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from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import torch
from torch import nn

iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target)

X_train = torch.from_numpy(X_train).float()
y_train = torch.tensor([[float(x)] for x in y_train])
X_test = torch.from_numpy(X_test).float()
y_test = torch.tensor([[float(x)] for x in y_test])

INPUT_FEATURES = 4
HIDDEN = 5
OUTPUT_FEATURES = 1

class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(INPUT_FEATURES, HIDDEN)
        self.fc2 = nn.Linear(HIDDEN, OUTPUT_FEATURES)

    def forward(self, x):
        x = self.fc1(x)
        x = torch.sigmoid(x)
        x = self.fc2(x)
        return x

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net = Net()

print('weight')
print(net.fc1.weight)
print('bias')
print(net.fc1.bias)

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