The output tensor is 1-D of size steps. Parameters start (float)– the starting value for the set of points end (float)– the ending value for the set of points steps (int)– number of points to sample between
from torch.utils.data import TensorDataset # 创建特征和标签张量features = torch.randn(1000, 10) # 1000个样本,每个样本10个特征labels = torch.randint(0, 5, (1000,)) # 5分类问题 # 创建TensorDatasettensor_dataset = TensorDataset(fe...
from torch.nn import Sequential, Tanh from tensordict.nn import TensorDictModule from torchrl.modules import MultiAgentMLP, ProbabilisticActor, TanhNormal from tensordict.nn.distributions import NormalParamExtractor share_parameters_policy = True # 定义策略网络 policy_net = Sequential( MultiAgentMLP( ...
get_attrretrieves a parameter from the module hierarchy.nameis similarly the name the result of the fetch is assigned to.targetis the fully-qualified name of the parameter’s position in the module hierarchy.argsandkwargsare don’t-care call_functionapplies a free function to some values.name...
with torch.no_grad(): advantage_module(tensordict_data) data_view = tensordict_data.reshape(-1) # [1000] replay_buffer.extend(data_view.cpu()) for _ in range(frames_per_batch // sub_batch_size): subdata = replay_buffer.sample(sub_batch_size) # [64] loss_vals = loss_module(sub...
torch.from_numpy(ndarray) → Tensor torch.zeros(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor torch.zeros_like(input, dtype=None, layout=None, device=None, requires_grad=False) → Tensor ...
fromtorch.utils.dataimportWeightedRandomSampler importtorch.nn.functionalasF # 假设我们有一个不平衡的数据集 imbalanced_labels=torch.tensor([0, 0, 0, 0, 1, 1, 2]) class_sample_count=torch.tensor([(imbalanced_labels==t).sum() fortintorch.unique(imbalanced_labels, sorted=True)]) ...
import torchfromtorchvision import transformsdata_train = torch.utils.data.DataLoader(MNIST('~/mnist_data', train=True, download=True,transform = transforms.Compose([transforms.ToTensor()])),batch_size=64,shuffle=True)forbatch_idx, samplesinenumerate(data_train):print(batch_idx, samples) ...
# Sample module classM(torch.nn.Module): defforward(self, x, y): returntorch.add(x, y) deftransform(m: torch.nn.Module, tracer_class :type= fx.Tracer) -> torch.nn.Module: graph : fx.Graph = tracer_class().trace(m) # 对于graph中的node,FX会以顺序的形式来表示这个网络 ...
2.6 从numpy创建Tensor# Torch code: x = torch.from_numpy(x).float() # PaddlePaddle code x = paddle.to_tensor(x).astype(np.float32) In [7] import paddle x=paddle.to_tensor([1,2,3,4,5,6,7,8,9,10,11,12]) sample_lst=[0,5,7,11] x[sample_lst] Tensor(shape=[4], dtype...