PyTorch：预测单个示例

Question

遵循以下示例：

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

该代码可以成功训练：

# 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

我如何预测一个例子？到目前为止，我的经验是使用just来使用前馈网络numpy。训练模型后，我利用前向传播，但仅举一个例子：

numpy代码片段在哪里new，我正在尝试预测的输出值：

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 包含预测的向量属性

惯用的PyTorch方法是否相同？使用前向传播以进行单个预测？

Answer 1

您发布的代码是一个简单的演示，试图揭示这种深度学习框架的内部机制。这些框架，包括PyTorch，Keras，Tensorflow等，只要定义了网络结构，就可以自动处理正向计算，跟踪和应用渐变。但是，您显示的代码仍然尝试手动执行这些操作。这就是为什么在预测一个示例时会感到笨拙的原因，因为您仍在从头开始。

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

从您提供的页面中获取示例：

# 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()

该代码定义了一个名为TwoLayerNet模型，它初始化在两个线性层__init__功能，并进一步定义如何这两个线性电源与输入交互x的forward功能。定义好模型后，我们只需调用模型实例即可执行单个前馈操作，如代码片段结尾所示：

y_pred = model(x)