print(rand)# 随机初始化的值在 [0,1) 之间# tensor([[0.5047, 0.8442, 0.1771],# [0.3896, 0.5468, 0.9686]])# torch.rand_like()# ===randn=torch.randn(2,3)# 返回一个张量,包含了从标准正态分布(均值为0,方差为 1,即高斯白噪声)中抽取一组随机数,形状由可变参数sizes定义print(randn)# tensor...
Intorch, instead of the number of units in a layer, you specify input and output dimensionalities of the “data” that run through it. Thus,nn_linear(128, 10)has 128 input connections and outputs 10 values – one for every class. In some cases, such as this one, specifying dimensions ...
importtorchimporttimea=torch.randn(1000,1000).cuda()b=torch.randn(1000,1000).cuda()ops=torch.mm# warm upforiinrange(100):ops(a,b)# record timestart_time=time.time()foriinrange(100000):ops(a,b)end_time=time.time()elapsed_time=end_time-start_timeprint("torch.mm cost time is :",...
torch.linspace torch.linspace(start, end, steps) returns a one-dimensional tensor of equally spaced points between [start, end]。steps默认值是100。 torch.rand torch.rand(size) returns a tensor filled with random numbers from a uniform distribution on the interval [0, 1). torch.randn torch.r...
def jaccard_loss(probas, labels,ignore=None, smooth = 100, bk_class = None): """ Something wrong with this loss Multi-class Lovasz-Softmax loss probas: [B, C, H, W] Variable, class probabilities at each prediction (between 0 and 1). Interpreted as binary (sigmoid) output with outp...
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The shapes ofmeanandstddon’t need to match, but the total number of elements in each tensor need to be the same. Note When the shapes do not match, the shape ofmeanis used as the shape for the returned output tensor Parameters
torch.rand()、torch.randint()、torch.randn() 的区别 torch.rand():均匀分布 uniform distribution on the interval[0, 1) torch.randint():均匀分布,high需要指定,且元素为整数。 generated uniformly betweenlow(默认为0) andhigh torch.randn():均值为0方差为1的标准正态分布(高斯分布) ...
Default: 1 dilation –the spacing between kernel elements. Can be a single number or a tuple (dH, dW). Default: 1 Examples: >>> # With square kernels and equal stride >>> inputs = torch.randn(1, 4, 5, 5) >>> weights = torch.randn(4, 8, 3, 3) >>> F.conv_transpose2d(...
a = torch.rand(4, 4) * 5 # rate parameter between 0 and 5torch.poisson(a)输出如下：tensor([[2., 1., 0., 8.], [2., 3., 3., 3.], [0., 0., 1., 6.], [0., 5., 3., 3.]])torch.normal ()正态分布，又称高斯分布，是独立随机变量的连续分布函数。该分布有...