How do I retrieve the weights estimated from a neural net using skorch?
I've trained a simple neural net using skorch to make it sklearn compatible and I would like to know how to retrieve the actual estimated weights.
Here's a replicable example of what I need.
The neural net presented here uses 10 features, has one hidden layer of 2 nodes, uses ReLu activation functions and linearly combines the output of the 2 nodes.
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(&quot;float32&quot;)

l2_params_1 = np.random.rand(1,n_features).astype(&quot;float32&quot;)
l2_params_2 = np.random.rand(1,n_features).astype(&quot;float32&quot;)

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()

The parameters at which I'm arriving are the following:
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)]

Now, to wrap my NNModule with skorch, I'm using this:
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))

And I'd like to retrieve the weights obtained in the training. I've used dir(net) to see if the weights are stored in any attributes to no avail.

To retrieve the weights one needs to output them like this:
list(net.module.parameters())

