还可以用help(model.forward)对运行该模型所需参数有更深入的了解。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 >>>help(model.forward)>>>Help on method forwardinmodule pytorch_pretrained_bert.modeling:forward(input_ids,token_type_ids=
x=torch.rand(20,1)*10# xdata(tensor),shape=(20,1)y=2*x+(5+torch.randn(20,1))# ydata(tensor),shape=(20,1)# Build Linear Regression Parameters # Initialize w and b,where w is initialized to a normal distribution and b is initialized to0# Automatic differentiation is required,soset...
lr=learning_rate) for epoch in range(epochs): optimizer_moe.zero_grad() outputs_moe = moe_model(x_train_moe) loss_moe = criterion(outputs_moe, y_train_moe) loss_moe.backward() optimizer_moe.step()
py复制 # Convert Input and Output data to Tensors and create a TensorDatasetinput = torch.Tensor(input.to_numpy())# Create tensor of type torch.float32print('\nInput format: ', input.shape, input.dtype)# Input format: torch.Size([150, 4]) torch.float32output = torch.tensor(output.to...
@torch.inference_mode()def p_sample(self, x: torch.Tensor, timestamp: int) -> torch.Tensor:b, *_, device = *x.shape, x.devicebatched_timestamps = torch.full((b,), timestamp, device=device, dtype=torch.long) preds = self.model(x, batch...
x = x.view(-1, int(x.nelement() / x.shape[0])) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x model = LeNet().to(device=device) model = LeNet() parameters_to_prune = ( (model.conv1, 'weight'), ...
│ ├── saved_model.pb │ └── variables │ ├── variables.data-00000-of-00001 │ └── variables.index └── config.pbtxt # triton 配置。 完成PyTorch单机训练,并将PyTorch模型转换成Torchscript,具体操作,请参见使用Arena提交PyTorch单机训练作业。
eval() a = torch.randn([1, 3, 224, 224]) o1 = model(a) o2 = model_int8(a) diff = torch.allclose(o1, o2, 1e-4) print(diff) print(o1.shape, o2.shape) print(o1, o2) # get_output_from_logits(o1) # get_output_from_logits(o2) train_loader, test_loader = prepare_...
# Define model parametersinput_size = list(input.shape)[1]# = 4. The input depends on how many features we initially feed the model. In our case, there are 4 features for every predict valuelearning_rate =0.01output_size = len(labels)# The output is prediction results for three types ...
accuracy_expert1 = evaluate(expert1, x_test, y_test) accuracy_expert2 = evaluate(expert2, x_test, y_test) accuracy_expert3 = evaluate(expert3, x_test, y_test) accuracy_moe = evaluate(moe_model, x_test, y_test) print("Expert 1 Accuracy:", accuracy_expert1) print("Expert 2 Accura...