net=resnet34()#注意:模型内部传参数和不传参数,输出的结果是不一样的#计算网络参数total = sum([param.nelement()forparaminnet.parameters()])#精确地计算:1MB=1024KB=1048576字节print('Number of parameter: % .4fM'% (total / 1e6)) 输出: Number of parameter: 21.7977M 参数量方法二: summary的...
total = sum([param.nelement() for param in net.parameters()]) # 精确地计算:1MB=1024KB=1048576字节 print('Number of parameter: % .4fM' % (total / 1e6)) 1. 2. 3. 4. 5. 6. 7. 输出: Number of parameter: 21.7977M 1. 参数量方法二: summary的使用:来自于torchinfo第三方库 torchi...
导入ptflops macs,params=get_model_complexity_info(model1,(3,352,352),as_strings=True,print_per_layer_stat=False,verbose=True)print('{:<30}{:<8}'.format('Computational complexity: ',macs))print('{:<30}{:<8}'.format('Number of parameters: ',params)) 调用自己的模型。如果as_strings设...
model = FPN() num_params =sum(p.numel()forpinmodel.parameters())print("num of params: {:.2f}k".format(num_params/1000.0))# torch.numel()返回tensor的元素数目,即number of elements# Returns the total number of elements in the input tensor. 3. 打印模型 model = FPN() num_params =sum...
for name, param in model.state_dict().items(): print(name,param.requires_grad=True) 获取参数个数torch.numel() torch.numel(input) → int Returns the total number of elements in the input tensor. def get_parameter_number(net): total_num = sum(p.numel() for p in net.parameters()) ...
Input[0]batch_size= 2 # number of samples input at once input_dim = 3 hidden_dim = 100 output_dim = 4 # Initialize model mlp = MultilayerPerceptron(input_dim, hidden_dim, output_dim) print(mlp) Output[0] MultilayerPerceptron(
print("The model will be running on", device,"device")# Convert model parameters and buffers to CPU or Cudamodel.to(device)forepochinrange(num_epochs):# loop over the dataset multiple timesrunning_loss =0.0running_acc =0.0fori, (images, labels)inenumerate(train_loader,0):# get the ...
[13],dtype=np.float32)#2.Createmodelprint("Creating13-(10-10)-1DNNregressionmodel\n")net=Net()#allworkdoneabove#3.Trainmodelnet=net.train()#settrainingmodebat_size=10loss_func=T.nn.MSELoss()#meansquarederroroptimizer=T.optim.SGD(net.parameters(),lr=0.01)n_items=len(train_x)batches_...
parameters(), lr=lr) criterion = nn.CrossEntropyLoss() # 训练模型 for epoch in range(num_epochs): for i, (inputs, labels) in enumerate(train_loader): outputs = model(inputs) loss = criterion(outputs, labels) optimizer.zero_grad() loss.backward() optimizer.step() if (i+1) % 10 ...
importtorch.optimasopt learning_rate =0.001optimizer = opt.Adam(model.parameters(), lr=learning_rate) 提示 有关PyTorch 中可用优化器的详细信息,请参阅 PyTorch 文档中的算法。 创建训练和测试函数 定义网络并为其准备数据后,可以使用数据来训练和测试模型,方法是通过网络传递训练数据、计算损失、优化网络权重和...