model.parameters()常用于定义优化器(optimizer)和计算梯度。 model.state_dict(): 这个方法返回一个字典,包含了模型的所有状态信息。字典中的键是参数名称,值是对应参数的张量(Tensor)。model.state_dict()的主要用途是保存和加载模型。通过调用torch.save()将model.state_dict()保存为文件后,可以使用torch.load...
2.2 计算模型参数数量 一旦定义了模型,就可以通过parameters()方法获取参数,并使用numpy计算总数量。以下是计算模型参数数量的示例代码: defcount_parameters(model):returnsum(p.numel()forpinmodel.parameters()ifp.requires_grad)num_params=count_parameters(model)print(f'Total number of trainable parameters:{num_...
table = PrettyTable([“Modules”, “Parameters”])total_params = 0 for name, parameter in model.named_parameters():if not parameter.requires_grad: continue params = parameter.numel()table.add_row([name, params])total_params+=params print(table)print(f”Total Trainable Params: {total_params}...
self).__init__()self.fc1=nn.Linear(10,5)self.fc2=nn.Linear(5,1)defforward(self,x):x=torch.relu(self.fc1(x))x=self.fc2(x)returnx# 创建模型实例model=SimpleNN()# 计算模型参数量defcount_parameters(model):returnsum(p.numel()forpinmodel.parameters()ifp.requires...
print(f”Total Trainable Params: {total_params}”) return total_params 我们拿RESNET18为例,以上函数的输出如下: +---+---+ | Modules | Parameters | +---+---+ | conv1.weight | 9408 | | bn1.weight | 64 | | bn1.bias | 64 | | layer1.0.conv1.weight | 36864...
print(f"{total_trainable_params:,} training parameters.") 学习参数 现在,我们将定义学习/训练参数,其中包括learning rate、epochs、optimizer和loss fuction。 #written and saved in train.py # learning parameters lr = 0.001 epochs = 100 # optimizer ...
#模型体系print(model)def count_parameters(model): return sum(p.numel() for p in model.parameters() if p.requires_grad) print(f'The model has {count_parameters(model):,} trainable parameters')#初始化预训练embeddingpretrained_embeddings = TEXT.vocab.vectorsmodel.embedding.weight.data.copy...
self.n_features = n_featuresself.n_components = n_componentsweights = torch.ones( n_components)means = torch.randn( n_components, n_features) *self.init_scalestdevs = torch.rand( n_components, n_features) *self.init_scale## Our trainable Para...
# Our trainable Parameters self.blend_weight=torch.nn.Parameter(weights)self.means=torch.nn.Parameter(means)self.stdevs=torch.nn.Parameter(stdevs)defforward(self,x):blend_weight=torch.distributions.Categorical(torch.nn.functional.relu(self.blend_weight))comp=torch.distributions.Independent(torch.distrib...
在下面的代码片段中，我们将分别使用Tensorflow和PyTorch trainable_variables和parameters方法来访问模型参数并绘制学习到的线性函数的图。绘制结果 [w_tf, b_tf] = tf_model.trainable_variables[w_torch, b_torch] = torch_model.parameters()with torch.no_grad():plt.figure(figsize = (12,5)) ax = plt...