binary_cross_entropy_with_logits(input, gpu_wrapper(Variable(torch.zeros(input.shape[0]))) elif self.gan_type == 'WGAN_hinge': if target_is_real: return F.relu(1.0 - input).mean() else: return F.relu(input + 1.0).mean() else: raise ValueError() Example...
binary_cross_entropy¶ torch.nn.functional.binary_cross_entropy(input, target, weight=None, size_average=None, reduce=None, reduction='mean')[source]¶ Function that measures the Binary Cross Entropy between the target and the output. See BCELoss for details. Parameters input –Tensor of arb...
print(f"Gradient function for z = {z.grad_fn}")print(f"Gradient function for loss = {loss.grad_fn}")---Gradientfunctionforz=<AddBackward0objectat0x7e7ffe3f6140>Gradientfunctionforloss=<BinaryCrossEntropyWithLogitsBackward0objectat0x7e7ffe3f6710> 计算梯度Gradients 为了优化神经网络中参数的权重,...
print('Gradient function for z =', z.grad_fn) print('Gradient function for loss =', loss.grad_fn) Out: Gradient function for z = <AddBackward0 object at 0x7fafdf903048> Gradient function for loss = <BinaryCrossEntropyWithLogitsBackward object at 0x7fafdf903048> 计算梯度 为了优化神经网络...
loss = torch.nn.functional.binary_cross_entropy_with_logits(z, y) Tensors、Functions and Computational graph 上述代码定义了下面的computational graph: 在该网络中,w和b是parameters,是我们需要优化的。因此,我们需要能够计算损失函数关于这些变量的梯度。因此,我们设置了这些tensor的requires_grad属性。
loss = torch.nn.functional.binary_cross_entropy_with_logits(z, y) 张量、函数与计算图 以上代码定义了以下计算图: 在这个网络中,w和b是需要优化的参数。因此,我们需要能够计算损失函数相对于这些变量的梯度。为了做到这一点,我们设置了这些张量的requires_grad属性为True。
Gradient function for loss = <BinaryCrossEntropyWithLogitsBackward0 object at 0x7efc61a05240> 1. 2. 计算梯度 为了优化神经网络中参数的权重,我们需要计算损失函数对参数的导数,即我们需要对x和y求偏导: ∂ l o s s ∂ w \frac{\partial loss}{\partial w...
print(f"Gradient function for z ={z.grad_fn}")print(f"Gradient function for loss ={loss.grad_fn}") 1. 2. 输出如下: Gradient function for z = <AddBackward0 object at 0x7ff369b0d310> Gradient function for loss = <BinaryCrossEntropyWithLogitsBackward0 object at 0x7ff3772319d0> ...
binary_cross_entropy_with_logits(x_recon, x, size_average=False).div(batch_size) elif distribution == 'gaussian': x_recon = F.sigmoid(x_recon) recon_loss = F.mse_loss(x_recon, x, size_average=False).div(batch_size) else: recon_loss = None return recon_loss ...
Shape: BCELoss class torch.nn.BCELoss(weight=None, size_average=None, reduce=None, reduction='mean')[source] Creates a criterion that measures the Binary Cross Entropy between the target and the output: The unreduced (i.e. with reduction set to 'none') loss can be described as: ℓ(...