fake_out = self.netD(fake_data, label) zeros = ZerosLike()(fake_out) fake_loss = self.loss_fn(fake_out, zeros) # real loss real_out = self.netD(real_data, label) ones = OnesLike()(real_out) real_loss = self.loss_fn(real_out, ones) # d loss loss_D = real_loss + fake_...
(real_data) label_real = ops.OnesLike()(out_real) loss_real = self.loss_fn(out_real, label_real) fake_data = self.netG(latent_code) fake_data = ops.stop_gradient(fake_data) out_fake = self.netD(fake_data) label_fake = ops.ZerosLike()(out_fake) loss_fake = self.loss_fn(...
4 初始值是NumPy.array，则生成的Tensor数据类型与之对应。继承另一个张量的属性，形成新的张量from mindspore import opsoneslike = ops.OnesLike()x = Tensor(np.array([[0, 1], [2, 1]]).astype(np.int32))output = oneslike(x)print(output)5 输出指定大小的恒定值张量shape是张量的尺寸元组，确定...
frommindsporeimportops x_ones=ops.ones_like(x_data) print(f"Ones Tensor: \n {x_ones} \n") x_zeros=ops.zeros_like(x_data) print(f"Zeros Tensor: \n {x_zeros} \n") # 张量的属性 # 张量的属性包括形状、数据类型、转置张量、单个元素大小、占用字节数量、维数、元素个数和每一维步长。 #...
- OnesLike - Select - StridedSlice - ScatterNd -…… (2) math:数学计算相关的算子 - AddN - Cos - Sub - Sin - Mul - LogicalAnd - MatMul - LogicalNot - RealDiv - Less - ReduceMean - Greater ….. (3) nn:网络类算子 - Conv2d ...
mindspore.mint.ones_like mindspore.mint.gather mindspore.mint.logical_and mindspore.mint.all mindspore.mint.zeros mindspore.mint.permute mindspore.mint.logical_not mindspore.mint.mean mindspore.mint.zeros_like mindspore.mint.repeat_interleave mindspore.mint.logical_or mindspore.mint.prod mindspore.mint.ara...
651在MindSpore中,运行图片中的这段代码可以得到以下哪一项结果From mindspore import opsOnespke = ops.OnesLikeX=Tensor(np.array(123.[123.[0,1].123.[2.1]]).astype(np.int32))Oneput=onespke(x)A. 创建一个2*2的张量output,其中每一个元素值都为1 B. 创建一个2*2的张量output,其中元素的值为...
ones_like(loss)*F.cast(scaling_sens, F.dtype(loss)) record_grad = self.grad(self.network, weights)(record_datas[i], record_labels[i], scaling_sens_filled) square_sum = self._zero for grad in record_grad: square_sum = self._add(square_sum, self._reduce_sum(self._...
MindSpore implemented automatic differentiation based on ST. On the one hand, it supports automatic differentiation of automatic control flow, so it is quite convenient to build models like PyTorch. On the other hand, MindSpore can perform static compilation optimization on neural networks to achieve ...
B、通过MindSpore自身的技术创新及MindSpore与Ascend处理器协同优化,实现了运行态的高效,大大提高了计算性能。 C、原生适应每个场景包括端,边缘和云,并能够按需协同,部署态灵活。 D、以上皆是 免费查看参考答案及解析 题目: 昇思MindSpore社区是最活跃的AI开源社区 ...