torch.nn.init.normal_(m.weight.data,1.0,0.02) torch.nn.init.constant_(m.bias.data,0.0) 这里的意思是选择module是conv或者是batchNorm2d的层进行初始化
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): classnam
pytorch中的model.apply(fn)会递归地将函数fn应用到父模块的每个子模块submodule,也包括model这个父模块自身。比如下面的网络例子中。net这个模块有两个子模块,分别为Linear(2,4)和Linear(4,8)。函数首先对Linear(2,4)和Linear(4,8)两个子模块调用init_weights函数,即print(m)打印Linear(2,4)和Linear(4,8)...
#@save def train_ch6(net, train_iter, test_iter, num_epochs, lr, device): """用GPU训练模型(在第六章定义)""" def init_weights(m): ##如果这一层是线性层或卷积层,对参数初始化 if type(m) == nn.Linear or type(m) == nn.Conv2d: nn.init.xavier_uniform_(m.weight) net.apply(i...
apply(self.init_weights) # 初始化环境模型中的参数 self.optimizer = torch.optim.Adam(self.parameters(), lr=learning_rate) def init_weights(self,m): ''' 初始化模型权重 ''' def truncated_normal_init(t, mean=0.0, std=0.01): torch.nn.init.normal_(t, mean=mean, std=std) while True:...
sampling_representation = encoder.apply(sampling_input, theano.tensor.ones(sampling_input.shape)) generateds = decoder.generate(sampling_input, sampling_representation) model =Model(generateds[1])# Initialize modelencoder.weights_init = decoder.weights_init = IsotropicGaussian(0.01) ...
self.apply(init_weights) 开发者ID:huminghao16,项目名称:SpanABSA,代码行数:23,代码来源:sentiment_modeling.py 示例5: create_predict_model ▲点赞 5▼ # 需要导入模块: from bert import modeling [as 别名]# 或者: from bert.modeling importBertModel[as 别名]defcreate_predict_model(bert_config, inpu...
# Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION, ...
super().__init__(config) self.model = Qwen2Model(config) self.vocab_size = config.vocab_size self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) raise "Use Qwen2ForCausalLM" # Initialize weights and apply final processing ...
Model loaded in 8.4s (load weights from disk: 0.2s, create model: 0.4s, apply weights to model: 4.4s, apply half(): 0.5s, load VAE: 0.7s, move model to device: 0.5s, load textual inversion embeddings: 1.7s). Running on local URL:http://127.0.0.1:7860 ...