(PreTrainedModel):config_class=LlamaConfigbase_model_prefix="model"supports_gradient_checkpointing=True_no_split_modules=["LlamaDecoderLayer"]_skip_keys_device_placement="past_key_values"_supports_flash_attn_2=Truedef_init_weights(self,module):std=self.config.initializer_rangei...
model.apply(weights_init_normal)方法 应用把方法应用于每一个module,这里意思是进行初始化 def weights_init_normal(m): classname = m.__class__.__name__ if classname.find("Conv") != -1: torch.nn.init.normal_(m.weight.data, 0.0, 0.02) elif classname.find("BatchNorm2d") != -1: ...
_init_weights方法:用于初始化模型权重的方法。 在这个基类中,大多数属性都被定义为 None 或空字符串,这些属性在具体的预训练模型类中会被重写或填充。接下来我们将看到如何使用 PretrainedModel 类定义 llama 模型。 class LlamaPreTrainedModel(PreTrainedModel): ...
_init_weights方法:用于初始化模型权重的方法。 在这个基类中,大多数属性都被定义为 None 或空字符串,这些属性在具体的预训练模型类中会被重写或填充。接下来我们将看到如何使用 PretrainedModel 类定义 llama 模型。 代码语言:javascript 复制 classLlamaPreTrainedModel(PreTrainedModel):config_class=LlamaConfig ...
defreset_weights(model):forlayerinmodel.layers:ifisinstance(layer,tf.keras.Model):#if you're using a model as a layerreset_weights(layer)#apply function recursivelycontinue#where are the initializers?ifhasattr(layer,'cell'):init_container=layer.cellelse:init_container=layerforkey,initializerininit...
Just opening this issue to keep track of it. As reported on the forum, sometimes a warning gets printed about certain weights not being initialized while they are not part of the model. These always seem to be pooler weights. Example 1 B...
self.init_weights() if not args.lstm: self.rnn.set_bias(args.bias) def init_weights(self): val_range = (3.0/self.n_d)**0.5 for p in self.parameters(): if p.dim() > 1: # matrix p.data.uniform_(-val_range, val_range) print('222222',p.data) else: p.data.zero_() print...
# 需要导入模块: from keras.models import Model [as 别名]# 或者: from keras.models.Model importload_weights[as 别名]defload_DAG_model(weight_file):model =Noneifweight_file.endswith('.weights'): model = init_model(weight_file,25)elifweight_file.endswith('.h5')orweight_file.endswith('....