一、torch.argmax() (1)torch.argmax(input, dim=None, keepdim=False)返回指定维度最大值的序号; (2)dim给定的定义是:the demention to reduce.也就是把dim这个维度的,变成这个维度的最大值的index。 二、栗子 # -*- coding: utf-8 -*- "...
b = torch.argmax(z, dim=1)#.view(B,1)logprob = cat.log_prob(b).view(B,1)# czs = []# for j in range(1):# z = sample_relax_z(logits)# surr_input = torch.cat([z, x, logits.detach()], dim=1)# cz = surrogate.net(surr_input)# czs.append(cz)# czs = torch.stack(...
argmax(dim=-1, keepdim=True), 1.0) actions = (actions_hard - actions).detach() + actions actions = clamp_grad(actions, -0.5, 0.5) else: actions, gumbel_noise = cat_distr.rsample(gumbel_noise=gumbel_noise) else: actions = torch.zeros_like(cat_distr.probs) actions.scatter_(-1, ...
dim=-1) def gumbel_softmax(logits, temperature, hard=False): y_soft = gumbel_softmax_sample(logits, temperature) if hard: # 通过one-hot编码进行离散化 shape = y_soft.size() _, max_idx = y_soft.max(dim=-1, keepdim=True) y_hard = torch.zeros_like(y_soft).scatter_(-1, max_...
acc_sum += (net(X).argmax(dim=1) == y).float().sum().item() n += y.shape[0]returnacc_sum / n 开发者ID:wdxtub,项目名称:deep-learning-note,代码行数:19,代码来源:utils.py 示例2: train_cnn ▲点赞 6▼ # 需要导入模块: import torch [as 别名]# 或者: from torch importargmax...
argmax函数:torch.argmax(input, dim=None, keepdim=False)返回指定维度最大值的序号,dim给定的定义是:the demention to reduce.也就是把dim这个维度的,变成这个维度的最大值的index。 例如tensor(2, 3, 4) dim=0,将第一维度去掉,这样结果为tensor(3, 4)。
ifhard:# Straight through.index=y_soft.max(dim,keepdim=True)[1]y_hard=torch.zeros_like(logits...
n - 1 - torch.tensor(x).flip(dims=(1,)).argmax(dim=1) Output: tensor([1, 0]) x = [[1, 4, 4, 3], [5, 5, 2, 5]] np.argmax(np.array(x), axis=1) Output: array([1, 0]) VitalyFedyunin added enhancement triaged good first issue labels May 7, 2019 Contributor...
例如,代码是input = torch.randn(3, 10)result = torch.argmax(input, dim=0, keepdim=True)input 是tensor([[ 1.5742, 0.8183, -2.3005, -1.1650, -0.2451], [ 1.0553, 0.6021, -0.4938, -1.5379, -1.2054], [-0.1728, 0.8372, -1.9181, -0.9110, 0.2422]])并且result是tensor([[ 0, 2, 1, ...
keepdim=False, dtype='int64', name=None)out1 = paddle.argmax(x)print(out1)#2out2 = paddle.argmax(x, axis=0)print(out2)# [2,2,0,1]out3 = paddle.argmax(x, axis=-1)print(out3)# [2,3,1]out4 = paddle.argmax(x, axis=0, keepdim=True)print(out4)#[[2, 2, 0, 1]...