d2l.plot(x.detach().numpy(), [y.detach().numpy(), x.grad.numpy()], legend=['sigmoid', 'gradient'], figsize=(4.5, 2.5)) x = np.arange(-8.0, 8.0, 0.1) x.attach_grad() with autograd.record(): y = npx.sigmoid(x) y.backward() d2l.plot(x, [y, x.grad], legend=['sigmoi...
attach_grad() # After we calculate a gradient taken with respect to `x`, we will be able to # access it via the `grad` attribute, whose values are initialized with 0s x.grad array([0., 0., 0., 0.]) x = jnp.arange(4.0) x No GPU/TPU found, falling back to CPU. (...
attach_grad() # 需要梯度 w2.attach_grad() # 需要梯度 for t in range(n_epoch): with autograd.record(): # 注意 MXNet 默认关闭梯度,在此打开 y_pred = nd.dot(nd.dot(x, w1).relu(), w2) loss = ((y_pred - y) ** 2.0).sum() / N loss.backward() w1 -= learning_rate * w1...
2) gradient_mask[0, 0] = 1.0 model[0].weight.register_hook(lambda grad: grad.mul_(gradient...
b1.attach_grad() # 同上 with autograd.record(): # 使用 autograd 来记录计算图 res = nd.dot(val, w1) res2 = res + b1 res2.backward() # 这里需要注意的是,如果 res2 不是标量的话,默认的操作是会对 res2 做一个 sum,然后再 backward ...
// Attach the appropriate autograd edges. auto grad_fn = std::make_shared<SendRpcBackward>(); // 构建了 SendRpcBackward grad_fn->set_next_edges( torch::autograd::collect_next_edges(tensors_with_grad)); // Add the appropriate input metadata for the grad_fn. ...
next_functions = t5.grad_fn.next_functions 具体对应如下图: 2.2 分布式示例 接下来看看分布式的例子,这个例子就是官方设计中图例大致对应的代码,我们把 torch.mul(t3, t4) 命名为 t5,加入了 loss。 defworker0():# On worker 0:# Setup the autograd context. Computations that take# part in the distr...
(tensors_with_grad),[](consttorch::Tensor&t){returnt.requires_grad();});// Attach the appropriate autograd edges.auto grad_fn=std::make_shared<SendRpcBackward>();// 构建了 SendRpcBackwardgrad_fn->set_next_edges(torch::autograd::collect_next_edges(tensors_with_grad));// Add the ...
grad_fn.next_functions 具体对应如下图: 2.2 分布式示例 接下来看看分布式的例子,这个例子就是官方设计中图例大致对应的代码,我们把 torch.mul(t3, t4) 命名为 t5,加入了 loss。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 def worker0(): # On worker 0: # Setup the autograd context. ...
b1 = torch.zeros(5, requires_grad=True) b2 = torch.zeros(2, requires_grad=True)forepochinrange(epochs): a1 = x @ w1 + b1 h1 = a2.sigmoid() a2 = h2 @ w2 + b1 hyp = a3.sigmoid() cost = (hyp - y).pow(2).sum()