我已经训练了一个简单的神经网络skorch来使其sklearn兼容，我想知道如何检索实际估计的权重。
这是我需要的可复制示例。
这里介绍的神经网络使用 10 个特征，具有 2 个节点的隐藏层，使用 ReLu 激活函数并线性组合 2 个节点的输出。
import torch
import numpy as np
from torch.autograd import Variable
# Create example data
np.random.seed(2022)
train_size = 1000
n_features= 10
X_train = np.random.rand(n_features, train_size).astype("float32")
l2_params_1 = np.random.rand(1,n_features).astype("float32")
l2_params_2 = np.random.rand(1,n_features).astype("float32")
l1_X = np.matmul(l2_params_1, X_train)
l2_X = np.matmul(l2_params_2, X_train)
y_train = l1_X + l2_X
# Defining my NN
class NNModule(torch.nn.Module):
def __init__(self, in_features):
super(NNModule, self).__init__()
self.l1 = torch.nn.Linear(in_features, 2)
self.a1 = torch.nn.ReLU()
self.l2 = torch.nn.Linear(2, 1)
def forward(self, x):
x = self.l1(x)
x = self.a1(x)
return self.l2(x)
# Initialize the NN
torch.manual_seed(200)
model = NNModule(in_features = 10)
model.l1.weight.data.uniform_(0.0, 1.0)
model.l1.bias.data.uniform_(0.0, 1.0)
# Define criterion and optimizer
criterion = torch.nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
# Train the NN
torch.manual_seed(200)
for epoch in range(100):
inputs = Variable(torch.from_numpy(np.transpose(X_train)))
labels = Variable(torch.from_numpy(np.transpose(y_train)))
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
我到达的参数如下：
list(model.parameters())
[Output]:
[Parameter containing:
tensor([[0.8997, 0.8345, 0.8284, 0.6950, 0.5949, 0.1217, 0.9067, 0.1824, 0.8272,
0.2372],
[0.7525, 0.6577, 0.4358, 0.6109, 0.8817, 0.5429, 0.5263, 0.7531, 0.1552,
0.7066]], requires_grad=True),
Parameter containing:
tensor([0.6617, 0.1079], requires_grad=True),
Parameter containing:
tensor([[0.9225, 0.8339]], requires_grad=True),
Parameter containing:
tensor([0.0786], requires_grad=True)]
现在，用 包装我NNModule，skorch我正在使用这个：
from skorch import NeuralNetRegressor
torch.manual_seed(200)
net = NeuralNetRegressor(
module=NNModule(in_features=10),
criterion=torch.nn.MSELoss,
optimizer=torch.optim.SGD,
optimizer__lr=0.01,
max_epochs=100,
verbose=0
)
net.fit(np.transpose(X_train), np.transpose(y_train))
我想检索在训练中获得的权重。我曾经dir(net)查看权重是否存储在任何属性中无济于事。