print(f"Computation device: {device}\n") # instantiate the model model = models.resnet50(pretrained=True, requires_grad=False).to(device) # total parameters and trainable parameters total_params = sum(p.numel() for p in model.parameters()) print(f"{total_params:,} total parameters.") ...
#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...
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}...
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_...
Trainable_params = 0 NonTrainable_params = 0 # 遍历model.parameters()返回的全局参数列表for param in model.parameters(): mulValue = np.prod(param.size()) # 使用numpy prod接口计算参数数组所有元素之积 Total_params += mulValue # 总参数量 ...
Non-trainable params: 0 开始进行网络训练，代码也较为简单 from keras.callbacks import TensorBoard #...
print(table) 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...
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') The model has 2,500,301 trainable parameters 1. 2. 3.
print(model) #可训练参数的数量 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') #初始化预训练的词嵌入 pretrained_embeddings = TEXT.vocab.vectors ...
table=PrettyTable(['Modules','Parameters'])total_params=0forname,parameterinmodel.named_parameters():ifnotparameter.requires_grad:continueparams=parameter.numel()table.add_row([name,params])total_params+=paramsprint(table)print(f'Total Trainable Params:{total_params}') ...