import torch def count_parameters(model): """Count the number of parameters in a model.""" return sum([p.numel() for p in model.parameters()]) conv = torch.nn.Conv1d(8,32,1) print(count_parameters(conv)) # 288 linear = torch.nn.Linear(8,32) print(count_parameters(linear)) # ...
or 2) a single argument denoting the default parameter of the function input.kwargsis don’t-care. Placeholders correspond to the function parameters (e.g.x) in the graph printout.
计算网络参数的数目: nb_param = 0 for param in net.parameters(): nb_param += np.prod(list(param.data.size())) print('Number of parameters:', nb_param)发布于 2021-01-14 10:55 Torch (深度学习框架) 赞同303 条评论 分享喜欢收藏申请转载 ...
torch.manual_seed(SEED_NUMBER)# 替换为:xm.set_rng_state(SEED_NUMBER) 在获取xla_device后,调用set_replication、封装dataloader并设置model device placement。 device = xm.xla_device() xm.set_replication(device, [device])# Wrapper dataloaderdata_loader_train = pl.MpDeviceLoader(data_loader_train, ...
number_iterations= 1000print("running") [b, w]=gradient_descent_runner(points, initial_b, initial_w, learning_rate, number_iterations)print("b={0}, w ={1}, error={2}".format(b, w, compute_error_for_line_given_point(b, w, points)))if__name__=='__main__': ...
Returns the total number of elements in the input tensor. Parameters input (Tensor)– the input tensor. Example: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 复制 >>> a = torch.randn(1, 2, 3, 4, 5) >>> torch.numel(a) 120 >>> a = torch.zeros(4,4) >>> torch.numel(a...
opt = torch.optim.Adam(net.parameters(), lr=0.0001) total_step = len(data_loader_train) start = datetime.now() # 网络训练 for epoch in range(epochs): for i, data in enumerate(data_loader_train): images, labels = data images, labels = images.cuda(), labels.cuda() opt.zero_grad...
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
Parameters Inputs: input, h_0 Outputs: output, h_n Shape: Variables Note All the weights and biases are initialized from U(−k,k)\mathcal{U}(-\sqrt{k}, \sqrt{k})U(−k ,k ) where k=1hidden_sizek = \frac{1}{\text{hidden\_size}}k=hidden_size1 ...
('bias', None) self.reset_parameters() def reset_parameters(self): init.kaiming_uniform_(self.weight, a=math.sqrt(5)) if self.bias is not None: fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight) bound = 1 / math.sqrt(fan_in) init.uniform_(self.bias, -bound, bound...