grad_fn .is_leaf 每个Tensor都有一个.grad_fn属性,该属性即创建该Tensor的Function, 就是说该Tensor是不是通过某些运算得到的,若是,则grad_fn返回一个与这些运算相关的对象,否则是None。 直接创建的,所以它没有grad_fn, 而y是通过一个加法操作创建的,所以它有一个为<AddBackward>的grad_fn。 直接创建的称...
a = torch.randn(1, requires_grad=True, dtype=torch.float, device=device) b = torch.randn(1, requires_grad=True, dtype=torch.float, device=device) for epoch in range(n_epochs): yhat = a + b * x_train_tensor error = y_train_tensor - yhat loss = (error ** 2).mean() # No ...
grad) print(y1) print('-'*30) x2 = torch.tensor(20.0, requires_grad=True) y2 = (x2.cos()/0.1-x2.exp()) ** 2 y2.backward() print(x2,x2.grad) print(y2) 结果如下 tensor(10., requires_grad=True) tensor(9.6978e+08) tensor(4.8495e+08, grad_fn=<PowBackward0>) --- te...
labels in data_loader: optimizer.zero_grad() loss_fn(model(data), labels).backward() optimizer.step() # 这将更新共享参数model = nn.Sequential(nn.Linear(n_in, n_h1), nn.ReLU(), nn.Linear(n_h1, n_out))model.share_memory() #需要"fork"方法工作processes = []for i in range(4)...
# y是操作的结果,所以它有grad_fn属性 print(y.grad_fn) #对y进行更多操作 z = y * y * 3 out = z.mean() print(z, out) 上述操作的结果如下: tensor([[1., 1.], [1., 1.]], requires_grad=True) tensor([[3., 3.],
importtorchimporttorch.nnasnn# dtype=torch.float必不可少v = torch.tensor([0], dtype=torch.float) m = nn.Linear(1,10) m(v) 运行结果: tensor([-0.6189, -0.9843, -0.7568,0.9157,0.5192, -0.6109, -0.5627, -0.7755, -0.9522,0.7771], grad_fn=<AddBackward0>)...
with torch.no_grad(): prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)return{labels[i]: float(prediction[i])foriinrange(1000)} inputs = gr.Image() outputs = gr.Label(num_top_classes=3) gr.Interface(fn=predict, inputs=inputs, outputs=outputs).launch() ...
保存变量的类如下：classSaveFeatures():def__init__(self, module):self.hook= module.register_forward_hook(self.hook_fn) def hook_fn(self, module, input, output): self.features = torch.tensor(output,requires_grad=True).cuda() def close(self): self.hook.remove()当执行 hook 时...
fn=[filename for filename in os.listdir("data/02Python使用入门") if filename.endswith(('.exe','.py'))] print(fn) 代码语言:javascript 复制 ['ex2_53.py', 'ex2_19.py', 'ex2_37.py', 'ex2_36.py', 'ex2_38_1.py', 'ex2_45.py', 'ex2_16.py', 'ex2_34.py', 'ex2_41....
classSaveFeatures():def__init__(self, module):self.hook= module.register_forward_hook(self.hook_fn) def hook_fn(self, module, input, output): self.features = torch.tensor(output,requires_grad=True).cuda() def close(self): self.hook.remove() 当执行 hook 时,调用方法 hook_fn。hook_fn...