x = torch.arange(-8.0, 8.0, 0.1, requires_grad=True) y = torch.sigmoid(x) y.backward(torch.ones_like(x)) 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_...
2) gradient_mask[0, 0] = 1.0 model[0].weight.register_hook(lambda grad: grad.mul_(gradient...
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. (...
w1.attach_grad() # 对模型参数进行 attach_grad() 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_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...
attach grad info and grad functions and send// rpcWithAutograd message.auto tensorsRequireGrad=torch::autograd::compute_requires_grad(wrappedRpcMsg.tensors());if(!autogradContainer.hasValidContext()||(!forceGradRecording&&!tensorsRequireGrad)){returnstd::move(wrappedRpcMsg);}// Retrieve the appr...
(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 ...
Map){// 获取到 DistAutogradContainerauto& autogradContainer = DistAutogradContainer::getInstance();// If there is no valid context and no tensor requires grads, send original// rpc message. otherwise, attach grad info and grad functions and send// rpcWithAutograd message.autotensorsRequireGrad ...
// 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. ...
for param in vgg19_model.parameters(): param.requires_grad_(False) 现在我们已经加载了 VGG 模型的相关部分,我们需要将maxpool层改为平均池化层,如前面讨论的那样。在此过程中,我们将注意到模型中卷积层的位置: conv_indices = []for i in range(len(vgg19_model)): if vgg19_model[i]._get_name...