PyTorch：预测单个示例

Question

以下示例来自：

https://github.com/jcjohnson/pytorch-examples

此代码训练成功：

# Code in file tensor/two_layer_net_tensor.py
import torch

device = torch.device('cpu')
# device = torch.device('cuda') # Uncomment this to run on GPU

# N is batch size; D_in is input dimension;
# H is hidden dimension; D_out is output dimension.
N, D_in, H, D_out = 64, 1000, 100, 10

# Create random input and output data
x = torch.randn(N, D_in, device=device)
y = torch.randn(N, D_out, device=device)

# Randomly initialize weights
w1 = torch.randn(D_in, H, device=device)
w2 = torch.randn(H, D_out, device=device)

learning_rate = 1e-6
for t in range(500):
  # Forward pass: compute predicted y
  h = x.mm(w1)
  h_relu = h.clamp(min=0)
  y_pred = h_relu.mm(w2)

  # Compute and print loss; loss is a scalar, and is stored in a PyTorch Tensor
  # of shape (); we can get its value as a Python number with loss.item().
  loss = (y_pred - y).pow(2).sum()
  print(t, loss.item())

  # Backprop to compute gradients of w1 and w2 with respect to loss
  grad_y_pred = 2.0 * (y_pred - y)
  grad_w2 = h_relu.t().mm(grad_y_pred)
  grad_h_relu = grad_y_pred.mm(w2.t())
  grad_h = grad_h_relu.clone()
  grad_h[h < 0] = 0
  grad_w1 = x.t().mm(grad_h)

  # Update weights using gradient descent
  w1 -= learning_rate * grad_w1
  w2 -= learning_rate * grad_w2

如何预测单个示例？到目前为止，我的经验是仅使用 numpy 来利用前馈网络。训练模型后，我使用前向传播，但举一个例子：

numpy 代码片段，其中 new 是我试图预测的输出值：

new = np.asarray(toclassify) 
Z1 = np.dot(weight_layer_1, new.T) + bias_1 
sigmoid_activation_1 = sigmoid(Z1) 
Z2 = np.dot(weight_layer_2, sigmoid_activation_1) + bias_2 
sigmoid_activation_2 = sigmoid(Z2)

sigmoid_activation_2包含预测向量属性

惯用的 PyTorch 方式是否相同？使用前向传播来进行单个预测？

Answer 1

您发布的代码是一个简单的演示，试图揭示此类深度学习框架的内部机制。这些框架，包括 PyTorch、Keras、Tensorflow 等，只要您定义了网络结构，就会自动为您处理前向计算、跟踪和应用梯度。但是，您显示的代码仍然尝试手动执行这些操作。这就是为什么你在预测一个例子时感到很麻烦的原因，因为你还在从头做起。

在实践中，我们将定义一个继承自torch.nn.Module的模型class，并初始化__init__中的所有网络组件（如神经层、GRU、LSTM层等）函数，并定义这些组件如何与 forward 函数中的网络输入交互。

以您提供的页面为例：

# Code in file nn/two_layer_net_module.py
import torch

class TwoLayerNet(torch.nn.Module):
    def __init__(self, D_in, H, D_out):
        """
        In the constructor we instantiate two nn.Linear modules and 
        assign them as
        member variables.
        """
        super(TwoLayerNet, self).__init__()
        self.linear1 = torch.nn.Linear(D_in, H)
        self.linear2 = torch.nn.Linear(H, D_out)

    def forward(self, x):
        """
        In the forward function we accept a Tensor of input data and we must return
        a Tensor of output data. We can use Modules defined in the constructor as
        well as arbitrary (differentiable) operations on Tensors.
        """
        h_relu = self.linear1(x).clamp(min=0)
        y_pred = self.linear2(h_relu)
        return y_pred

# N is batch size; D_in is input dimension;
# H is hidden dimension; D_out is output dimension.
N, D_in, H, D_out = 64, 1000, 100, 10

# Create random Tensors to hold inputs and outputs
x = torch.randn(N, D_in)
y = torch.randn(N, D_out)

# Construct our model by instantiating the class defined above.
model = TwoLayerNet(D_in, H, D_out)

# Construct our loss function and an Optimizer. The call to 
model.parameters()
# in the SGD constructor will contain the learnable parameters of the two
# nn.Linear modules which are members of the model.
loss_fn = torch.nn.MSELoss(size_average=False)
optimizer = torch.optim.SGD(model.parameters(), lr=1e-4)
for t in range(500):
    # Forward pass: Compute predicted y by passing x to the model
    y_pred = model(x)

    # Compute and print loss
    loss = loss_fn(y_pred, y)
    print(t, loss.item())

    # Zero gradients, perform a backward pass, and update the weights.
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

代码定义了一个名为TwoLayerNet的模型，它在__init__函数中初始化了两个线性层，并在forward函数中进一步定义了这两个线性层如何与输入x交互.

定义好模型后，我们可以按如下方式执行单个前馈操作。说 xu 包含一个看不见的例子：

xu = torch.randn(D_in)

然后执行预测：

y_pred = model(torch.atleast_2d(xu))

PyTorch：预测单个示例

PyTorch : predict single example

python

machine-learning

backpropagation

pytorch