1torch.Tensor ---> 单个Python数据,使用data.item(),data为Tensor变量且只能为包含单个数据 2torch.Tensor ---> Python list,使用data.tolist(),data为Tensor变量,返回shape相同的可嵌套的list 3torch.Tensor ---> numpy,使用data.numpy(),data为Tensor变量 4numpy ---> torch.Tensor,tensor = torch.from_numpy...
在上面的代码中,我们首先使用squeeze()函数将张量中的所有维度大小为1的维度移除,使其变为一维张量。然后,我们使用tolist()函数将一维张量转换为Python列表。最后,我们将结果打印出来,即得到了[5, 2, 6]。 二、余弦相似度 余弦相似度的取值范围为 [-1, 1],其中 1 表示完全相似,-1 表示完全不相似。 import...
# Torch Code: torch.Tensor((1,2,3,4)) #output: #tensor([1., 2., 3., 4.]) # PaddlePaddle Code: paddle.to_tensor((1,2,3,4)) # 全部为整数 #output: #Tensor(shape=[4], dtype=int64, place=Place(cpu), stop_gradient=True, # [1, 2, 3, 4]) paddle.to_tensor((1,2,3,...
equal(tensor1, tensor2) # bool 张量比较shape and value返回bool # torch.ge(input, other, out=None) # Tensor 大于 # torch.gt(input, other, out=None) # Tensor 与equal类似返回不同 # torch.kthvalue(input, k, dim=None, out=None) -> (Tensor, LongTensor) 取指定维度最小值 # torch.le...
bn_w=torch.ones_like(bn_rm)ifbn_bisNone:bn_b=torch.zeros_like(bn_rm)bn_var_rsqrt=torch.rsqrt(bn_rv+bn_eps)iftranspose:shape=[1,-1]+[1]*(len(conv_w.shape)-2)else:shape=[-1,1]+[1]*(len(conv_w.shape)-2)conv_w=conv_w*(bn_w*bn_var_rsqrt).reshape(shape)conv_b=(...
input_dim=X_train.shape[1] learning_rate=0.001 n_epochs=100 # Initialize the model, loss function, and optimizer model=SimpleNN(input_dim) criterion=nn.MSELoss() optimizer=optim.Adam(model.parameters(), lr=learning_rate) # Define custom compiler ...
import tensorflow as tf import numpy as np # 创建一些模拟数据 X = np.array([1.0, 2.0, 3.0, 4.0]) Y = np.array([2.0, 4.0, 6.0, 8.0]) # 定义线性回归模型 model = tf.keras.models.Sequential([ tf.keras.layers.Dense(1, input_shape=(1,)) ]) # 编译模型 model.compile(optimizer='...
], dtype=torch.int32, ).npu() out = torch_npu.npu_incre_flash_attention( query, key, value, actual_seq_lengths=kv_lengths, block_table=block_table, num_heads=num_heads, scale_value=1.0, input_layout="BSH", num_key_value_heads=num_heads, ...
)– a list, tuple, or torch.Size of integers defining the shape of the output tensor. fill_value –the number to fill the output tensor with. out (Tensor, optional)– the output tensor. dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None,...
append(hvp) return hvps # Get HVPs with 10 random vectors n_v = 10 vs : List[Optional[torch.Tensor]] = [] for p in model.parameters(): vs.append(torch.randn(n_v, *tuple(p.shape)).to(device)) hvps = hvp_parallel(list(model.parameters()), vs, grad_params) print(hvps[0][0...